Coordination of heterogeneous nonlinear multi-agent systems with prescribed behaviours

NASA Astrophysics Data System (ADS)

Tang, Yutao

2017-10-01

In this paper, we consider a coordination problem for a class of heterogeneous nonlinear multi-agent systems with a prescribed input-output behaviour which was represented by another input-driven system. In contrast to most existing multi-agent coordination results with an autonomous (virtual) leader, this formulation takes possible control inputs of the leader into consideration. First, the coordination was achieved by utilising a group of distributed observers based on conventional assumptions of model matching problem. Then, a fully distributed adaptive extension was proposed without using the input of this input-output behaviour. An example was given to verify their effectiveness.

Experimental feedback linearisation of a vibrating system with a non-smooth nonlinearity

NASA Astrophysics Data System (ADS)

Lisitano, D.; Jiffri, S.; Bonisoli, E.; Mottershead, J. E.

2018-03-01

Input-output partial feedback linearisation is demonstrated experimentally for the first time on a system with non-smooth nonlinearity, a laboratory three degrees of freedom lumped mass system with a piecewise-linear spring. The output degree of freedom is located away from the nonlinearity so that the partial feedback linearisation possesses nonlinear internal dynamics. The dynamic behaviour of the linearised part is specified by eigenvalue assignment and an investigation of the zero dynamics is carried out to confirm stability of the overall system. A tuned numerical model is developed for use in the controller and to produce numerical outputs for comparison with experimental closed-loop results. A new limitation of the feedback linearisation method is discovered in the case of lumped mass systems - that the input and output must share the same degrees of freedom.

From Spiking Neuron Models to Linear-Nonlinear Models

PubMed Central

Ostojic, Srdjan; Brunel, Nicolas

2011-01-01

Neurons transform time-varying inputs into action potentials emitted stochastically at a time dependent rate. The mapping from current input to output firing rate is often represented with the help of phenomenological models such as the linear-nonlinear (LN) cascade, in which the output firing rate is estimated by applying to the input successively a linear temporal filter and a static non-linear transformation. These simplified models leave out the biophysical details of action potential generation. It is not a priori clear to which extent the input-output mapping of biophysically more realistic, spiking neuron models can be reduced to a simple linear-nonlinear cascade. Here we investigate this question for the leaky integrate-and-fire (LIF), exponential integrate-and-fire (EIF) and conductance-based Wang-Buzsáki models in presence of background synaptic activity. We exploit available analytic results for these models to determine the corresponding linear filter and static non-linearity in a parameter-free form. We show that the obtained functions are identical to the linear filter and static non-linearity determined using standard reverse correlation analysis. We then quantitatively compare the output of the corresponding linear-nonlinear cascade with numerical simulations of spiking neurons, systematically varying the parameters of input signal and background noise. We find that the LN cascade provides accurate estimates of the firing rates of spiking neurons in most of parameter space. For the EIF and Wang-Buzsáki models, we show that the LN cascade can be reduced to a firing rate model, the timescale of which we determine analytically. Finally we introduce an adaptive timescale rate model in which the timescale of the linear filter depends on the instantaneous firing rate. This model leads to highly accurate estimates of instantaneous firing rates. PMID:21283777

From spiking neuron models to linear-nonlinear models.

PubMed

Ostojic, Srdjan; Brunel, Nicolas

2011-01-20

Neurons transform time-varying inputs into action potentials emitted stochastically at a time dependent rate. The mapping from current input to output firing rate is often represented with the help of phenomenological models such as the linear-nonlinear (LN) cascade, in which the output firing rate is estimated by applying to the input successively a linear temporal filter and a static non-linear transformation. These simplified models leave out the biophysical details of action potential generation. It is not a priori clear to which extent the input-output mapping of biophysically more realistic, spiking neuron models can be reduced to a simple linear-nonlinear cascade. Here we investigate this question for the leaky integrate-and-fire (LIF), exponential integrate-and-fire (EIF) and conductance-based Wang-Buzsáki models in presence of background synaptic activity. We exploit available analytic results for these models to determine the corresponding linear filter and static non-linearity in a parameter-free form. We show that the obtained functions are identical to the linear filter and static non-linearity determined using standard reverse correlation analysis. We then quantitatively compare the output of the corresponding linear-nonlinear cascade with numerical simulations of spiking neurons, systematically varying the parameters of input signal and background noise. We find that the LN cascade provides accurate estimates of the firing rates of spiking neurons in most of parameter space. For the EIF and Wang-Buzsáki models, we show that the LN cascade can be reduced to a firing rate model, the timescale of which we determine analytically. Finally we introduce an adaptive timescale rate model in which the timescale of the linear filter depends on the instantaneous firing rate. This model leads to highly accurate estimates of instantaneous firing rates.

Modal control of an oblique wing aircraft

NASA Technical Reports Server (NTRS)

Phillips, James D.

1989-01-01

A linear modal control algorithm is applied to the NASA Oblique Wing Research Aircraft (OWRA). The control law is evaluated using a detailed nonlinear flight simulation. It is shown that the modal control law attenuates the coupling and nonlinear aerodynamics of the oblique wing and remains stable during control saturation caused by large command inputs or large external disturbances. The technique controls each natural mode independently allowing single-input/single-output techniques to be applied to multiple-input/multiple-output systems.

Identification of an internal combustion engine model by nonlinear multi-input multi-output system identification. Ph.D. Thesis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Luh, G.C.

1994-01-01

This thesis presents the application of advanced modeling techniques to construct nonlinear forward and inverse models of internal combustion engines for the detection and isolation of incipient faults. The NARMAX (Nonlinear Auto-Regressive Moving Average modeling with eXogenous inputs) technique of system identification proposed by Leontaritis and Billings was used to derive the nonlinear model of a internal combustion engine, over operating conditions corresponding to the I/M240 cycle. The I/M240 cycle is a standard proposed by the United States Environmental Protection Agency to measure tailpipe emissions in inspection and maintenance programs and consists of a driving schedule developed for the purposemore » of testing compliance with federal vehicle emission standards for carbon monoxide, unburned hydrocarbons, and nitrogen oxides. The experimental work for model identification and validation was performed on a 3.0 liter V6 engine installed in an engine test cell at the Center for Automotive Research at The Ohio State University. In this thesis, different types of model structures were proposed to obtain multi-input multi-output (MIMO) nonlinear NARX models. A modification of the algorithm proposed by He and Asada was used to estimate the robust orders of the derived MIMO nonlinear models. A methodology for the analysis of inverse NARX model was developed. Two methods were proposed to derive the inverse NARX model: (1) inversion from the forward NARX model; and (2) direct identification of inverse model from the output-input data set. In this thesis, invertibility, minimum-phase characteristic of zero dynamics, and stability analysis of NARX forward model are also discussed. Stability in the sense of Lyapunov is also investigated to check the stability of the identified forward and inverse models. This application of inverse problem leads to the estimation of unknown inputs and to actuator fault diagnosis.« less

Raman-Suppressing Coupling for Optical Parametric Oscillator

NASA Technical Reports Server (NTRS)

Savchenkov, Anatoliy; Maleki, Lute; Matsko, Andrey; Rubiola, Enrico

2007-01-01

A Raman-scattering-suppressing input/ output coupling scheme has been devised for a whispering-gallery-mode optical resonator that is used as a four-wave-mixing device to effect an all-optical parametric oscillator. Raman scattering is undesired in such a device because (1) it is a nonlinear process that competes with the desired nonlinear four-wave conversion process involved in optical parametric oscillation and (2) as such, it reduces the power of the desired oscillation and contributes to output noise. The essence of the present input/output coupling scheme is to reduce output loading of the desired resonator modes while increasing output loading of the undesired ones.

Transformation of nonlinear discrete-time system into the extended observer form

NASA Astrophysics Data System (ADS)

Kaparin, V.; Kotta, Ü.

2018-04-01

The paper addresses the problem of transforming discrete-time single-input single-output nonlinear state equations into the extended observer form, which, besides the input and output, also depends on a finite number of their past values. Necessary and sufficient conditions for the existence of both the extended coordinate and output transformations, solving the problem, are formulated in terms of differential one-forms, associated with the input-output equation, corresponding to the state equations. An algorithm for transformation of state equations into the extended observer form is proposed and illustrated by an example. Moreover, the considered approach is compared with the method of dynamic observer error linearisation, which likewise is intended to enlarge the class of systems transformable into an observer form.

Nonlinear distortion analysis for single heterojunction GaAs HEMT with frequency and temperature

NASA Astrophysics Data System (ADS)

Alim, Mohammad A.; Ali, Mayahsa M.; Rezazadeh, Ali A.

2018-07-01

Nonlinearity analysis using two-tone intermodulation distortion (IMD) technique for 0.5 μm gate-length AlGaAs/GaAs based high electron mobility transistor have been investigated based on biasing conditions, input power, frequency and temperature. The outcomes indicate a significant modification on the output IMD power and as well as the minimum distortion level. The input IMD power effects the output current and subsequently the threshold voltage reduces, resulting to an increment in the output IMD power. Both frequency and temperature reduces the magnitude of the output IMDs. In addition, the threshold voltage response with temperature alters the notch point of the nonlinear output IMD’s accordingly. The aforementioned investigation will help the circuit designers to evaluate the best biasing option in terms of minimum distortion, maximum gain for future design optimizations.

How to characterize the nonlinear amplifier?

NASA Technical Reports Server (NTRS)

Kallistratova, Dmitri Kouznetsov; Cotera, Carlos Flores

1994-01-01

The conception of the amplification of the coherent field is formulated. The definition of the coefficient of the amplification as the relation between the mean value of the field at the output to the value at the input and the definition of the noise as the difference between the number of photons in the output mode and square of the modulus of the mean value of the output amplitude are considered. Using a simple example it is shown that by these definitions the noise of the nonlinear amplifier may be less than the noise of the ideal linear amplifier of the same amplification coefficient. Proposals to search another definition of basic parameters of the nonlinear amplifiers are discussed. This definition should enable us to formulate the universal fundamental lower limit of the noise which should be valid for linear quantum amplifiers as for nonlinear ones.

Tunable pulsed narrow bandwidth light source

DOEpatents

Powers, Peter E.; Kulp, Thomas J.

2002-01-01

A tunable pulsed narrow bandwidth light source and a method of operating a light source are provided. The light source includes a pump laser, first and second non-linear optical crystals, a tunable filter, and light pulse directing optics. The method includes the steps of operating the pump laser to generate a pulsed pump beam characterized by a nanosecond pulse duration and arranging the light pulse directing optics so as to (i) split the pulsed pump beam into primary and secondary pump beams; (ii) direct the primary pump beam through an input face of the first non-linear optical crystal such that a primary output beam exits from an output face of the first non-linear optical crystal; (iii) direct the primary output beam through the tunable filter to generate a sculpted seed beam; and direct the sculpted seed beam and the secondary pump beam through an input face of the second non-linear optical crystal such that a secondary output beam characterized by at least one spectral bandwidth on the order of about 0.1 cm.sup.-1 and below exits from an output face of the second non-linear optical crystal.

Adaptive Control for Uncertain Nonlinear Multi-Input Multi-Output Systems

NASA Technical Reports Server (NTRS)

Cao, Chengyu (Inventor); Hovakimyan, Naira (Inventor); Xargay, Enric (Inventor)

2014-01-01

Systems and methods of adaptive control for uncertain nonlinear multi-input multi-output systems in the presence of significant unmatched uncertainty with assured performance are provided. The need for gain-scheduling is eliminated through the use of bandwidth-limited (low-pass) filtering in the control channel, which appropriately attenuates the high frequencies typically appearing in fast adaptation situations and preserves the robustness margins in the presence of fast adaptation.

General mechanism for the 1 /f noise

NASA Astrophysics Data System (ADS)

Yadav, Avinash Chand; Ramaswamy, Ramakrishna; Dhar, Deepak

2017-08-01

We consider the response of a memoryless nonlinear device that acts instantaneously, converting an input signal ξ (t ) into an output η (t ) at the same time t . For input Gaussian noise with power-spectrum 1 /fα , the nonlinearity can modify the spectral index of the output to give a spectrum that varies as 1 /fα ' with α'≠α . We show that the value of α' depends on the nonlinear transformation and can be tuned continuously. This provides a general mechanism for the ubiquitous 1 /f noise found in nature.

Fuzzy Counter Propagation Neural Network Control for a Class of Nonlinear Dynamical Systems

PubMed Central

Sakhre, Vandana; Jain, Sanjeev; Sapkal, Vilas S.; Agarwal, Dev P.

2015-01-01

Fuzzy Counter Propagation Neural Network (FCPN) controller design is developed, for a class of nonlinear dynamical systems. In this process, the weight connecting between the instar and outstar, that is, input-hidden and hidden-output layer, respectively, is adjusted by using Fuzzy Competitive Learning (FCL). FCL paradigm adopts the principle of learning, which is used to calculate Best Matched Node (BMN) which is proposed. This strategy offers a robust control of nonlinear dynamical systems. FCPN is compared with the existing network like Dynamic Network (DN) and Back Propagation Network (BPN) on the basis of Mean Absolute Error (MAE), Mean Square Error (MSE), Best Fit Rate (BFR), and so forth. It envisages that the proposed FCPN gives better results than DN and BPN. The effectiveness of the proposed FCPN algorithms is demonstrated through simulations of four nonlinear dynamical systems and multiple input and single output (MISO) and a single input and single output (SISO) gas furnace Box-Jenkins time series data. PMID:26366169

Fuzzy Counter Propagation Neural Network Control for a Class of Nonlinear Dynamical Systems.

PubMed

Sakhre, Vandana; Jain, Sanjeev; Sapkal, Vilas S; Agarwal, Dev P

2015-01-01

Fuzzy Counter Propagation Neural Network (FCPN) controller design is developed, for a class of nonlinear dynamical systems. In this process, the weight connecting between the instar and outstar, that is, input-hidden and hidden-output layer, respectively, is adjusted by using Fuzzy Competitive Learning (FCL). FCL paradigm adopts the principle of learning, which is used to calculate Best Matched Node (BMN) which is proposed. This strategy offers a robust control of nonlinear dynamical systems. FCPN is compared with the existing network like Dynamic Network (DN) and Back Propagation Network (BPN) on the basis of Mean Absolute Error (MAE), Mean Square Error (MSE), Best Fit Rate (BFR), and so forth. It envisages that the proposed FCPN gives better results than DN and BPN. The effectiveness of the proposed FCPN algorithms is demonstrated through simulations of four nonlinear dynamical systems and multiple input and single output (MISO) and a single input and single output (SISO) gas furnace Box-Jenkins time series data.

An artificial neural network model for periodic trajectory generation

NASA Astrophysics Data System (ADS)

Shankar, S.; Gander, R. E.; Wood, H. C.

A neural network model based on biological systems was developed for potential robotic application. The model consists of three interconnected layers of artificial neurons or units: an input layer subdivided into state and plan units, an output layer, and a hidden layer between the two outer layers which serves to implement nonlinear mappings between the input and output activation vectors. Weighted connections are created between the three layers, and learning is effected by modifying these weights. Feedback connections between the output and the input state serve to make the network operate as a finite state machine. The activation vector of the plan units of the input layer emulates the supraspinal commands in biological central pattern generators in that different plan activation vectors correspond to different sequences or trajectories being recalled, even with different frequencies. Three trajectories were chosen for implementation, and learning was accomplished in 10,000 trials. The fault tolerant behavior, adaptiveness, and phase maintenance of the implemented network are discussed.

Improving dynamic performances of PWM-driven servo-pneumatic systems via a novel pneumatic circuit.

PubMed

Taghizadeh, Mostafa; Ghaffari, Ali; Najafi, Farid

2009-10-01

In this paper, the effect of pneumatic circuit design on the input-output behavior of PWM-driven servo-pneumatic systems is investigated and their control performances are improved using linear controllers instead of complex and costly nonlinear ones. Generally, servo-pneumatic systems are well known for their nonlinear behavior. However, PWM-driven servo-pneumatic systems have the advantage of flexibility in the design of pneumatic circuits which affects the input-output linearity of the whole system. A simple pneumatic circuit with only one fast switching valve is designed which leads to a quasi-linear input-output relation. The quasi-linear behavior of the proposed circuit is verified both experimentally and by simulations. Closed loop position control experiments are then carried out using linear P- and PD-controllers. Since the output position is noisy and cannot be directly differentiated, a Kalman filter is designed to estimate the velocity of the cylinder. Highly improved tracking performances are obtained using these linear controllers, compared to previous works with nonlinear controllers.

Functional expansion representations of artificial neural networks

NASA Technical Reports Server (NTRS)

Gray, W. Steven

1992-01-01

In the past few years, significant interest has developed in using artificial neural networks to model and control nonlinear dynamical systems. While there exists many proposed schemes for accomplishing this and a wealth of supporting empirical results, most approaches to date tend to be ad hoc in nature and rely mainly on heuristic justifications. The purpose of this project was to further develop some analytical tools for representing nonlinear discrete-time input-output systems, which when applied to neural networks would give insight on architecture selection, pruning strategies, and learning algorithms. A long term goal is to determine in what sense, if any, a neural network can be used as a universal approximator for nonliner input-output maps with memory (i.e., realized by a dynamical system). This property is well known for the case of static or memoryless input-output maps. The general architecture under consideration in this project was a single-input, single-output recurrent feedforward network.

Structural identifiability analyses of candidate models for in vitro Pitavastatin hepatic uptake.

PubMed

Grandjean, Thomas R B; Chappell, Michael J; Yates, James W T; Evans, Neil D

2014-05-01

In this paper a review of the application of four different techniques (a version of the similarity transformation approach for autonomous uncontrolled systems, a non-differential input/output observable normal form approach, the characteristic set differential algebra and a recent algebraic input/output relationship approach) to determine the structural identifiability of certain in vitro nonlinear pharmacokinetic models is provided. The Organic Anion Transporting Polypeptide (OATP) substrate, Pitavastatin, is used as a probe on freshly isolated animal and human hepatocytes. Candidate pharmacokinetic non-linear compartmental models have been derived to characterise the uptake process of Pitavastatin. As a prerequisite to parameter estimation, structural identifiability analyses are performed to establish that all unknown parameters can be identified from the experimental observations available. Copyright © 2013. Published by Elsevier Ireland Ltd.

Statistical linearization for multi-input/multi-output nonlinearities

NASA Technical Reports Server (NTRS)

Lin, Ching-An; Cheng, Victor H. L.

1991-01-01

Formulas are derived for the computation of the random input-describing functions for MIMO nonlinearities; these straightforward and rigorous derivations are based on the optimal mean square linear approximation. The computations involve evaluations of multiple integrals. It is shown that, for certain classes of nonlinearities, multiple-integral evaluations are obviated and the computations are significantly simplified.

Weakly nonlinear behavior of a plate thickness-mode piezoelectric transformer.

PubMed

Yang, Jiashi; Chen, Ziguang; Hu, Yuantai; Jiang, Shunong; Guo, Shaohua

2007-04-01

We analyzed the weakly nonlinear behavior of a plate thickness-shear mode piezoelectric transformer near resonance. An approximate analytical solution was obtained. Numerical results based on the analytical solution are presented. It is shown that on one side of the resonant frequency the input-output relation becomes nonlinear, and on the other side the output voltage experiences jumps.

Further results on open-loop compensation of rate-dependent hysteresis in a magnetostrictive actuator with the Prandtl-Ishlinskii model

NASA Astrophysics Data System (ADS)

Al Janaideh, Mohammad; Aljanaideh, Omar

2018-05-01

Apart from the output-input hysteresis loops, the magnetostrictive actuators also exhibit asymmetry and saturation, particularly under moderate to large magnitude inputs and at relatively higher frequencies. Such nonlinear input-output characteristics could be effectively characterized by a rate-dependent Prandtl-Ishlinskii model in conjunction with a function of deadband operators. In this study, an inverse model is formulated to seek real-time compensation of rate-dependent and asymmetric hysteresis nonlinearities of a Terfenol-D magnetostrictive actuator. The inverse model is formulated with the inverse of the rate-dependent Prandtl-Ishlinskii model, satisfying the threshold dilation condition, with the inverse of the deadband function. The inverse model was subsequently applied to the hysteresis model as a feedforward compensator. The proposed compensator is applied as a feedforward compensator to the actuator hardware to study its potential for rate-dependent and asymmetric hysteresis loops. The experimental results are obtained under harmonic and complex harmonic inputs further revealed that the inverse compensator can substantially suppress the hysteresis and output asymmetry nonlinearities in the entire frequency range considered in the study.

Non-intrusive parameter identification procedure user's guide

NASA Technical Reports Server (NTRS)

Hanson, G. D.; Jewell, W. F.

1983-01-01

Written in standard FORTRAN, NAS is capable of identifying linear as well as nonlinear relations between input and output parameters; the only restriction is that the input/output relation be linear with respect to the unknown coefficients of the estimation equations. The output of the identification algorithm can be specified to be in either the time domain (i.e., the estimation equation coefficients) or in the frequency domain (i.e., a frequency response of the estimation equation). The frame length ("window") over which the identification procedure is to take place can be specified to be any portion of the input time history, thereby allowing the freedom to start and stop the identification procedure within a time history. There also is an option which allows a sliding window, which gives a moving average over the time history. The NAS software also includes the ability to identify several assumed solutions simultaneously for the same or different input data.

Matrix completion by deep matrix factorization.

PubMed

Fan, Jicong; Cheng, Jieyu

2018-02-01

Conventional methods of matrix completion are linear methods that are not effective in handling data of nonlinear structures. Recently a few researchers attempted to incorporate nonlinear techniques into matrix completion but there still exists considerable limitations. In this paper, a novel method called deep matrix factorization (DMF) is proposed for nonlinear matrix completion. Different from conventional matrix completion methods that are based on linear latent variable models, DMF is on the basis of a nonlinear latent variable model. DMF is formulated as a deep-structure neural network, in which the inputs are the low-dimensional unknown latent variables and the outputs are the partially observed variables. In DMF, the inputs and the parameters of the multilayer neural network are simultaneously optimized to minimize the reconstruction errors for the observed entries. Then the missing entries can be readily recovered by propagating the latent variables to the output layer. DMF is compared with state-of-the-art methods of linear and nonlinear matrix completion in the tasks of toy matrix completion, image inpainting and collaborative filtering. The experimental results verify that DMF is able to provide higher matrix completion accuracy than existing methods do and DMF is applicable to large matrices. Copyright © 2017 Elsevier Ltd. All rights reserved.

On-Chip Power-Combining for High-Power Schottky Diode Based Frequency Multipliers

NASA Technical Reports Server (NTRS)

Siles Perez, Jose Vicente (Inventor); Chattopadhyay, Goutam (Inventor); Lee, Choonsup (Inventor); Schlecht, Erich T. (Inventor); Jung-Kubiak, Cecile D. (Inventor); Mehdi, Imran (Inventor)

2015-01-01

A novel MMIC on-chip power-combined frequency multiplier device and a method of fabricating the same, comprising two or more multiplying structures integrated on a single chip, wherein each of the integrated multiplying structures are electrically identical and each of the multiplying structures include one input antenna (E-probe) for receiving an input signal in the millimeter-wave, submillimeter-wave or terahertz frequency range inputted on the chip, a stripline based input matching network electrically connecting the input antennas to two or more Schottky diodes in a balanced configuration, two or more Schottky diodes that are used as nonlinear semiconductor devices to generate harmonics out of the input signal and produce the multiplied output signal, stripline based output matching networks for transmitting the output signal from the Schottky diodes to an output antenna, and an output antenna (E-probe) for transmitting the output signal off the chip into the output waveguide transmission line.

Proposal of ultra-compact NAND/NOR/XNOR all-optical logic gates based on a nonlinear 3x1 multimode interference

NASA Astrophysics Data System (ADS)

Tajaldini, Mehdi; Mat Jafri, M. Z.

2014-05-01

We present a highly miniaturized multimode interference (MMI) coupler based on nonlinear modal propagation analysis (NMPA) method as a novel design method and potential application for optical NAND, NOR and XNOR logic gates for Boolean logic signal processing devices. Crystalline polydiacetylene is used to allow the appearances of nonlinear effects in low input intensities and ultra- short length to control the MMI coupler as an active device to access light switching due to its high nonlinear susceptibility. We consider a 10x33 μm2 MMI structure with three inputs and one output. Notably, the access facets are single-mode waveguides with sub-micron width. The center input contributes to control the induced light propagation in MMI by intensity variation whereas others could be launched by particular intensity when they are ON and 0 in OFF. Output intensity is analyzed in various sets of inputs to show the capability of Boolean logic gates, the contrast between ON and OFF is calculated on mentioned gates to present the efficiency. Good operation in low intensity and highly miniaturized MMI coupler is observed. Furthermore, nonlinear effects could be realized through the modal interferences. The issue of high insertion loss is addressed with a 3×3 upgraded coupler. Furthermore, the main significant aspect of this paper is simulating an MMI coupler that is launched by three nonlinear inputs, simultaneously, whereas last presents have never studied more than one input in nonlinear regimes.

A modified NARMAX model-based self-tuner with fault tolerance for unknown nonlinear stochastic hybrid systems with an input-output direct feed-through term.

PubMed

Tsai, Jason S-H; Hsu, Wen-Teng; Lin, Long-Guei; Guo, Shu-Mei; Tann, Joseph W

2014-01-01

A modified nonlinear autoregressive moving average with exogenous inputs (NARMAX) model-based state-space self-tuner with fault tolerance is proposed in this paper for the unknown nonlinear stochastic hybrid system with a direct transmission matrix from input to output. Through the off-line observer/Kalman filter identification method, one has a good initial guess of modified NARMAX model to reduce the on-line system identification process time. Then, based on the modified NARMAX-based system identification, a corresponding adaptive digital control scheme is presented for the unknown continuous-time nonlinear system, with an input-output direct transmission term, which also has measurement and system noises and inaccessible system states. Besides, an effective state space self-turner with fault tolerance scheme is presented for the unknown multivariable stochastic system. A quantitative criterion is suggested by comparing the innovation process error estimated by the Kalman filter estimation algorithm, so that a weighting matrix resetting technique by adjusting and resetting the covariance matrices of parameter estimate obtained by the Kalman filter estimation algorithm is utilized to achieve the parameter estimation for faulty system recovery. Consequently, the proposed method can effectively cope with partially abrupt and/or gradual system faults and input failures by the fault detection. Copyright © 2013 ISA. Published by Elsevier Ltd. All rights reserved.

L1 Adaptive Control Augmentation System with Application to the X-29 Lateral/Directional Dynamics: A Multi-Input Multi-Output Approach

NASA Technical Reports Server (NTRS)

Griffin, Brian Joseph; Burken, John J.; Xargay, Enric

2010-01-01

This paper presents an L(sub 1) adaptive control augmentation system design for multi-input multi-output nonlinear systems in the presence of unmatched uncertainties which may exhibit significant cross-coupling effects. A piecewise continuous adaptive law is adopted and extended for applicability to multi-input multi-output systems that explicitly compensates for dynamic cross-coupling. In addition, explicit use of high-fidelity actuator models are added to the L1 architecture to reduce uncertainties in the system. The L(sub 1) multi-input multi-output adaptive control architecture is applied to the X-29 lateral/directional dynamics and results are evaluated against a similar single-input single-output design approach.

Proper orthogonal decomposition-based spectral higher-order stochastic estimation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Baars, Woutijn J., E-mail: wbaars@unimelb.edu.au; Tinney, Charles E.

A unique routine, capable of identifying both linear and higher-order coherence in multiple-input/output systems, is presented. The technique combines two well-established methods: Proper Orthogonal Decomposition (POD) and Higher-Order Spectra Analysis. The latter of these is based on known methods for characterizing nonlinear systems by way of Volterra series. In that, both linear and higher-order kernels are formed to quantify the spectral (nonlinear) transfer of energy between the system's input and output. This reduces essentially to spectral Linear Stochastic Estimation when only first-order terms are considered, and is therefore presented in the context of stochastic estimation as spectral Higher-Order Stochastic Estimationmore » (HOSE). The trade-off to seeking higher-order transfer kernels is that the increased complexity restricts the analysis to single-input/output systems. Low-dimensional (POD-based) analysis techniques are inserted to alleviate this void as POD coefficients represent the dynamics of the spatial structures (modes) of a multi-degree-of-freedom system. The mathematical framework behind this POD-based HOSE method is first described. The method is then tested in the context of jet aeroacoustics by modeling acoustically efficient large-scale instabilities as combinations of wave packets. The growth, saturation, and decay of these spatially convecting wave packets are shown to couple both linearly and nonlinearly in the near-field to produce waveforms that propagate acoustically to the far-field for different frequency combinations.« less

A nonlinear autoregressive Volterra model of the Hodgkin-Huxley equations.

PubMed

Eikenberry, Steffen E; Marmarelis, Vasilis Z

2013-02-01

We propose a new variant of Volterra-type model with a nonlinear auto-regressive (NAR) component that is a suitable framework for describing the process of AP generation by the neuron membrane potential, and we apply it to input-output data generated by the Hodgkin-Huxley (H-H) equations. Volterra models use a functional series expansion to describe the input-output relation for most nonlinear dynamic systems, and are applicable to a wide range of physiologic systems. It is difficult, however, to apply the Volterra methodology to the H-H model because is characterized by distinct subthreshold and suprathreshold dynamics. When threshold is crossed, an autonomous action potential (AP) is generated, the output becomes temporarily decoupled from the input, and the standard Volterra model fails. Therefore, in our framework, whenever membrane potential exceeds some threshold, it is taken as a second input to a dual-input Volterra model. This model correctly predicts membrane voltage deflection both within the subthreshold region and during APs. Moreover, the model naturally generates a post-AP afterpotential and refractory period. It is known that the H-H model converges to a limit cycle in response to a constant current injection. This behavior is correctly predicted by the proposed model, while the standard Volterra model is incapable of generating such limit cycle behavior. The inclusion of cross-kernels, which describe the nonlinear interactions between the exogenous and autoregressive inputs, is found to be absolutely necessary. The proposed model is general, non-parametric, and data-derived.

Globally linearized control on diabatic continuous stirred tank reactor: a case study.

PubMed

Jana, Amiya Kumar; Samanta, Amar Nath; Ganguly, Saibal

2005-07-01

This paper focuses on the promise of globally linearized control (GLC) structure in the realm of strongly nonlinear reactor system control. The proposed nonlinear control strategy is comprised of: (i) an input-output linearizing state feedback law (transformer), (ii) a state observer, and (iii) an external linear controller. The synthesis of discrete-time GLC controller for single-input single-output diabatic continuous stirred tank reactor (DCSTR) has been studied first, followed by the synthesis of feedforward/feedback controller for the same reactor having dead time in process as well as in disturbance. Subsequently, the multivariable GLC structure has been designed and then applied on multi-input multi-output DCSTR system. The simulation study shows high quality performance of the derived nonlinear controllers. The better-performed GLC in conjunction with reduced-order observer has been compared with the conventional proportional integral controller on the example reactor and superior performance has been achieved by the proposed GLC control scheme.

Prescribed performance distributed consensus control for nonlinear multi-agent systems with unknown dead-zone input

NASA Astrophysics Data System (ADS)

Cui, Guozeng; Xu, Shengyuan; Ma, Qian; Li, Yongmin; Zhang, Zhengqiang

2018-05-01

In this paper, the problem of prescribed performance distributed output consensus for higher-order non-affine nonlinear multi-agent systems with unknown dead-zone input is investigated. Fuzzy logical systems are utilised to identify the unknown nonlinearities. By introducing prescribed performance, the transient and steady performance of synchronisation errors are guaranteed. Based on Lyapunov stability theory and the dynamic surface control technique, a new distributed consensus algorithm for non-affine nonlinear multi-agent systems is proposed, which ensures cooperatively uniformly ultimately boundedness of all signals in the closed-loop systems and enables the output of each follower to synchronise with the leader within predefined bounded error. Finally, simulation examples are provided to demonstrate the effectiveness of the proposed control scheme.

Adaptive nonlinear control for autonomous ground vehicles

NASA Astrophysics Data System (ADS)

Black, William S.

We present the background and motivation for ground vehicle autonomy, and focus on uses for space-exploration. Using a simple design example of an autonomous ground vehicle we derive the equations of motion. After providing the mathematical background for nonlinear systems and control we present two common methods for exactly linearizing nonlinear systems, feedback linearization and backstepping. We use these in combination with three adaptive control methods: model reference adaptive control, adaptive sliding mode control, and extremum-seeking model reference adaptive control. We show the performances of each combination through several simulation results. We then consider disturbances in the system, and design nonlinear disturbance observers for both single-input-single-output and multi-input-multi-output systems. Finally, we show the performance of these observers with simulation results.

A comparative study of linear and nonlinear MIMO feedback configurations

NASA Technical Reports Server (NTRS)

Desoer, C. A.; Lin, C. A.

1984-01-01

In this paper, a comparison is conducted of several feedback configurations which have appeared in the literature (e.g. unity-feedback, model-reference, etc.). The linear time-invariant multi-input multi-output case is considered. For each configuration, the stability conditions are specified, the relation between achievable I/O maps and the achievable disturbance-to-output maps is examined, and the effect of various subsystem perturbations on the system performance is studied. In terms of these considerations, it is demonstrated that one of the configurations considered is better than all the others. The results are then extended to the nonlinear multi-input multi-output case.

Orientation selectivity and the functional clustering of synaptic inputs in primary visual cortex

PubMed Central

Wilson, Daniel E.; Whitney, David E.; Scholl, Benjamin; Fitzpatrick, David

2016-01-01

The majority of neurons in primary visual cortex are tuned for stimulus orientation, but the factors that account for the range of orientation selectivities exhibited by cortical neurons remain unclear. To address this issue, we used in vivo 2-photon calcium imaging to characterize the orientation tuning and spatial arrangement of synaptic inputs to the dendritic spines of individual pyramidal neurons in layer 2/3 of ferret visual cortex. The summed synaptic input to individual neurons reliably predicted the neuron’s orientation preference, but did not account for differences in orientation selectivity among neurons. These differences reflected a robust input-output nonlinearity that could not be explained by spike threshold alone, and was strongly correlated with the spatial clustering of co-tuned synaptic inputs within the dendritic field. Dendritic branches with more co-tuned synaptic clusters exhibited greater rates of local dendritic calcium events supporting a prominent role for functional clustering of synaptic inputs in dendritic nonlinearities that shape orientation selectivity. PMID:27294510

SNDR enhancement in noisy sinusoidal signals by non-linear processing elements

NASA Astrophysics Data System (ADS)

Martorell, Ferran; McDonnell, Mark D.; Abbott, Derek; Rubio, Antonio

2007-06-01

We investigate the possibility of building linear amplifiers capable of enhancing the Signal-to-Noise and Distortion Ratio (SNDR) of sinusoidal input signals using simple non-linear elements. Other works have proven that it is possible to enhance the Signal-to-Noise Ratio (SNR) by using limiters. In this work we study a soft limiter non-linear element with and without hysteresis. We show that the SNDR of sinusoidal signals can be enhanced by 0.94 dB using a wideband soft limiter and up to 9.68 dB using a wideband soft limiter with hysteresis. These results indicate that linear amplifiers could be constructed using non-linear circuits with hysteresis. This paper presents mathematical descriptions for the non-linear elements using statistical parameters. Using these models, the input-output SNDR enhancement is obtained by optimizing the non-linear transfer function parameters to maximize the output SNDR.

Mode-selective mapping and control of vectorial nonlinear-optical processes in multimode photonic-crystal fibers.

PubMed

Hu, Ming-Lie; Wang, Ching-Yue; Song, You-Jian; Li, Yan-Feng; Chai, Lu; Serebryannikov, Evgenii; Zheltikov, Aleksei

2006-02-06

We demonstrate an experimental technique that allows a mapping of vectorial nonlinear-optical processes in multimode photonic-crystal fibers (PCFs). Spatial and polarization modes of PCFs are selectively excited in this technique by varying the tilt angle of the input beam and rotating the polarization of the input field. Intensity spectra of the PCF output plotted as a function of the input field power and polarization then yield mode-resolved maps of nonlinear-optical interactions in multimode PCFs, facilitating the analysis and control of nonlinear-optical transformations of ultrashort laser pulses in such fibers.

Systems and methods for compensating for electrical converter nonlinearities

DOEpatents

Perisic, Milun; Ransom, Ray M.; Kajouke, Lateef A.

2013-06-18

Systems and methods are provided for delivering energy from an input interface to an output interface. An electrical system includes an input interface, an output interface, an energy conversion module coupled between the input interface and the output interface, and a control module. The control module determines a duty cycle control value for operating the energy conversion module to produce a desired voltage at the output interface. The control module determines an input power error at the input interface and adjusts the duty cycle control value in a manner that is influenced by the input power error, resulting in a compensated duty cycle control value. The control module operates switching elements of the energy conversion module to deliver energy to the output interface with a duty cycle that is influenced by the compensated duty cycle control value.

Adaptive fuzzy dynamic surface control of nonlinear systems with input saturation and time-varying output constraints

NASA Astrophysics Data System (ADS)

Edalati, L.; Khaki Sedigh, A.; Aliyari Shooredeli, M.; Moarefianpour, A.

2018-02-01

This paper deals with the design of adaptive fuzzy dynamic surface control for uncertain strict-feedback nonlinear systems with asymmetric time-varying output constraints in the presence of input saturation. To approximate the unknown nonlinear functions and overcome the problem of explosion of complexity, a Fuzzy logic system is combined with the dynamic surface control in the backstepping design technique. To ensure the output constraints satisfaction, an asymmetric time-varying Barrier Lyapunov Function (BLF) is used. Moreover, by applying the minimal learning parameter technique, the number of the online parameters update for each subsystem is reduced to 2. Hence, the semi-globally uniformly ultimately boundedness (SGUUB) of all the closed-loop signals with appropriate tracking error convergence is guaranteed. The effectiveness of the proposed control is demonstrated by two simulation examples.

Model Based Predictive Control of Multivariable Hammerstein Processes with Fuzzy Logic Hypercube Interpolated Models

PubMed Central

Coelho, Antonio Augusto Rodrigues

2016-01-01

This paper introduces the Fuzzy Logic Hypercube Interpolator (FLHI) and demonstrates applications in control of multiple-input single-output (MISO) and multiple-input multiple-output (MIMO) processes with Hammerstein nonlinearities. FLHI consists of a Takagi-Sugeno fuzzy inference system where membership functions act as kernel functions of an interpolator. Conjunction of membership functions in an unitary hypercube space enables multivariable interpolation of N-dimensions. Membership functions act as interpolation kernels, such that choice of membership functions determines interpolation characteristics, allowing FLHI to behave as a nearest-neighbor, linear, cubic, spline or Lanczos interpolator, to name a few. The proposed interpolator is presented as a solution to the modeling problem of static nonlinearities since it is capable of modeling both a function and its inverse function. Three study cases from literature are presented, a single-input single-output (SISO) system, a MISO and a MIMO system. Good results are obtained regarding performance metrics such as set-point tracking, control variation and robustness. Results demonstrate applicability of the proposed method in modeling Hammerstein nonlinearities and their inverse functions for implementation of an output compensator with Model Based Predictive Control (MBPC), in particular Dynamic Matrix Control (DMC). PMID:27657723

Nonlinear Dynamic Models in Advanced Life Support

NASA Technical Reports Server (NTRS)

Jones, Harry

2002-01-01

To facilitate analysis, ALS systems are often assumed to be linear and time invariant, but they usually have important nonlinear and dynamic aspects. Nonlinear dynamic behavior can be caused by time varying inputs, changes in system parameters, nonlinear system functions, closed loop feedback delays, and limits on buffer storage or processing rates. Dynamic models are usually cataloged according to the number of state variables. The simplest dynamic models are linear, using only integration, multiplication, addition, and subtraction of the state variables. A general linear model with only two state variables can produce all the possible dynamic behavior of linear systems with many state variables, including stability, oscillation, or exponential growth and decay. Linear systems can be described using mathematical analysis. Nonlinear dynamics can be fully explored only by computer simulations of models. Unexpected behavior is produced by simple models having only two or three state variables with simple mathematical relations between them. Closed loop feedback delays are a major source of system instability. Exceeding limits on buffer storage or processing rates forces systems to change operating mode. Different equilibrium points may be reached from different initial conditions. Instead of one stable equilibrium point, the system may have several equilibrium points, oscillate at different frequencies, or even behave chaotically, depending on the system inputs and initial conditions. The frequency spectrum of an output oscillation may contain harmonics and the sums and differences of input frequencies, but it may also contain a stable limit cycle oscillation not related to input frequencies. We must investigate the nonlinear dynamic aspects of advanced life support systems to understand and counter undesirable behavior.

Nonlinear predictive control of a boiler-turbine unit: A state-space approach with successive on-line model linearisation and quadratic optimisation.

PubMed

Ławryńczuk, Maciej

2017-03-01

This paper details development of a Model Predictive Control (MPC) algorithm for a boiler-turbine unit, which is a nonlinear multiple-input multiple-output process. The control objective is to follow set-point changes imposed on two state (output) variables and to satisfy constraints imposed on three inputs and one output. In order to obtain a computationally efficient control scheme, the state-space model is successively linearised on-line for the current operating point and used for prediction. In consequence, the future control policy is easily calculated from a quadratic optimisation problem. For state estimation the extended Kalman filter is used. It is demonstrated that the MPC strategy based on constant linear models does not work satisfactorily for the boiler-turbine unit whereas the discussed algorithm with on-line successive model linearisation gives practically the same trajectories as the truly nonlinear MPC controller with nonlinear optimisation repeated at each sampling instant. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Experimental implementation of a nonlinear beamsplitter based on a phase-sensitive parametric amplifier

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fang, Yami; Feng, Jingliang; Cao, Leiming

2016-03-28

Beamsplitters have played an important role in quantum optics experiments. They are often used to split and combine two beams, especially in the construct of an interferometer. In this letter, we experimentally implement a nonlinear beamsplitter using a phase-sensitive parametric amplifier, which is based on four-wave mixing in hot rubidium vapor. Here we show that, despite the different frequencies of the two input beams, the output ports of the nonlinear beamsplitter exhibit interference phenomena. We make measurements of the interference fringe visibility and study how various parameters, such as the intensity gain of the amplifier, the intensity ratio of themore » two input beams, and the one and two photon detunings, affect the behavior of the nonlinear beamsplitter. It may find potential applications in quantum metrology and quantum information processing.« less

Experimental comparison of conventional and nonlinear model-based control of a mixing tank

DOE Office of Scientific and Technical Information (OSTI.GOV)

Haeggblom, K.E.

1993-11-01

In this case study concerning control of a laboratory-scale mixing tank, conventional multiloop single-input single-output (SISO) control is compared with model-based'' control where the nonlinearity and multivariable characteristics of the process are explicitly taken into account. It is shown, especially if the operating range of the process is large, that the two outputs (level and temperature) cannot be adequately controlled by multiloop SISO control even if gain scheduling is used. By nonlinear multiple-input multiple-output (MIMO) control, on the other hand, very good control performance is obtained. The basic approach to nonlinear control used in this study is first to transformmore » the process into a globally linear and decoupled system, and then to design controllers for this system. Because of the properties of the resulting MIMO system, the controller design is very easy. Two nonlinear control system designs based on a steady-state and a dynamic model, respectively, are considered. In the dynamic case, both setpoint tracking and disturbance rejection can be addressed separately.« less

Terminal sliding mode tracking control for a class of SISO uncertain nonlinear systems.

PubMed

Chen, Mou; Wu, Qing-Xian; Cui, Rong-Xin

2013-03-01

In this paper, the terminal sliding mode tracking control is proposed for the uncertain single-input and single-output (SISO) nonlinear system with unknown external disturbance. For the unmeasured disturbance of nonlinear systems, terminal sliding mode disturbance observer is presented. The developed disturbance observer can guarantee the disturbance approximation error to converge to zero in the finite time. Based on the output of designed disturbance observer, the terminal sliding mode tracking control is presented for uncertain SISO nonlinear systems. Subsequently, terminal sliding mode tracking control is developed using disturbance observer technique for the uncertain SISO nonlinear system with control singularity and unknown non-symmetric input saturation. The effects of the control singularity and unknown input saturation are combined with the external disturbance which is approximated using the disturbance observer. Under the proposed terminal sliding mode tracking control techniques, the finite time convergence of all closed-loop signals are guaranteed via Lyapunov analysis. Numerical simulation results are given to illustrate the effectiveness of the proposed terminal sliding mode tracking control. Copyright © 2012 ISA. Published by Elsevier Ltd. All rights reserved.

INDES User's guide multistep input design with nonlinear rotorcraft modeling

NASA Technical Reports Server (NTRS)

1979-01-01

The INDES computer program, a multistep input design program used as part of a data processing technique for rotorcraft systems identification, is described. Flight test inputs base on INDES improve the accuracy of parameter estimates. The input design algorithm, program input, and program output are presented.

Expanded all-optical programmable logic array based on multi-input/output canonical logic units.

PubMed

Lei, Lei; Dong, Jianji; Zou, Bingrong; Wu, Zhao; Dong, Wenchan; Zhang, Xinliang

2014-04-21

We present an expanded all-optical programmable logic array (O-PLA) using multi-input and multi-output canonical logic units (CLUs) generation. Based on four-wave mixing (FWM) in highly nonlinear fiber (HNLF), two-input and three-input CLUs are simultaneously achieved in five different channels with an operation speed of 40 Gb/s. Clear temporal waveforms and wide open eye diagrams are successfully observed. The effectiveness of the scheme is validated by extinction ratio and optical signal-to-noise ratio measurements. The computing capacity, defined as the total amount of logic functions achieved by the O-PLA, is discussed in detail. For a three-input O-PLA, the computing capacity of the expanded CLUs-PLA is more than two times as large as that of the standard CLUs-PLA, and this multiple will increase to more than three and a half as the idlers are individually independent.

Stable nonlinear Mach-Zehnder fiber switch

DOEpatents

Digonnet, Michel J. F.; Shaw, H. John; Pantell, Richard H.; Sadowski, Robert W.

1999-01-01

An all-optical fiber switch is implemented within a short Mach-Zehnder interferometer configuration. The Mach-Zehnder switch is constructed to have a high temperature stability so as to minimize temperature gradients and other thermal effects which result in undesirable instability at the output of the switch. The Mach-Zehnder switch of the preferred embodiment is advantageously less than 2 cm in length between couplers to be sufficiently short to be thermally stable, and full switching is accomplished by heavily doping one or both of the arms between the couplers so as to provide a highly nonlinear region within one or both of the arms. A pump input source is used to affect the propagation characteristics of one of the arms to control the output coupling ratio of the switch. Because of the high nonlinearity of the pump input arm, low pump powers can be used, thereby alleviating difficulties and high cost associated with high pump input powers.

Adaptive Neural Control of Uncertain MIMO Nonlinear Systems With State and Input Constraints.

PubMed

Chen, Ziting; Li, Zhijun; Chen, C L Philip

2017-06-01

An adaptive neural control strategy for multiple input multiple output nonlinear systems with various constraints is presented in this paper. To deal with the nonsymmetric input nonlinearity and the constrained states, the proposed adaptive neural control is combined with the backstepping method, radial basis function neural network, barrier Lyapunov function (BLF), and disturbance observer. By ensuring the boundedness of the BLF of the closed-loop system, it is demonstrated that the output tracking is achieved with all states remaining in the constraint sets and the general assumption on nonsingularity of unknown control coefficient matrices has been eliminated. The constructed adaptive neural control has been rigorously proved that it can guarantee the semiglobally uniformly ultimate boundedness of all signals in the closed-loop system. Finally, the simulation studies on a 2-DOF robotic manipulator system indicate that the designed adaptive control is effective.

Finding identifiable parameter combinations in nonlinear ODE models and the rational reparameterization of their input-output equations.

PubMed

Meshkat, Nicolette; Anderson, Chris; Distefano, Joseph J

2011-09-01

When examining the structural identifiability properties of dynamic system models, some parameters can take on an infinite number of values and yet yield identical input-output data. These parameters and the model are then said to be unidentifiable. Finding identifiable combinations of parameters with which to reparameterize the model provides a means for quantitatively analyzing the model and computing solutions in terms of the combinations. In this paper, we revisit and explore the properties of an algorithm for finding identifiable parameter combinations using Gröbner Bases and prove useful theoretical properties of these parameter combinations. We prove a set of M algebraically independent identifiable parameter combinations can be found using this algorithm and that there exists a unique rational reparameterization of the input-output equations over these parameter combinations. We also demonstrate application of the procedure to a nonlinear biomodel. Copyright © 2011 Elsevier Inc. All rights reserved.

Compensation for electrical converter nonlinearities

DOEpatents

Perisic, Milun; Ransom, Ray M; Kajouke, Lateef A

2013-11-19

Systems and methods are provided for delivering energy from an input interface to an output interface. An electrical system includes an input interface, an output interface, an energy conversion module between the input interface and the output interface, an inductive element between the input interface and the energy conversion module, and a control module. The control module determines a compensated duty cycle control value for operating the energy conversion module to produce a desired voltage at the output interface and operates the energy conversion module to deliver energy to the output interface with a duty cycle that is influenced by the compensated duty cycle control value. The compensated duty cycle control value is influenced by the current through the inductive element and accounts for voltage across the switching elements of the energy conversion module.

Adaptive filtering with the self-organizing map: a performance comparison.

PubMed

Barreto, Guilherme A; Souza, Luís Gustavo M

2006-01-01

In this paper we provide an in-depth evaluation of the SOM as a feasible tool for nonlinear adaptive filtering. A comprehensive survey of existing SOM-based and related architectures for learning input-output mappings is carried out and the application of these architectures to nonlinear adaptive filtering is formulated. Then, we introduce two simple procedures for building RBF-based nonlinear filters using the Vector-Quantized Temporal Associative Memory (VQTAM), a recently proposed method for learning dynamical input-output mappings using the SOM. The aforementioned SOM-based adaptive filters are compared with standard FIR/LMS and FIR/LMS-Newton linear transversal filters, as well as with powerful MLP-based filters in nonlinear channel equalization and inverse modeling tasks. The obtained results in both tasks indicate that SOM-based filters can consistently outperform powerful MLP-based ones.

Singularity perturbed zero dynamics of nonlinear systems

NASA Technical Reports Server (NTRS)

Isidori, A.; Sastry, S. S.; Kokotovic, P. V.; Byrnes, C. I.

1992-01-01

Stability properties of zero dynamics are among the crucial input-output properties of both linear and nonlinear systems. Unstable, or 'nonminimum phase', zero dynamics are a major obstacle to input-output linearization and high-gain designs. An analysis of the effects of regular perturbations in system equations on zero dynamics shows that whenever a perturbation decreases the system's relative degree, it manifests itself as a singular perturbation of zero dynamics. Conditions are given under which the zero dynamics evolve in two timescales characteristic of a standard singular perturbation form that allows a separate analysis of slow and fast parts of the zero dynamics.

Sensitivity Analysis of the Integrated Medical Model for ISS Programs

NASA Technical Reports Server (NTRS)

Goodenow, D. A.; Myers, J. G.; Arellano, J.; Boley, L.; Garcia, Y.; Saile, L.; Walton, M.; Kerstman, E.; Reyes, D.; Young, M.

2016-01-01

Sensitivity analysis estimates the relative contribution of the uncertainty in input values to the uncertainty of model outputs. Partial Rank Correlation Coefficient (PRCC) and Standardized Rank Regression Coefficient (SRRC) are methods of conducting sensitivity analysis on nonlinear simulation models like the Integrated Medical Model (IMM). The PRCC method estimates the sensitivity using partial correlation of the ranks of the generated input values to each generated output value. The partial part is so named because adjustments are made for the linear effects of all the other input values in the calculation of correlation between a particular input and each output. In SRRC, standardized regression-based coefficients measure the sensitivity of each input, adjusted for all the other inputs, on each output. Because the relative ranking of each of the inputs and outputs is used, as opposed to the values themselves, both methods accommodate the nonlinear relationship of the underlying model. As part of the IMM v4.0 validation study, simulations are available that predict 33 person-missions on ISS and 111 person-missions on STS. These simulated data predictions feed the sensitivity analysis procedures. The inputs to the sensitivity procedures include the number occurrences of each of the one hundred IMM medical conditions generated over the simulations and the associated IMM outputs: total quality time lost (QTL), number of evacuations (EVAC), and number of loss of crew lives (LOCL). The IMM team will report the results of using PRCC and SRRC on IMM v4.0 predictions of the ISS and STS missions created as part of the external validation study. Tornado plots will assist in the visualization of the condition-related input sensitivities to each of the main outcomes. The outcomes of this sensitivity analysis will drive review focus by identifying conditions where changes in uncertainty could drive changes in overall model output uncertainty. These efforts are an integral part of the overall verification, validation, and credibility review of IMM v4.0.

Achieving nonlinear optical modulation via four-wave mixing in a four-level atomic system

NASA Astrophysics Data System (ADS)

Li, Hai-Chao; Ge, Guo-Qin; Zubairy, M. Suhail

2018-05-01

We propose an accessible scheme for implementing tunable nonlinear optical amplification and attenuation via a synergetic mechanism of four-wave mixing (FWM) and optical interference in a four-level ladder-type atomic system. By constructing a cyclic atom-field interaction, we show that two reverse FWM processes can coexist via optical transitions in different branches. In the suitable input-field conditions, strong interference effects between the input fields and the generated FWM fields can be induced and result in large amplification and deep attenuation of the output fields. Moreover, such an optical modulation from enhancement to suppression can be controlled by tuning the relative phase. The quantum system can be served as a switchable optical modulator with potential applications in quantum nonlinear optics.

A consensual neural network

NASA Technical Reports Server (NTRS)

Benediktsson, J. A.; Ersoy, O. K.; Swain, P. H.

1991-01-01

A neural network architecture called a consensual neural network (CNN) is proposed for the classification of data from multiple sources. Its relation to hierarchical and ensemble neural networks is discussed. CNN is based on the statistical consensus theory and uses nonlinearly transformed input data. The input data are transformed several times, and the different transformed data are applied as if they were independent inputs. The independent inputs are classified using stage neural networks and outputs from the stage networks are then weighted and combined to make a decision. Experimental results based on remote-sensing data and geographic data are given.

Assessment of input-output properties and control of neuroprosthetic hand grasp.

PubMed

Hines, A E; Owens, N E; Crago, P E

1992-06-01

Three tests have been developed to evaluate rapidly and quantitatively the input-output properties and patient control of neuroprosthetic hand grasp. Each test utilizes a visual pursuit tracking task during which the subject controls the grasp force and grasp opening (position) of the hand. The first test characterizes the static input-output properties of the hand grasp, where the input is a slowly changing patient generated command signal and the outputs are grasp force and grasp opening. Nonlinearities and inappropriate slopes have been documented in these relationships, and in some instances the need for system returning has been indicated. For each subject larger grasp forces were produced when grasping larger objects, and for some subjects the shapes of the relationships also varied with object size. The second test quantifies the ability of the subject to control the hand grasp outputs while tracking steps and ramps. Neuroprosthesis users had rms errors two to three times larger when tracking steps versus ramps, and had rms errors four to five times larger than normals when tracking ramps. The third test provides an estimate of the frequency response of the hand grasp system dynamics, from input and output data collected during a random tracking task. Transfer functions were estimated by spectral analysis after removal of the static input-output nonlinearities measured in the first test. The dynamics had low-pass filter characteristics with 3 dB cutoff frequencies from 1.0 to 1.4 Hz. The tests developed in this study provide a rapid evaluation of both the system and the user. They provide information to 1) help interpret subject performance of functional tasks, 2) evaluate the efficacy of system features such as closed-loop control, and 3) screen the neuroprosthesis to indicate the need for retuning.

Data-driven model reference control of MIMO vertical tank systems with model-free VRFT and Q-Learning.

PubMed

Radac, Mircea-Bogdan; Precup, Radu-Emil; Roman, Raul-Cristian

2018-02-01

This paper proposes a combined Virtual Reference Feedback Tuning-Q-learning model-free control approach, which tunes nonlinear static state feedback controllers to achieve output model reference tracking in an optimal control framework. The novel iterative Batch Fitted Q-learning strategy uses two neural networks to represent the value function (critic) and the controller (actor), and it is referred to as a mixed Virtual Reference Feedback Tuning-Batch Fitted Q-learning approach. Learning convergence of the Q-learning schemes generally depends, among other settings, on the efficient exploration of the state-action space. Handcrafting test signals for efficient exploration is difficult even for input-output stable unknown processes. Virtual Reference Feedback Tuning can ensure an initial stabilizing controller to be learned from few input-output data and it can be next used to collect substantially more input-state data in a controlled mode, in a constrained environment, by compensating the process dynamics. This data is used to learn significantly superior nonlinear state feedback neural networks controllers for model reference tracking, using the proposed Batch Fitted Q-learning iterative tuning strategy, motivating the original combination of the two techniques. The mixed Virtual Reference Feedback Tuning-Batch Fitted Q-learning approach is experimentally validated for water level control of a multi input-multi output nonlinear constrained coupled two-tank system. Discussions on the observed control behavior are offered. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Vulnerability of dynamic systems

NASA Technical Reports Server (NTRS)

Siljak, D. D.

1976-01-01

Directed graphs are associated with dynamic systems in order to determine in any given system if each state can be reached by at least one input (input reachability), or can each state reach at least one output (output reachability). Then, the structural perturbations of a dynamic system are identified as lines or points removals from the corresponding digraph, and a system is considered vulnerable at those lines or points of the digraph whose removal destroys its input or output reachability. A suitable framework is formulated for resolving the problems of reachability and vulnerability which applies to both linear and nonlinear systems alike.

Distributed Adaptive Neural Network Output Tracking of Leader-Following High-Order Stochastic Nonlinear Multiagent Systems With Unknown Dead-Zone Input.

PubMed

Hua, Changchun; Zhang, Liuliu; Guan, Xinping

2017-01-01

This paper studies the problem of distributed output tracking consensus control for a class of high-order stochastic nonlinear multiagent systems with unknown nonlinear dead-zone under a directed graph topology. The adaptive neural networks are used to approximate the unknown nonlinear functions and a new inequality is used to deal with the completely unknown dead-zone input. Then, we design the controllers based on backstepping method and the dynamic surface control technique. It is strictly proved that the resulting closed-loop system is stable in probability in the sense of semiglobally uniform ultimate boundedness and the tracking errors between the leader and the followers approach to a small residual set based on Lyapunov stability theory. Finally, two simulation examples are presented to show the effectiveness and the advantages of the proposed techniques.

Nonlinear SVM-DTC for induction motor drive using input-output feedback linearization and high order sliding mode control.

PubMed

Ammar, Abdelkarim; Bourek, Amor; Benakcha, Abdelhamid

2017-03-01

This paper presents a nonlinear Direct Torque Control (DTC) strategy with Space Vector Modulation (SVM) for an induction motor. A nonlinear input-output feedback linearization (IOFL) is implemented to achieve a decoupled torque and flux control and the SVM is employed to reduce high torque and flux ripples. Furthermore, the control scheme performance is improved by inserting a super twisting speed controller in the outer loop and a load torque observer to enhance the speed regulation. The combining of dual nonlinear strategies ensures a good dynamic and robustness against parameters variation and disturbance. The system stability has been analyzed using Lyapunov stability theory. The effectiveness of the control algorithm is investigated by simulation and experimental validation using Matlab/Simulink software with real-time interface based on dSpace 1104. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Non-linear transfer characteristics of stimulation and recording hardware account for spurious low-frequency artifacts during amplitude modulated transcranial alternating current stimulation (AM-tACS).

PubMed

Kasten, Florian H; Negahbani, Ehsan; Fröhlich, Flavio; Herrmann, Christoph S

2018-05-31

Amplitude modulated transcranial alternating current stimulation (AM-tACS) has been recently proposed as a possible solution to overcome the pronounced stimulation artifact encountered when recording brain activity during tACS. In theory, AM-tACS does not entail power at its modulating frequency, thus avoiding the problem of spectral overlap between brain signal of interest and stimulation artifact. However, the current study demonstrates how weak non-linear transfer characteristics inherent to stimulation and recording hardware can reintroduce spurious artifacts at the modulation frequency. The input-output transfer functions (TFs) of different stimulation setups were measured. Setups included recordings of signal-generator and stimulator outputs and M/EEG phantom measurements. 6 th -degree polynomial regression models were fitted to model the input-output TFs of each setup. The resulting TF models were applied to digitally generated AM-tACS signals to predict the frequency of spurious artifacts in the spectrum. All four setups measured for the study exhibited low-frequency artifacts at the modulation frequency and its harmonics when recording AM-tACS. Fitted TF models showed non-linear contributions significantly different from zero (all p < .05) and successfully predicted the frequency of artifacts observed in AM-signal recordings. Results suggest that even weak non-linearities of stimulation and recording hardware can lead to spurious artifacts at the modulation frequency and its harmonics. These artifacts were substantially larger than alpha-oscillations of a human subject in the MEG. Findings emphasize the need for more linear stimulation devices for AM-tACS and careful analysis procedures, taking into account low-frequency artifacts to avoid confusion with effects of AM-tACS on the brain. Copyright © 2018 Elsevier Inc. All rights reserved.

Curvilinear component analysis: a self-organizing neural network for nonlinear mapping of data sets.

PubMed

Demartines, P; Herault, J

1997-01-01

We present a new strategy called "curvilinear component analysis" (CCA) for dimensionality reduction and representation of multidimensional data sets. The principle of CCA is a self-organized neural network performing two tasks: vector quantization (VQ) of the submanifold in the data set (input space); and nonlinear projection (P) of these quantizing vectors toward an output space, providing a revealing unfolding of the submanifold. After learning, the network has the ability to continuously map any new point from one space into another: forward mapping of new points in the input space, or backward mapping of an arbitrary position in the output space.

Optimal fixed-finite-dimensional compensator for Burgers' equation with unbounded input/output operators

NASA Technical Reports Server (NTRS)

Burns, John A.; Marrekchi, Hamadi

1993-01-01

The problem of using reduced order dynamic compensators to control a class of nonlinear parabolic distributed parameter systems was considered. Concentration was on a system with unbounded input and output operators governed by Burgers' equation. A linearized model was used to compute low-order-finite-dimensional control laws by minimizing certain energy functionals. Then these laws were applied to the nonlinear model. Standard approaches to this problem employ model/controller reduction techniques in conjunction with linear quadratic Gaussian (LQG) theory. The approach used is based on the finite dimensional Bernstein/Hyland optimal projection theory which yields a fixed-finite-order controller.

A component prediction method for flue gas of natural gas combustion based on nonlinear partial least squares method.

PubMed

Cao, Hui; Yan, Xingyu; Li, Yaojiang; Wang, Yanxia; Zhou, Yan; Yang, Sanchun

2014-01-01

Quantitative analysis for the flue gas of natural gas-fired generator is significant for energy conservation and emission reduction. The traditional partial least squares method may not deal with the nonlinear problems effectively. In the paper, a nonlinear partial least squares method with extended input based on radial basis function neural network (RBFNN) is used for components prediction of flue gas. For the proposed method, the original independent input matrix is the input of RBFNN and the outputs of hidden layer nodes of RBFNN are the extension term of the original independent input matrix. Then, the partial least squares regression is performed on the extended input matrix and the output matrix to establish the components prediction model of flue gas. A near-infrared spectral dataset of flue gas of natural gas combustion is used for estimating the effectiveness of the proposed method compared with PLS. The experiments results show that the root-mean-square errors of prediction values of the proposed method for methane, carbon monoxide, and carbon dioxide are, respectively, reduced by 4.74%, 21.76%, and 5.32% compared to those of PLS. Hence, the proposed method has higher predictive capabilities and better robustness.

Identification and modeling of the electrohydraulic systems of the main gun of a main battle tank

NASA Astrophysics Data System (ADS)

Campos, Luiz C. A.; Menegaldo, Luciano L.

2012-11-01

The black-box mathematical models of the electrohydraulic systems responsible for driving the two degrees of freedom (elevation and azimuth) of the main gun of a main battle tank (MBT) were identified. Such systems respond to gunner's inputs while acquiring and tracking targets. Identification experiments were designed to collect simultaneous data from two inertial measurement units (IMU) installed at the gunner's handle (input) and at the center of rotation of the turret (output), for the identification of the azimuth system. For the elevation system, IMUs were installed at the gunner's handle (input) and at the breech of the gun (output). Linear accelerations and angular rates were collected for both input and output. Several black-box model architectures were investigated. As a result, nonlinear autoregressive with exogenous variables (NARX) second order model and nonlinear finite impulse response (NFIR) fourth order model, demonstrate to best fit the experimental data, with low computational costs. The derived models are being employed in a broader research, aiming to reproduce such systems in a laboratory virtual main gun simulator.

Optimal Frequency-Domain System Realization with Weighting

NASA Technical Reports Server (NTRS)

Juang, Jer-Nan; Maghami, Peiman G.

1999-01-01

Several approaches are presented to identify an experimental system model directly from frequency response data. The formulation uses a matrix-fraction description as the model structure. Frequency weighting such as exponential weighting is introduced to solve a weighted least-squares problem to obtain the coefficient matrices for the matrix-fraction description. A multi-variable state-space model can then be formed using the coefficient matrices of the matrix-fraction description. Three different approaches are introduced to fine-tune the model using nonlinear programming methods to minimize the desired cost function. The first method uses an eigenvalue assignment technique to reassign a subset of system poles to improve the identified model. The second method deals with the model in the real Schur or modal form, reassigns a subset of system poles, and adjusts the columns (rows) of the input (output) influence matrix using a nonlinear optimizer. The third method also optimizes a subset of poles, but the input and output influence matrices are refined at every optimization step through least-squares procedures.

Visuo-manual tracking: does intermittent control with aperiodic sampling explain linear power and non-linear remnant without sensorimotor noise?

PubMed

Gollee, Henrik; Gawthrop, Peter J; Lakie, Martin; Loram, Ian D

2017-11-01

A human controlling an external system is described most easily and conventionally as linearly and continuously translating sensory input to motor output, with the inevitable output remnant, non-linearly related to the input, attributed to sensorimotor noise. Recent experiments show sustained manual tracking involves repeated refractoriness (insensitivity to sensory information for a certain duration), with the temporary 200-500 ms periods of irresponsiveness to sensory input making the control process intrinsically non-linear. This evidence calls for re-examination of the extent to which random sensorimotor noise is required to explain the non-linear remnant. This investigation of manual tracking shows how the full motor output (linear component and remnant) can be explained mechanistically by aperiodic sampling triggered by prediction error thresholds. Whereas broadband physiological noise is general to all processes, aperiodic sampling is associated with sensorimotor decision making within specific frontal, striatal and parietal networks; we conclude that manual tracking utilises such slow serial decision making pathways up to several times per second. The human operator is described adequately by linear translation of sensory input to motor output. Motor output also always includes a non-linear remnant resulting from random sensorimotor noise from multiple sources, and non-linear input transformations, for example thresholds or refractory periods. Recent evidence showed that manual tracking incurs substantial, serial, refractoriness (insensitivity to sensory information of 350 and 550 ms for 1st and 2nd order systems respectively). Our two questions are: (i) What are the comparative merits of explaining the non-linear remnant using noise or non-linear transformations? (ii) Can non-linear transformations represent serial motor decision making within the sensorimotor feedback loop intrinsic to tracking? Twelve participants (instructed to act in three prescribed ways) manually controlled two systems (1st and 2nd order) subject to a periodic multi-sine disturbance. Joystick power was analysed using three models, continuous-linear-control (CC), continuous-linear-control with calculated noise spectrum (CCN), and intermittent control with aperiodic sampling triggered by prediction error thresholds (IC). Unlike the linear mechanism, the intermittent control mechanism explained the majority of total power (linear and remnant) (77-87% vs. 8-48%, IC vs. CC). Between conditions, IC used thresholds and distributions of open loop intervals consistent with, respectively, instructions and previous measured, model independent values; whereas CCN required changes in noise spectrum deviating from broadband, signal dependent noise. We conclude that manual tracking uses open loop predictive control with aperiodic sampling. Because aperiodic sampling is inherent to serial decision making within previously identified, specific frontal, striatal and parietal networks we suggest that these structures are intimately involved in visuo-manual tracking. © 2017 The Authors. The Journal of Physiology published by John Wiley & Sons Ltd on behalf of The Physiological Society.

Tripartite correlations over two octaves from cascaded harmonic generation

NASA Astrophysics Data System (ADS)

Olsen, M. K.

2018-03-01

We analyse the output quantum tripartite correlations from an intracavity nonlinear optical system which uses cascaded nonlinearities to produce both second and fourth harmonic outputs from an input field at the fundamental frequency. Using fully quantum equations of motion, we investigate two parameter regimes and show that the system produces tripartite inseparability, entanglement and EPR steering, with the detection of these depending on the correlations being considered.

Robust adaptive fuzzy tracking control for pure-feedback stochastic nonlinear systems with input constraints.

PubMed

Wang, Huanqing; Chen, Bing; Liu, Xiaoping; Liu, Kefu; Lin, Chong

2013-12-01

This paper is concerned with the problem of adaptive fuzzy tracking control for a class of pure-feedback stochastic nonlinear systems with input saturation. To overcome the design difficulty from nondifferential saturation nonlinearity, a smooth nonlinear function of the control input signal is first introduced to approximate the saturation function; then, an adaptive fuzzy tracking controller based on the mean-value theorem is constructed by using backstepping technique. The proposed adaptive fuzzy controller guarantees that all signals in the closed-loop system are bounded in probability and the system output eventually converges to a small neighborhood of the desired reference signal in the sense of mean quartic value. Simulation results further illustrate the effectiveness of the proposed control scheme.

Wideband nonlinear spectral broadening in ultra-short ultra - silicon rich nitride waveguides.

PubMed

Choi, Ju Won; Chen, George F R; Ng, D K T; Ooi, Kelvin J A; Tan, Dawn T H

2016-06-08

CMOS-compatible nonlinear optics platforms with high Kerr nonlinearity facilitate the generation of broadband spectra based on self-phase modulation. Our ultra - silicon rich nitride (USRN) platform is designed to have a large nonlinear refractive index and low nonlinear losses at 1.55 μm for the facilitation of wideband spectral broadening. We investigate the ultrafast spectral characteristics of USRN waveguides with 1-mm-length, which have high nonlinear parameters (γ ∼ 550 W(-1)/m) and anomalous dispersion at 1.55 μm wavelength of input light. USRN add-drop ring resonators broaden output spectra by a factor of 2 compared with the bandwidth of input fs laser with the highest quality factors of 11000 and 15000. Two - fold self phase modulation induced spectral broadening is observed using waveguides only 430 μm in length, whereas a quadrupling of the output bandwidth is observed with USRN waveguides with a 1-mm-length. A broadening factor of around 3 per 1 mm length is achieved in the USRN waveguides, a value which is comparatively larger than many other CMOS-compatible platforms.

Efficient Digital Implementation of The Sigmoidal Function For Artificial Neural Network

NASA Astrophysics Data System (ADS)

Pratap, Rana; Subadra, M.

2011-10-01

An efficient piecewise linear approximation of a nonlinear function (PLAN) is proposed. This uses simulink environment design to perform a direct transformation from X to Y, where X is the input and Y is the approximated sigmoidal output. This PLAN is then used within the outputs of an artificial neural network to perform the nonlinear approximation. In This paper, is proposed a method to implement in FPGA (Field Programmable Gate Array) circuits different approximation of the sigmoid function.. The major benefit of the proposed method resides in the possibility to design neural networks by means of predefined block systems created in System Generator environment and the possibility to create a higher level design tools used to implement neural networks in logical circuits.

200-W single frequency laser based on short active double clad tapered fiber

NASA Astrophysics Data System (ADS)

Pierre, Christophe; Guiraud, Germain; Yehouessi, Jean-Paul; Santarelli, Giorgio; Boullet, Johan; Traynor, Nicholas; Vincont, Cyril

2018-02-01

High power single frequency lasers are very attractive for a wide range of applications such as nonlinear conversion, gravitational wave sensing or atom trapping. Power scaling in single frequency regime is a challenging domain of research. In fact, nonlinear effect as stimulated Brillouin scattering (SBS) is the primary power limitation in single frequency amplifiers. To mitigate SBS, different well-known techniques has been improved. These techniques allow generation of several hundred of watts [1]. Large mode area (LMA) fibers, transverse acoustically tailored fibers [2], coherent beam combining and also tapered fiber [3] seem to be serious candidates to continue the power scaling. We have demonstrated the generation of stable 200W output power with nearly diffraction limited output, and narrow linewidth (Δν<30kHz) by using a tapered Yb-doped fiber which allow an adiabatic transition from a small purely single mode input to a large core output.

Enhanced Sensitivity to Rapid Input Fluctuations by Nonlinear Threshold Dynamics in Neocortical Pyramidal Neurons.

PubMed

Mensi, Skander; Hagens, Olivier; Gerstner, Wulfram; Pozzorini, Christian

2016-02-01

The way in which single neurons transform input into output spike trains has fundamental consequences for network coding. Theories and modeling studies based on standard Integrate-and-Fire models implicitly assume that, in response to increasingly strong inputs, neurons modify their coding strategy by progressively reducing their selective sensitivity to rapid input fluctuations. Combining mathematical modeling with in vitro experiments, we demonstrate that, in L5 pyramidal neurons, the firing threshold dynamics adaptively adjust the effective timescale of somatic integration in order to preserve sensitivity to rapid signals over a broad range of input statistics. For that, a new Generalized Integrate-and-Fire model featuring nonlinear firing threshold dynamics and conductance-based adaptation is introduced that outperforms state-of-the-art neuron models in predicting the spiking activity of neurons responding to a variety of in vivo-like fluctuating currents. Our model allows for efficient parameter extraction and can be analytically mapped to a Generalized Linear Model in which both the input filter--describing somatic integration--and the spike-history filter--accounting for spike-frequency adaptation--dynamically adapt to the input statistics, as experimentally observed. Overall, our results provide new insights on the computational role of different biophysical processes known to underlie adaptive coding in single neurons and support previous theoretical findings indicating that the nonlinear dynamics of the firing threshold due to Na+-channel inactivation regulate the sensitivity to rapid input fluctuations.

Desired Accuracy Estimation of Noise Function from ECG Signal by Fuzzy Approach

PubMed Central

Vahabi, Zahra; Kermani, Saeed

2012-01-01

Unknown noise and artifacts present in medical signals with non-linear fuzzy filter will be estimated and then removed. An adaptive neuro-fuzzy interference system which has a non-linear structure presented for the noise function prediction by before Samples. This paper is about a neuro-fuzzy method to estimate unknown noise of Electrocardiogram signal. Adaptive neural combined with Fuzzy System to construct a fuzzy Predictor. For this system setting parameters such as the number of Membership Functions for each input and output, training epochs, type of MFs for each input and output, learning algorithm and etc. is determined by learning data. At the end simulated experimental results are presented for proper validation. PMID:23717810

Global output feedback control for a class of high-order feedforward nonlinear systems with input delay.

PubMed

Zha, Wenting; Zhai, Junyong; Fei, Shumin

2013-07-01

This paper investigates the problem of output feedback stabilization for a class of high-order feedforward nonlinear systems with time-varying input delay. First, a scaling gain is introduced into the system under a set of coordinate transformations. Then, the authors construct an observer and controller to make the nominal system globally asymptotically stable. Based on homogeneous domination approach and Lyapunov-Krasovskii functional, it is shown that the closed-loop system can be rendered globally asymptotically stable by the scaling gain. Finally, two simulation examples are provided to illustrate the effectiveness of the proposed scheme. Copyright © 2013 ISA. Published by Elsevier Ltd. All rights reserved.

Subwavelength Sensing Using Nonlinear Feedback in a Wave-Chaotic Cavity

DTIC Science & Technology

2013-01-01

r̃in and r̃in are the Fourier transforms of the input pulse and output pulse response, respectively. The magnitude and phase of Hcavity( f ) are plotted...function between its input and output voltages. But, as f increases (beyond ∼ 500 MHz), the amplitudes of the output signals decrease and the phase delay...coupled to free space where it is directed along two paths via a beam splitter (BS), where it is reflected of of mirrors (M1 and M2) that are attached to

Decoding the cortical transformations for visually guided reaching in 3D space.

PubMed

Blohm, Gunnar; Keith, Gerald P; Crawford, J Douglas

2009-06-01

To explore the possible cortical mechanisms underlying the 3-dimensional (3D) visuomotor transformation for reaching, we trained a 4-layer feed-forward artificial neural network to compute a reach vector (output) from the visual positions of both the hand and target viewed from different eye and head orientations (inputs). The emergent properties of the intermediate layers reflected several known neurophysiological findings, for example, gain field-like modulations and position-dependent shifting of receptive fields (RFs). We performed a reference frame analysis for each individual network unit, simulating standard electrophysiological experiments, that is, RF mapping (unit input), motor field mapping, and microstimulation effects (unit outputs). At the level of individual units (in both intermediate layers), the 3 different electrophysiological approaches identified different reference frames, demonstrating that these techniques reveal different neuronal properties and suggesting that a comparison across these techniques is required to understand the neural code of physiological networks. This analysis showed fixed input-output relationships within each layer and, more importantly, within each unit. These local reference frame transformation modules provide the basic elements for the global transformation; their parallel contributions are combined in a gain field-like fashion at the population level to implement both the linear and nonlinear elements of the 3D visuomotor transformation.

Multi-Target Regression via Robust Low-Rank Learning.

PubMed

Zhen, Xiantong; Yu, Mengyang; He, Xiaofei; Li, Shuo

2018-02-01

Multi-target regression has recently regained great popularity due to its capability of simultaneously learning multiple relevant regression tasks and its wide applications in data mining, computer vision and medical image analysis, while great challenges arise from jointly handling inter-target correlations and input-output relationships. In this paper, we propose Multi-layer Multi-target Regression (MMR) which enables simultaneously modeling intrinsic inter-target correlations and nonlinear input-output relationships in a general framework via robust low-rank learning. Specifically, the MMR can explicitly encode inter-target correlations in a structure matrix by matrix elastic nets (MEN); the MMR can work in conjunction with the kernel trick to effectively disentangle highly complex nonlinear input-output relationships; the MMR can be efficiently solved by a new alternating optimization algorithm with guaranteed convergence. The MMR leverages the strength of kernel methods for nonlinear feature learning and the structural advantage of multi-layer learning architectures for inter-target correlation modeling. More importantly, it offers a new multi-layer learning paradigm for multi-target regression which is endowed with high generality, flexibility and expressive ability. Extensive experimental evaluation on 18 diverse real-world datasets demonstrates that our MMR can achieve consistently high performance and outperforms representative state-of-the-art algorithms, which shows its great effectiveness and generality for multivariate prediction.

Effects of Age of English Exposure, Current Input/Output, and grade on bilingual language performance*

PubMed Central

BEDORE, LISA M.; PEÑA, ELIZABETH D.; GRIFFIN, ZENZI M.; HIXON, J. GREGORY

2018-01-01

This study evaluates the effects of Age of Exposure to English (AoEE) and Current Input/Output on language performance in a cross-sectional sample of Spanish–English bilingual children. First- (N= 586) and third-graders (N= 298) who spanned a wide range of bilingual language experience participated. Parents and teachers provided information about English and Spanish language use. Short tests of semantic and morphosyntactic development in Spanish and English were used to quantify children’s knowledge of each language. There were significant interactions between AoEE and Current Input/Output for children at third grade in English and in both grades for Spanish. In English, the relationship between AoEE and language scores were linear for first- and third-graders. In Spanish a nonlinear relationship was observed. We discuss how much of the variance was accounted for by AoEE and Current Input/Output. PMID:26916066

Output transformations and separation results for feedback linearisable delay systems

NASA Astrophysics Data System (ADS)

Cacace, F.; Conte, F.; Germani, A.

2018-04-01

The class of strict-feedback systems enjoys special properties that make it similar to linear systems. This paper proves that such a class is equivalent, under a change of coordinates, to the wider class of feedback linearisable systems with multiplicative input, when the multiplicative terms are functions of the measured variables only. We apply this result to the control problem of feedback linearisable nonlinear MIMO systems with input and/or output delays. In this way, we provide sufficient conditions under which a separation result holds for output feedback control and moreover a predictor-based controller exists. When these conditions are satisfied, we obtain that the existence of stabilising controllers for arbitrarily large delays in the input and/or the output can be proved for a wider class of systems than previously known.

A Hippocampal Cognitive Prosthesis: Multi-Input, Multi-Output Nonlinear Modeling and VLSI Implementation

PubMed Central

Berger, Theodore W.; Song, Dong; Chan, Rosa H. M.; Marmarelis, Vasilis Z.; LaCoss, Jeff; Wills, Jack; Hampson, Robert E.; Deadwyler, Sam A.; Granacki, John J.

2012-01-01

This paper describes the development of a cognitive prosthesis designed to restore the ability to form new long-term memories typically lost after damage to the hippocampus. The animal model used is delayed nonmatch-to-sample (DNMS) behavior in the rat, and the “core” of the prosthesis is a biomimetic multi-input/multi-output (MIMO) nonlinear model that provides the capability for predicting spatio-temporal spike train output of hippocampus (CA1) based on spatio-temporal spike train inputs recorded presynaptically to CA1 (e.g., CA3). We demonstrate the capability of the MIMO model for highly accurate predictions of CA1 coded memories that can be made on a single-trial basis and in real-time. When hippocampal CA1 function is blocked and long-term memory formation is lost, successful DNMS behavior also is abolished. However, when MIMO model predictions are used to reinstate CA1 memory-related activity by driving spatio-temporal electrical stimulation of hippocampal output to mimic the patterns of activity observed in control conditions, successful DNMS behavior is restored. We also outline the design in very-large-scale integration for a hardware implementation of a 16-input, 16-output MIMO model, along with spike sorting, amplification, and other functions necessary for a total system, when coupled together with electrode arrays to record extracellularly from populations of hippocampal neurons, that can serve as a cognitive prosthesis in behaving animals. PMID:22438335

Coupling induced logical stochastic resonance

NASA Astrophysics Data System (ADS)

Aravind, Manaoj; Murali, K.; Sinha, Sudeshna

2018-06-01

In this work we will demonstrate the following result: when we have two coupled bistable sub-systems, each driven separately by an external logic input signal, the coupled system yields outputs that can be mapped to specific logic gate operations in a robust manner, in an optimal window of noise. So, though the individual systems receive only one logic input each, due to the interplay of coupling, nonlinearity and noise, they cooperatively respond to give a logic output that is a function of both inputs. Thus the emergent collective response of the system, due to the inherent coupling, in the presence of a noise floor, maps consistently to that of logic outputs of the two inputs, a phenomenon we term coupling induced Logical Stochastic Resonance. Lastly, we demonstrate our idea in proof of principle circuit experiments.

Wideband nonlinear spectral broadening in ultra-short ultra - silicon rich nitride waveguides

PubMed Central

Choi, Ju Won; Chen, George F. R.; Ng, D. K. T.; Ooi, Kelvin J. A.; Tan, Dawn T. H.

2016-01-01

CMOS-compatible nonlinear optics platforms with high Kerr nonlinearity facilitate the generation of broadband spectra based on self-phase modulation. Our ultra – silicon rich nitride (USRN) platform is designed to have a large nonlinear refractive index and low nonlinear losses at 1.55 μm for the facilitation of wideband spectral broadening. We investigate the ultrafast spectral characteristics of USRN waveguides with 1-mm-length, which have high nonlinear parameters (γ ∼ 550 W−1/m) and anomalous dispersion at 1.55 μm wavelength of input light. USRN add-drop ring resonators broaden output spectra by a factor of 2 compared with the bandwidth of input fs laser with the highest quality factors of 11000 and 15000. Two – fold self phase modulation induced spectral broadening is observed using waveguides only 430 μm in length, whereas a quadrupling of the output bandwidth is observed with USRN waveguides with a 1-mm-length. A broadening factor of around 3 per 1 mm length is achieved in the USRN waveguides, a value which is comparatively larger than many other CMOS-compatible platforms. PMID:27272558

Applications of information theory, genetic algorithms, and neural models to predict oil flow

NASA Astrophysics Data System (ADS)

Ludwig, Oswaldo; Nunes, Urbano; Araújo, Rui; Schnitman, Leizer; Lepikson, Herman Augusto

2009-07-01

This work introduces a new information-theoretic methodology for choosing variables and their time lags in a prediction setting, particularly when neural networks are used in non-linear modeling. The first contribution of this work is the Cross Entropy Function (XEF) proposed to select input variables and their lags in order to compose the input vector of black-box prediction models. The proposed XEF method is more appropriate than the usually applied Cross Correlation Function (XCF) when the relationship among the input and output signals comes from a non-linear dynamic system. The second contribution is a method that minimizes the Joint Conditional Entropy (JCE) between the input and output variables by means of a Genetic Algorithm (GA). The aim is to take into account the dependence among the input variables when selecting the most appropriate set of inputs for a prediction problem. In short, theses methods can be used to assist the selection of input training data that have the necessary information to predict the target data. The proposed methods are applied to a petroleum engineering problem; predicting oil production. Experimental results obtained with a real-world dataset are presented demonstrating the feasibility and effectiveness of the method.

Reduced order modeling and active flow control of an inlet duct

NASA Astrophysics Data System (ADS)

Ge, Xiaoqing

Many aerodynamic applications require the modeling of compressible flows in or around a body, e.g., the design of aircraft, inlet or exhaust duct, wind turbines, or tall buildings. Traditional methods use wind tunnel experiments and computational fluid dynamics (CFD) to investigate the spatial and temporal distribution of the flows. Although they provide a great deal of insight into the essential characteristics of the flow field, they are not suitable for control analysis and design due to the high physical/computational cost. Many model reduction methods have been studied to reduce the complexity of the flow model. There are two main approaches: linearization based input/output modeling and proper orthogonal decomposition (POD) based model reduction. The former captures mostly the local behavior near a steady state, which is suitable to model laminar flow dynamics. The latter obtains a reduced order model by projecting the governing equation onto an "optimal" subspace and is able to model complex nonlinear flow phenomena. In this research we investigate various model reduction approaches and compare them in flow modeling and control design. We propose an integrated model-based control methodology and apply it to the reduced order modeling and active flow control of compressible flows within a very aggressive (length to exit diameter ratio, L/D, of 1.5) inlet duct and its upstream contraction section. The approach systematically applies reduced order modeling, estimator design, sensor placement and control design to improve the aerodynamic performance. The main contribution of this work is the development of a hybrid model reduction approach that attempts to combine the best features of input/output model identification and POD method. We first identify a linear input/output model by using a subspace algorithm. We next project the difference between CFD response and the identified model response onto a set of POD basis. This trajectory is fit to a nonlinear dynamical model to augment the linear input/output model. Thus, the full system is decomposed into a dominant linear subsystem and a low order nonlinear subsystem. The hybrid model is then used for control design and compared with other modeling methods in CFD simulations. Numerical results indicate that the hybrid model accurately predicts the nonlinear behavior of the flow for a 2D diffuser contraction section model. It also performs best in terms of feedback control design and learning control. Since some outputs of interest (e.g., the AIP pressure recovery) are not observable during normal operations, static and dynamic estimators are designed to recreate the information from available sensor measurements. The latter also provides a state estimation for feedback controller. Based on the reduced order models and estimators, different controllers are designed to improve the aerodynamic performance of the contraction section and inlet duct. The integrated control methodology is evaluated with CFD simulations. Numerical results demonstrate the feasibility and efficacy of the active flow control based on reduced order models. Our reduced order models not only generate a good approximation of the nonlinear flow dynamics over a wide input range, but also help to design controllers that significantly improve the flow response. The tools developed for model reduction, estimator and control design can also be applied to wind tunnel experiment.

All-optical regenerator of multi-channel signals.

PubMed

Li, Lu; Patki, Pallavi G; Kwon, Young B; Stelmakh, Veronika; Campbell, Brandon D; Annamalai, Muthiah; Lakoba, Taras I; Vasilyev, Michael

2017-10-12

One of the main reasons why nonlinear-optical signal processing (regeneration, logic, etc.) has not yet become a practical alternative to electronic processing is that the all-optical elements with nonlinear input-output relationship have remained inherently single-channel devices (just like their electronic counterparts) and, hence, cannot fully utilise the parallel processing potential of optical fibres and amplifiers. The nonlinear input-output transfer function requires strong optical nonlinearity, e.g. self-phase modulation, which, for fundamental reasons, is always accompanied by cross-phase modulation and four-wave mixing. In processing multiple wavelength-division-multiplexing channels, large cross-phase modulation and four-wave mixing crosstalks among the channels destroy signal quality. Here we describe a solution to this problem: an optical signal processor employing a group-delay-managed nonlinear medium where strong self-phase modulation is achieved without such nonlinear crosstalk. We demonstrate, for the first time to our knowledge, simultaneous all-optical regeneration of up to 16 wavelength-division-multiplexing channels by one device. This multi-channel concept can be extended to other nonlinear-optical processing schemes.Nonlinear optical processing devices are not yet fully practical as they are single channel. Here the authors demonstrate all-optical regeneration of up to 16 channels by one device, employing a group-delay-managed nonlinear medium where strong self-phase modulation is achieved without nonlinear inter-channel crosstalk.

Global robust output regulation control for cascaded nonlinear systems using the internal model principle

NASA Astrophysics Data System (ADS)

Yu, Jiang-Bo; Zhao, Yan; Wu, Yu-Qiang

2014-04-01

This article considers the global robust output regulation problem via output feedback for a class of cascaded nonlinear systems with input-to-state stable inverse dynamics. The system uncertainties depend not only on the measured output but also all the unmeasurable states. By introducing an internal model, the output regulation problem is converted into a stabilisation problem for an appropriately augmented system. The designed dynamic controller could achieve the global asymptotic tracking control for a class of time-varying reference signals for the system output while keeping all other closed-loop signals bounded. It is of interest to note that the developed control approach can be applied to the speed tracking control of the fan speed control system. The simulation results demonstrate its effectiveness.

Nonlinear data-driven identification of polymer electrolyte membrane fuel cells for diagnostic purposes: A Volterra series approach

NASA Astrophysics Data System (ADS)

Ritzberger, D.; Jakubek, S.

2017-09-01

In this work, a data-driven identification method, based on polynomial nonlinear autoregressive models with exogenous inputs (NARX) and the Volterra series, is proposed to describe the dynamic and nonlinear voltage and current characteristics of polymer electrolyte membrane fuel cells (PEMFCs). The structure selection and parameter estimation of the NARX model is performed on broad-band voltage/current data. By transforming the time-domain NARX model into a Volterra series representation using the harmonic probing algorithm, a frequency-domain description of the linear and nonlinear dynamics is obtained. With the Volterra kernels corresponding to different operating conditions, information from existing diagnostic tools in the frequency domain such as electrochemical impedance spectroscopy (EIS) and total harmonic distortion analysis (THDA) are effectively combined. Additionally, the time-domain NARX model can be utilized for fault detection by evaluating the difference between measured and simulated output. To increase the fault detectability, an optimization problem is introduced which maximizes this output residual to obtain proper excitation frequencies. As a possible extension it is shown, that by optimizing the periodic signal shape itself that the fault detectability is further increased.

Feedback linearizing control of a MIMO power system

NASA Astrophysics Data System (ADS)

Ilyes, Laszlo

Prior research has demonstrated that either the mechanical or electrical subsystem of a synchronous electric generator may be controlled using single-input single-output (SISO) nonlinear feedback linearization. This research suggests a new approach which applies nonlinear feedback linearization to a multi-input multi-output (MIMO) model of the synchronous electric generator connected to an infinite bus load model. In this way, the electrical and mechanical subsystems may be linearized and simultaneously decoupled through the introduction of a pair of auxiliary inputs. This allows well known, linear, SISO control methods to be effectively applied to the resulting systems. The derivation of the feedback linearizing control law is presented in detail, including a discussion on the use of symbolic math processing as a development tool. The linearizing and decoupling properties of the control law are validated through simulation. And finally, the robustness of the control law is demonstrated.

Noise behavior of microwave amplifiers operating under nonlinear conditions

NASA Astrophysics Data System (ADS)

Escotte, L.; Gonneau, E.; Chambon, C.; Graffeuil, J.

2005-12-01

B The noise behavior of microwave amplifiers operating under a large-signal condition has been studied in this paper. A Gaussian noise is added to a microwave signal and they are applied at the input of several amplifying devices. Experimental data show a decrease of the output noise spectral density when the power of the microwave signal at the input of the devices increases due to the compression of the amplifiers. A distortion component due to the interaction of the signal and its harmonics with the noise is also demonstrated from a simplified theoretical model. The statistical properties of the signal and the noise have also been investigated in order to verify the Gaussianity of the noise at the output of the nonlinear circuits. We have also observed that the majority of the measured devices show some variations of their additive noise versus the input power level.

Feedforward Inhibition Allows Input Summation to Vary in Recurrent Cortical Networks

PubMed Central

2018-01-01

Abstract Brain computations depend on how neurons transform inputs to spike outputs. Here, to understand input-output transformations in cortical networks, we recorded spiking responses from visual cortex (V1) of awake mice of either sex while pairing sensory stimuli with optogenetic perturbation of excitatory and parvalbumin-positive inhibitory neurons. We found that V1 neurons’ average responses were primarily additive (linear). We used a recurrent cortical network model to determine whether these data, as well as past observations of nonlinearity, could be described by a common circuit architecture. Simulations showed that cortical input-output transformations can be changed from linear to sublinear with moderate (∼20%) strengthening of connections between inhibitory neurons, but this change away from linear scaling depends on the presence of feedforward inhibition. Simulating a variety of recurrent connection strengths showed that, compared with when input arrives only to excitatory neurons, networks produce a wider range of output spiking responses in the presence of feedforward inhibition. PMID:29682603

Blind Deconvolution for Distributed Parameter Systems with Unbounded Input and Output and Determining Blood Alcohol Concentration from Transdermal Biosensor Data.

PubMed

Rosen, I G; Luczak, Susan E; Weiss, Jordan

2014-03-15

We develop a blind deconvolution scheme for input-output systems described by distributed parameter systems with boundary input and output. An abstract functional analytic theory based on results for the linear quadratic control of infinite dimensional systems with unbounded input and output operators is presented. The blind deconvolution problem is then reformulated as a series of constrained linear and nonlinear optimization problems involving infinite dimensional dynamical systems. A finite dimensional approximation and convergence theory is developed. The theory is applied to the problem of estimating blood or breath alcohol concentration (respectively, BAC or BrAC) from biosensor-measured transdermal alcohol concentration (TAC) in the field. A distributed parameter model with boundary input and output is proposed for the transdermal transport of ethanol from the blood through the skin to the sensor. The problem of estimating BAC or BrAC from the TAC data is formulated as a blind deconvolution problem. A scheme to identify distinct drinking episodes in TAC data based on a Hodrick Prescott filter is discussed. Numerical results involving actual patient data are presented.

Networked iterative learning control design for discrete-time systems with stochastic communication delay in input and output channels

NASA Astrophysics Data System (ADS)

Liu, Jian; Ruan, Xiaoe

2017-07-01

This paper develops two kinds of derivative-type networked iterative learning control (NILC) schemes for repetitive discrete-time systems with stochastic communication delay occurred in input and output channels and modelled as 0-1 Bernoulli-type stochastic variable. In the two schemes, the delayed signal of the current control input is replaced by the synchronous input utilised at the previous iteration, whilst for the delayed signal of the system output the one scheme substitutes it by the synchronous predetermined desired trajectory and the other takes it by the synchronous output at the previous operation, respectively. In virtue of the mathematical expectation, the tracking performance is analysed which exhibits that for both the linear time-invariant and nonlinear affine systems the two kinds of NILCs are convergent under the assumptions that the probabilities of communication delays are adequately constrained and the product of the input-output coupling matrices is full-column rank. Last, two illustrative examples are presented to demonstrate the effectiveness and validity of the proposed NILC schemes.

Strange nonchaotic attractors for computation

NASA Astrophysics Data System (ADS)

Sathish Aravindh, M.; Venkatesan, A.; Lakshmanan, M.

2018-05-01

We investigate the response of quasiperiodically driven nonlinear systems exhibiting strange nonchaotic attractors (SNAs) to deterministic input signals. We show that if one uses two square waves in an aperiodic manner as input to a quasiperiodically driven double-well Duffing oscillator system, the response of the system can produce logical output controlled by such a forcing. Changing the threshold or biasing of the system changes the output to another logic operation and memory latch. The interplay of nonlinearity and quasiperiodic forcing yields logical behavior, and the emergent outcome of such a system is a logic gate. It is further shown that the logical behaviors persist even for an experimental noise floor. Thus the SNA turns out to be an efficient tool for computation.

Magnetic current sensor

NASA Technical Reports Server (NTRS)

Black, Jr., William C. (Inventor); Hermann, Theodore M. (Inventor)

1998-01-01

A current determiner having an output at which representations of input currents are provided having an input conductor for the input current and a current sensor supported on a substrate electrically isolated from one another but with the sensor positioned in the magnetic fields arising about the input conductor due to any input currents. The sensor extends along the substrate in a direction primarily perpendicular to the extent of the input conductor and is formed of at least a pair of thin-film ferromagnetic layers separated by a non-magnetic conductive layer. The sensor can be electrically connected to a electronic circuitry formed in the substrate including a nonlinearity adaptation circuit to provide representations of the input currents of increased accuracy despite nonlinearities in the current sensor, and can include further current sensors in bridge circuits.

A first approach to the distortion analysis of nonlinear analog circuits utilizing X-parameters

NASA Astrophysics Data System (ADS)

Weber, H.; Widemann, C.; Mathis, W.

2013-07-01

In this contribution a first approach to the distortion analysis of nonlinear 2-port-networks with X-parameters1 is presented. The X-parameters introduced by Verspecht and Root (2006) offer the possibility to describe nonlinear microwave 2-port-networks under large signal conditions. On the basis of X-parameter measurements with a nonlinear network analyzer (NVNA) behavioral models can be extracted for the networks. These models can be used to consider the nonlinear behavior during the design process of microwave circuits. The idea of the present work is to extract the behavioral models in order to describe the influence of interfering signals on the output behavior of the nonlinear circuits. Hereby, a simulator is used instead of a NVNA to extract the X-parameters. Assuming that the interfering signals are relatively small compared to the nominal input signal, the output signal can be described as a superposition of the effects of each input signal. In order to determine the functional correlation between the scattering variables, a polynomial dependency is assumed. The required datasets for the approximation of the describing functions are simulated by a directional coupler model in Cadence Design Framework. The polynomial coefficients are obtained by a least-square method. The resulting describing functions can be used to predict the system's behavior under certain conditions as well as the effects of the interfering signal on the output signal. 1 X-parameter is a registered trademark of Agilent Technologies, Inc.

A Neural Network Design for the Estimation of Nonlinear Behavior of a Magnetically-Excited Piezoelectric Harvester

NASA Astrophysics Data System (ADS)

Çelik, Emre; Uzun, Yunus; Kurt, Erol; Öztürk, Nihat; Topaloğlu, Nurettin

2018-01-01

An application of an artificial neural network (ANN) has been implemented in this article to model the nonlinear relationship of the harvested electrical power of a recently developed piezoelectric pendulum with respect to its resistive load R L and magnetic excitation frequency f. Prediction of harvested power for a wide range is a difficult task, because it increases dramatically when f gets closer to the natural frequency f 0 of the system. The neural model of the concerned system is designed upon the basis of a standard multi-layer network with a back propagation learning algorithm. Input data, termed input patterns, to present to the network and the respective output data, termed output patterns, describing desired network output that are carefully collected from the experiment under several conditions in order to train the developed network accurately. Results have indicated that the designed ANN is an effective means for predicting the harvested power of the piezoelectric harvester as functions of R L and f with a root mean square error of 6.65 × 10-3 for training and 1.40 for different test conditions. Using the proposed approach, the harvested power can be estimated reasonably without tackling the difficulty of experimental studies and complexity of analytical formulas representing the concerned system.

Optimal Control-Based Adaptive NN Design for a Class of Nonlinear Discrete-Time Block-Triangular Systems.

PubMed

Liu, Yan-Jun; Tong, Shaocheng

2016-11-01

In this paper, we propose an optimal control scheme-based adaptive neural network design for a class of unknown nonlinear discrete-time systems. The controlled systems are in a block-triangular multi-input-multi-output pure-feedback structure, i.e., there are both state and input couplings and nonaffine functions to be included in every equation of each subsystem. The design objective is to provide a control scheme, which not only guarantees the stability of the systems, but also achieves optimal control performance. The main contribution of this paper is that it is for the first time to achieve the optimal performance for such a class of systems. Owing to the interactions among subsystems, making an optimal control signal is a difficult task. The design ideas are that: 1) the systems are transformed into an output predictor form; 2) for the output predictor, the ideal control signal and the strategic utility function can be approximated by using an action network and a critic network, respectively; and 3) an optimal control signal is constructed with the weight update rules to be designed based on a gradient descent method. The stability of the systems can be proved based on the difference Lyapunov method. Finally, a numerical simulation is given to illustrate the performance of the proposed scheme.

Enhanced Sensitivity to Rapid Input Fluctuations by Nonlinear Threshold Dynamics in Neocortical Pyramidal Neurons

PubMed Central

Mensi, Skander; Hagens, Olivier; Gerstner, Wulfram; Pozzorini, Christian

2016-01-01

The way in which single neurons transform input into output spike trains has fundamental consequences for network coding. Theories and modeling studies based on standard Integrate-and-Fire models implicitly assume that, in response to increasingly strong inputs, neurons modify their coding strategy by progressively reducing their selective sensitivity to rapid input fluctuations. Combining mathematical modeling with in vitro experiments, we demonstrate that, in L5 pyramidal neurons, the firing threshold dynamics adaptively adjust the effective timescale of somatic integration in order to preserve sensitivity to rapid signals over a broad range of input statistics. For that, a new Generalized Integrate-and-Fire model featuring nonlinear firing threshold dynamics and conductance-based adaptation is introduced that outperforms state-of-the-art neuron models in predicting the spiking activity of neurons responding to a variety of in vivo-like fluctuating currents. Our model allows for efficient parameter extraction and can be analytically mapped to a Generalized Linear Model in which both the input filter—describing somatic integration—and the spike-history filter—accounting for spike-frequency adaptation—dynamically adapt to the input statistics, as experimentally observed. Overall, our results provide new insights on the computational role of different biophysical processes known to underlie adaptive coding in single neurons and support previous theoretical findings indicating that the nonlinear dynamics of the firing threshold due to Na+-channel inactivation regulate the sensitivity to rapid input fluctuations. PMID:26907675

Stochastic modular analysis for gene circuits: interplay among retroactivity, nonlinearity, and stochasticity.

PubMed

Kim, Kyung Hyuk; Sauro, Herbert M

2015-01-01

This chapter introduces a computational analysis method for analyzing gene circuit dynamics in terms of modules while taking into account stochasticity, system nonlinearity, and retroactivity. (1) ANALOG ELECTRICAL CIRCUIT REPRESENTATION FOR GENE CIRCUITS: A connection between two gene circuit components is often mediated by a transcription factor (TF) and the connection signal is described by the TF concentration. The TF is sequestered to its specific binding site (promoter region) and regulates downstream transcription. This sequestration has been known to affect the dynamics of the TF by increasing its response time. The downstream effect-retroactivity-has been shown to be explicitly described in an electrical circuit representation, as an input capacitance increase. We provide a brief review on this topic. (2) MODULAR DESCRIPTION OF NOISE PROPAGATION: Gene circuit signals are noisy due to the random nature of biological reactions. The noisy fluctuations in TF concentrations affect downstream regulation. Thus, noise can propagate throughout the connected system components. This can cause different circuit components to behave in a statistically dependent manner, hampering a modular analysis. Here, we show that the modular analysis is still possible at the linear noise approximation level. (3) NOISE EFFECT ON MODULE INPUT-OUTPUT RESPONSE: We investigate how to deal with a module input-output response and its noise dependency. Noise-induced phenotypes are described as an interplay between system nonlinearity and signal noise. Lastly, we provide the comprehensive approach incorporating the above three analysis methods, which we call "stochastic modular analysis." This method can provide an analysis framework for gene circuit dynamics when the nontrivial effects of retroactivity, stochasticity, and nonlinearity need to be taken into account.

Nonlinear dynamics of the magnetosphere and space weather

NASA Technical Reports Server (NTRS)

Sharma, A. Surjalal

1996-01-01

The solar wind-magnetosphere system exhibits coherence on the global scale and such behavior can arise from nonlinearity on the dynamics. The observational time series data were used together with phase space reconstruction techniques to analyze the magnetospheric dynamics. Analysis of the solar wind, auroral electrojet and Dst indices showed low dimensionality of the dynamics and accurate prediction can be made with an input/output model. The predictability of the magnetosphere in spite of the apparent complexity arises from its dynamical synchronism with the solar wind. The electrodynamic coupling between different regions of the magnetosphere yields its coherent, low dimensional behavior. The data from multiple satellites and ground stations can be used to develop a spatio-temporal model that identifies the coupling between different regions. These nonlinear dynamical models provide space weather forecasting capabilities.

High amplitude nonlinear acoustic wave driven flow fields in cylindrical and conical resonators.

PubMed

Antao, Dion Savio; Farouk, Bakhtier

2013-08-01

A high fidelity computational fluid dynamic model is used to simulate the flow, pressure, and density fields generated in a cylindrical and a conical resonator by a vibrating end wall/piston producing high-amplitude standing waves. The waves in the conical resonator are found to be shock-less and can generate peak acoustic overpressures that exceed the initial undisturbed pressure by two to three times. A cylindrical (consonant) acoustic resonator has limitations to the output response observed at one end when the opposite end is acoustically excited. In the conical geometry (dissonant acoustic resonator) the linear acoustic input is converted to high energy un-shocked nonlinear acoustic output. The model is validated using past numerical results of standing waves in cylindrical resonators. The nonlinear nature of the harmonic response in the conical resonator system is further investigated for two different working fluids (carbon dioxide and argon) operating at various values of piston amplitude. The high amplitude nonlinear oscillations observed in the conical resonator can potentially enhance the performance of pulse tube thermoacoustic refrigerators and these conical resonators can be used as efficient mixers.

Peak expiratory flow profiles delivered by pump systems. Limitations due to wave action.

PubMed

Miller, M R; Jones, B; Xu, Y; Pedersen, O F; Quanjer, P H

2000-06-01

Pump systems are currently used to test the performance of both spirometers and peak expiratory flow (PEF) meters, but for certain flow profiles the input signal (i.e., requested profile) and the output profile can differ. We developed a mathematical model of wave action within a pump and compared the recorded flow profiles with both the input profiles and the output predicted by the model. Three American Thoracic Society (ATS) flow profiles and four artificial flow-versus-time profiles were delivered by a pump, first to a pneumotachograph (PT) on its own, then to the PT with a 32-cm upstream extension tube (which would favor wave action), and lastly with the PT in series with and immediately downstream to a mini-Wright peak flow meter. With the PT on its own, recorded flow for the seven profiles was 2.4 +/- 1.9% (mean +/- SD) higher than the pump's input flow, and similarly was 2.3 +/- 2.3% higher than the pump's output flow as predicted by the model. With the extension tube in place, the recorded flow was 6.6 +/- 6.4% higher than the input flow (range: 0.1 to 18.4%), but was only 1.2 +/- 2.5% higher than the output flow predicted by the model (range: -0.8 to 5.2%). With the mini-Wright meter in series, the flow recorded by the PT was on average 6.1 +/- 9.1% below the input flow (range: -23.8 to 2. 5%), but was only 0.6 +/- 3.3% above the pump's output flow predicted by the model (range: -5.5 to 3.9%). The mini-Wright meter's reading (corrected for its nonlinearity) was on average 1.3 +/- 3.6% below the model's predicted output flow (range: -9.0 to 1. 5%). The mini-Wright meter would be deemed outside ATS limits for accuracy for three of the seven profiles when compared with the pump's input PEF, but this would be true for only one profile when compared with the pump's output PEF as predicted by the model. Our study shows that the output flow from pump systems can differ from the input waveform depending on the operating configuration. This effect can be predicted with reasonable accuracy using a model based on nonsteady flow analysis that takes account of pressure wave reflections within pump systems.

Non-reciprocal wave propagation in one-dimensional nonlinear periodic structures

NASA Astrophysics Data System (ADS)

Luo, Benbiao; Gao, Sha; Liu, Jiehui; Mao, Yiwei; Li, Yifeng; Liu, Xiaozhou

2018-01-01

We study a one-dimensional nonlinear periodic structure which contains two different spring stiffness and an identical mass in each period. The linear dispersion relationship we obtain indicates that our periodic structure has obvious advantages compared to other kinds of periodic structures (i.e. those with the same spring stiffness but two different mass), including its increased flexibility for manipulating the band gap. Theoretically, the optical cutoff frequency remains unchanged while the acoustic cutoff frequency shifts to a lower or higher frequency. A numerical simulation verifies the dispersion relationship and the effect of the amplitude-dependent signal filter. Based upon this, we design a device which contains both a linear periodic structure and a nonlinear periodic structure. When incident waves with the same, large amplitude pass through it from opposite directions, the output amplitude of the forward input is one order magnitude larger than that of the reverse input. Our devised, non-reciprocal device can potentially act as an acoustic diode (AD) without an electrical circuit and frequency shifting. Our result represents a significant step forwards in the research of non-reciprocal wave manipulation.

Adaptive neural control of MIMO nonlinear systems with a block-triangular pure-feedback control structure.

PubMed

Chen, Zhenfeng; Ge, Shuzhi Sam; Zhang, Yun; Li, Yanan

2014-11-01

This paper presents adaptive neural tracking control for a class of uncertain multiinput-multioutput (MIMO) nonlinear systems in block-triangular form. All subsystems within these MIMO nonlinear systems are of completely nonaffine pure-feedback form and allowed to have different orders. To deal with the nonaffine appearance of the control variables, the mean value theorem is employed to transform the systems into a block-triangular strict-feedback form with control coefficients being couplings among various inputs and outputs. A systematic procedure is proposed for the design of a new singularity-free adaptive neural tracking control strategy. Such a design procedure can remove the couplings among subsystems and hence avoids the possible circular control construction problem. As a consequence, all the signals in the closed-loop system are guaranteed to be semiglobally uniformly ultimately bounded. Moreover, the outputs of the systems are ensured to converge to a small neighborhood of the desired trajectories. Simulation studies verify the theoretical findings revealed in this paper.

Photonic crystal based 1-bit full-adder optical circuit by using ring resonators in a nonlinear structure

NASA Astrophysics Data System (ADS)

Alipour-Banaei, Hamed; Seif-Dargahi, Hamed

2017-05-01

In this paper we proposed a novel design for realizing all optical 1*bit full-adder based on photonic crystals. The proposed structure was realized by cascading two optical 1-bit half-adders. The final structure is consisted of eight optical waveguides and two nonlinear resonant rings, created inside rod type two dimensional photonic crystal with square lattice. The structure has ;X;, ;Y; and ;Z; as input and ;SUM; and ;CARRY; as output ports. The performance and functionality of the proposed structure was validated by means of finite difference time domain method.

NL(q) Theory: A Neural Control Framework with Global Asymptotic Stability Criteria.

PubMed

Vandewalle, Joos; De Moor, Bart L.R.; Suykens, Johan A.K.

1997-06-01

In this paper a framework for model-based neural control design is presented, consisting of nonlinear state space models and controllers, parametrized by multilayer feedforward neural networks. The models and closed-loop systems are transformed into so-called NL(q) system form. NL(q) systems represent a large class of nonlinear dynamical systems consisting of q layers with alternating linear and static nonlinear operators that satisfy a sector condition. For such NL(q)s sufficient conditions for global asymptotic stability, input/output stability (dissipativity with finite L(2)-gain) and robust stability and performance are presented. The stability criteria are expressed as linear matrix inequalities. In the analysis problem it is shown how stability of a given controller can be checked. In the synthesis problem two methods for neural control design are discussed. In the first method Narendra's dynamic backpropagation for tracking on a set of specific reference inputs is modified with an NL(q) stability constraint in order to ensure, e.g., closed-loop stability. In a second method control design is done without tracking on specific reference inputs, but based on the input/output stability criteria itself, within a standard plant framework as this is done, for example, in H( infinity ) control theory and &mgr; theory. Copyright 1997 Elsevier Science Ltd.

Chaotic behaviors of operational amplifiers.

PubMed

Yim, Geo-Su; Ryu, Jung-Wan; Park, Young-Jai; Rim, Sunghwan; Lee, Soo-Young; Kye, Won-Ho; Kim, Chil-Min

2004-04-01

We investigate nonlinear dynamical behaviors of operational amplifiers. When the output terminal of an operational amplifier is connected to the inverting input terminal, the circuit exhibits period-doubling bifurcation, chaos, and periodic windows, depending on the voltages of the positive and the negative power supplies. We study these nonlinear dynamical characteristics of this electronic circuit experimentally.

Nonlinear system identification of smart structures under high impact loads

NASA Astrophysics Data System (ADS)

Sarp Arsava, Kemal; Kim, Yeesock; El-Korchi, Tahar; Park, Hyo Seon

2013-05-01

The main purpose of this paper is to develop numerical models for the prediction and analysis of the highly nonlinear behavior of integrated structure control systems subjected to high impact loading. A time-delayed adaptive neuro-fuzzy inference system (TANFIS) is proposed for modeling of the complex nonlinear behavior of smart structures equipped with magnetorheological (MR) dampers under high impact forces. Experimental studies are performed to generate sets of input and output data for training and validation of the TANFIS models. The high impact load and current signals are used as the input disturbance and control signals while the displacement and acceleration responses from the structure-MR damper system are used as the output signals. The benchmark adaptive neuro-fuzzy inference system (ANFIS) is used as a baseline. Comparisons of the trained TANFIS models with experimental results demonstrate that the TANFIS modeling framework is an effective way to capture nonlinear behavior of integrated structure-MR damper systems under high impact loading. In addition, the performance of the TANFIS model is much better than that of ANFIS in both the training and the validation processes.

A novel auto-tuning PID control mechanism for nonlinear systems.

PubMed

Cetin, Meric; Iplikci, Serdar

2015-09-01

In this paper, a novel Runge-Kutta (RK) discretization-based model-predictive auto-tuning proportional-integral-derivative controller (RK-PID) is introduced for the control of continuous-time nonlinear systems. The parameters of the PID controller are tuned using RK model of the system through prediction error-square minimization where the predicted information of tracking error provides an enhanced tuning of the parameters. Based on the model-predictive control (MPC) approach, the proposed mechanism provides necessary PID parameter adaptations while generating additive correction terms to assist the initially inadequate PID controller. Efficiency of the proposed mechanism has been tested on two experimental real-time systems: an unstable single-input single-output (SISO) nonlinear magnetic-levitation system and a nonlinear multi-input multi-output (MIMO) liquid-level system. RK-PID has been compared to standard PID, standard nonlinear MPC (NMPC), RK-MPC and conventional sliding-mode control (SMC) methods in terms of control performance, robustness, computational complexity and design issue. The proposed mechanism exhibits acceptable tuning and control performance with very small steady-state tracking errors, and provides very short settling time for parameter convergence. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Principal Dynamic Mode Analysis of the Hodgkin–Huxley Equations

PubMed Central

Eikenberry, Steffen E.; Marmarelis, Vasilis Z.

2015-01-01

We develop an autoregressive model framework based on the concept of Principal Dynamic Modes (PDMs) for the process of action potential (AP) generation in the excitable neuronal membrane described by the Hodgkin–Huxley (H–H) equations. The model's exogenous input is injected current, and whenever the membrane potential output exceeds a specified threshold, it is fed back as a second input. The PDMs are estimated from the previously developed Nonlinear Autoregressive Volterra (NARV) model, and represent an efficient functional basis for Volterra kernel expansion. The PDM-based model admits a modular representation, consisting of the forward and feedback PDM bases as linear filterbanks for the exogenous and autoregressive inputs, respectively, whose outputs are then fed to a static nonlinearity composed of polynomials operating on the PDM outputs and cross-terms of pair-products of PDM outputs. A two-step procedure for model reduction is performed: first, influential subsets of the forward and feedback PDM bases are identified and selected as the reduced PDM bases. Second, the terms of the static nonlinearity are pruned. The first step reduces model complexity from a total of 65 coefficients to 27, while the second further reduces the model coefficients to only eight. It is demonstrated that the performance cost of model reduction in terms of out-of-sample prediction accuracy is minimal. Unlike the full model, the eight coefficient pruned model can be easily visualized to reveal the essential system components, and thus the data-derived PDM model can yield insight into the underlying system structure and function. PMID:25630480

Sensitivity analysis and nonlinearity assessment of steam cracking furnace process

NASA Astrophysics Data System (ADS)

Rosli, M. N.; Sudibyo, Aziz, N.

2017-11-01

In this paper, sensitivity analysis and nonlinearity assessment of cracking furnace process are presented. For the sensitivity analysis, the fractional factorial design method is employed as a method to analyze the effect of input parameters, which consist of four manipulated variables and two disturbance variables, to the output variables and to identify the interaction between each parameter. The result of the factorial design method is used as a screening method to reduce the number of parameters, and subsequently, reducing the complexity of the model. It shows that out of six input parameters, four parameters are significant. After the screening is completed, step test is performed on the significant input parameters to assess the degree of nonlinearity of the system. The result shows that the system is highly nonlinear with respect to changes in an air-to-fuel ratio (AFR) and feed composition.

Smooth function approximation using neural networks.

PubMed

Ferrari, Silvia; Stengel, Robert F

2005-01-01

An algebraic approach for representing multidimensional nonlinear functions by feedforward neural networks is presented. In this paper, the approach is implemented for the approximation of smooth batch data containing the function's input, output, and possibly, gradient information. The training set is associated to the network adjustable parameters by nonlinear weight equations. The cascade structure of these equations reveals that they can be treated as sets of linear systems. Hence, the training process and the network approximation properties can be investigated via linear algebra. Four algorithms are developed to achieve exact or approximate matching of input-output and/or gradient-based training sets. Their application to the design of forward and feedback neurocontrollers shows that algebraic training is characterized by faster execution speeds and better generalization properties than contemporary optimization techniques.

The Vestibular System Implements a Linear–Nonlinear Transformation In Order to Encode Self-Motion

PubMed Central

Massot, Corentin; Schneider, Adam D.; Chacron, Maurice J.; Cullen, Kathleen E.

2012-01-01

Although it is well established that the neural code representing the world changes at each stage of a sensory pathway, the transformations that mediate these changes are not well understood. Here we show that self-motion (i.e. vestibular) sensory information encoded by VIIIth nerve afferents is integrated nonlinearly by post-synaptic central vestibular neurons. This response nonlinearity was characterized by a strong (∼50%) attenuation in neuronal sensitivity to low frequency stimuli when presented concurrently with high frequency stimuli. Using computational methods, we further demonstrate that a static boosting nonlinearity in the input-output relationship of central vestibular neurons accounts for this unexpected result. Specifically, when low and high frequency stimuli are presented concurrently, this boosting nonlinearity causes an intensity-dependent bias in the output firing rate, thereby attenuating neuronal sensitivities. We suggest that nonlinear integration of afferent input extends the coding range of central vestibular neurons and enables them to better extract the high frequency features of self-motion when embedded with low frequency motion during natural movements. These findings challenge the traditional notion that the vestibular system uses a linear rate code to transmit information and have important consequences for understanding how the representation of sensory information changes across sensory pathways. PMID:22911113

Solid-state repetitive generator with a gyromagnetic nonlinear transmission line operating as a peak power amplifier

NASA Astrophysics Data System (ADS)

Gusev, A. I.; Pedos, M. S.; Rukin, S. N.; Timoshenkov, S. P.

2017-07-01

In this work, experiments were made in which gyromagnetic nonlinear transmission line (NLTL) operates as a peak power amplifier of the input pulse. At such an operating regime, the duration of the input pulse is close to the period of generated oscillations, and the main part of the input pulse energy is transmitted only to the first peak of the oscillations. Power amplification is achieved due to the voltage amplitude of the first peak across the NLTL output exceeding the voltage amplitude of the input pulse. In the experiments, the input pulse with an amplitude of 500 kV and a half-height pulse duration of 7 ns is applied to the NLTL with a natural oscillation frequency of ˜300 MHz. At the output of the NLTL in 40 Ω coaxial transmission line, the pulse amplitude is increased to 740 kV and the pulse duration is reduced to ˜2 ns, which correspond to power amplification of the input pulse from ˜6 to ˜13 GW. As a source of input pulses, a solid-state semiconductor opening switch generator was used, which allowed carrying out experiments at pulse repetition frequency up to 1 kHz in the burst mode of operation.

A novel method for predicting the power outputs of wave energy converters

NASA Astrophysics Data System (ADS)

Wang, Yingguang

2018-03-01

This paper focuses on realistically predicting the power outputs of wave energy converters operating in shallow water nonlinear waves. A heaving two-body point absorber is utilized as a specific calculation example, and the generated power of the point absorber has been predicted by using a novel method (a nonlinear simulation method) that incorporates a second order random wave model into a nonlinear dynamic filter. It is demonstrated that the second order random wave model in this article can be utilized to generate irregular waves with realistic crest-trough asymmetries, and consequently, more accurate generated power can be predicted by subsequently solving the nonlinear dynamic filter equation with the nonlinearly simulated second order waves as inputs. The research findings demonstrate that the novel nonlinear simulation method in this article can be utilized as a robust tool for ocean engineers in their design, analysis and optimization of wave energy converters.

Identification of Linear and Nonlinear Sensory Processing Circuits from Spiking Neuron Data.

PubMed

Florescu, Dorian; Coca, Daniel

2018-03-01

Inferring mathematical models of sensory processing systems directly from input-output observations, while making the fewest assumptions about the model equations and the types of measurements available, is still a major issue in computational neuroscience. This letter introduces two new approaches for identifying sensory circuit models consisting of linear and nonlinear filters in series with spiking neuron models, based only on the sampled analog input to the filter and the recorded spike train output of the spiking neuron. For an ideal integrate-and-fire neuron model, the first algorithm can identify the spiking neuron parameters as well as the structure and parameters of an arbitrary nonlinear filter connected to it. The second algorithm can identify the parameters of the more general leaky integrate-and-fire spiking neuron model, as well as the parameters of an arbitrary linear filter connected to it. Numerical studies involving simulated and real experimental recordings are used to demonstrate the applicability and evaluate the performance of the proposed algorithms.

Adaptive control using neural networks and approximate models.

PubMed

Narendra, K S; Mukhopadhyay, S

1997-01-01

The NARMA model is an exact representation of the input-output behavior of finite-dimensional nonlinear discrete-time dynamical systems in a neighborhood of the equilibrium state. However, it is not convenient for purposes of adaptive control using neural networks due to its nonlinear dependence on the control input. Hence, quite often, approximate methods are used for realizing the neural controllers to overcome computational complexity. In this paper, we introduce two classes of models which are approximations to the NARMA model, and which are linear in the control input. The latter fact substantially simplifies both the theoretical analysis as well as the practical implementation of the controller. Extensive simulation studies have shown that the neural controllers designed using the proposed approximate models perform very well, and in many cases even better than an approximate controller designed using the exact NARMA model. In view of their mathematical tractability as well as their success in simulation studies, a case is made in this paper that such approximate input-output models warrant a detailed study in their own right.

Linear and quadratic models of point process systems: contributions of patterned input to output.

PubMed

Lindsay, K A; Rosenberg, J R

2012-08-01

In the 1880's Volterra characterised a nonlinear system using a functional series connecting continuous input and continuous output. Norbert Wiener, in the 1940's, circumvented problems associated with the application of Volterra series to physical problems by deriving from it a new series of terms that are mutually uncorrelated with respect to Gaussian processes. Subsequently, Brillinger, in the 1970's, introduced a point-process analogue of Volterra's series connecting point-process inputs to the instantaneous rate of point-process output. We derive here a new series from this analogue in which its terms are mutually uncorrelated with respect to Poisson processes. This new series expresses how patterned input in a spike train, represented by third-order cross-cumulants, is converted into the instantaneous rate of an output point-process. Given experimental records of suitable duration, the contribution of arbitrary patterned input to an output process can, in principle, be determined. Solutions for linear and quadratic point-process models with one and two inputs and a single output are investigated. Our theoretical results are applied to isolated muscle spindle data in which the spike trains from the primary and secondary endings from the same muscle spindle are recorded in response to stimulation of one and then two static fusimotor axons in the absence and presence of a random length change imposed on the parent muscle. For a fixed mean rate of input spikes, the analysis of the experimental data makes explicit which patterns of two input spikes contribute to an output spike. Copyright © 2012 Elsevier Ltd. All rights reserved.

A comprehensive inversion approach for feedforward compensation of piezoactuator system at high frequency

NASA Astrophysics Data System (ADS)

Tian, Lizhi; Xiong, Zhenhua; Wu, Jianhua; Ding, Han

2016-09-01

Motion control of the piezoactuator system over broadband frequencies is limited due to its inherent hysteresis and system dynamics. One of the suggested ways is to use feedforward controller to linearize the input-output relationship of the piezoactuator system. Although there have been many feedforward approaches, it is still a challenge to develop feedforward controller for the piezoactuator system at high frequency. Hence, this paper presents a comprehensive inversion approach in consideration of the coupling of hysteresis and dynamics. In this work, the influence of dynamics compensation on the input-output relationship of the piezoactuator system is investigated first. With system dynamics compensation, the input-output relationship of the piezoactuator system will be further represented as rate-dependent nonlinearity due to the inevitable dynamics compensation error, especially at high frequency. Base on this result, the feedforward controller composed by a cascade of linear dynamics inversion and rate-dependent nonlinearity inversion is developed. Then, the system identification of the comprehensive inversion approach is proposed. Finally, experimental results show that the proposed approach can improve the performance on tracking of both periodic and non-periodic trajectories at medium and high frequency compared with the conventional feedforward approaches.

Point-Wise Phase Matching for Nonlinear Frequency Generation in Dielectric Resonators

NASA Technical Reports Server (NTRS)

Yu, Nan (Inventor); Strekalov, Dmitry V. (Inventor); Lin, Guoping (Inventor)

2016-01-01

An optical resonator fabricated from a uniaxial birefringent crystal, such as beta barium borate. The crystal is cut with the optical axis not perpendicular to a face of the cut crystal. In some cases the optical axis lies in the plane of the cut crystal face. An incident (input) electromagnetic signal (which can range from the infrared through the visible to the ultraviolet) is applied to the resonator. An output signal is recovered which has a frequency that is an integer multiple of the frequency of the input signal. In some cases a prism is used to evanescently couple the input and the output signals to the resonator.

Adaptive neural network output feedback control for stochastic nonlinear systems with unknown dead-zone and unmodeled dynamics.

PubMed

Tong, Shaocheng; Wang, Tong; Li, Yongming; Zhang, Huaguang

2014-06-01

This paper discusses the problem of adaptive neural network output feedback control for a class of stochastic nonlinear strict-feedback systems. The concerned systems have certain characteristics, such as unknown nonlinear uncertainties, unknown dead-zones, unmodeled dynamics and without the direct measurements of state variables. In this paper, the neural networks (NNs) are employed to approximate the unknown nonlinear uncertainties, and then by representing the dead-zone as a time-varying system with a bounded disturbance. An NN state observer is designed to estimate the unmeasured states. Based on both backstepping design technique and a stochastic small-gain theorem, a robust adaptive NN output feedback control scheme is developed. It is proved that all the variables involved in the closed-loop system are input-state-practically stable in probability, and also have robustness to the unmodeled dynamics. Meanwhile, the observer errors and the output of the system can be regulated to a small neighborhood of the origin by selecting appropriate design parameters. Simulation examples are also provided to illustrate the effectiveness of the proposed approach.

Assessing first-order emulator inference for physical parameters in nonlinear mechanistic models

USGS Publications Warehouse

Hooten, Mevin B.; Leeds, William B.; Fiechter, Jerome; Wikle, Christopher K.

2011-01-01

We present an approach for estimating physical parameters in nonlinear models that relies on an approximation to the mechanistic model itself for computational efficiency. The proposed methodology is validated and applied in two different modeling scenarios: (a) Simulation and (b) lower trophic level ocean ecosystem model. The approach we develop relies on the ability to predict right singular vectors (resulting from a decomposition of computer model experimental output) based on the computer model input and an experimental set of parameters. Critically, we model the right singular vectors in terms of the model parameters via a nonlinear statistical model. Specifically, we focus our attention on first-order models of these right singular vectors rather than the second-order (covariance) structure.

Spectral analysis for nonstationary and nonlinear systems: a discrete-time-model-based approach.

PubMed

He, Fei; Billings, Stephen A; Wei, Hua-Liang; Sarrigiannis, Ptolemaios G; Zhao, Yifan

2013-08-01

A new frequency-domain analysis framework for nonlinear time-varying systems is introduced based on parametric time-varying nonlinear autoregressive with exogenous input models. It is shown how the time-varying effects can be mapped to the generalized frequency response functions (FRFs) to track nonlinear features in frequency, such as intermodulation and energy transfer effects. A new mapping to the nonlinear output FRF is also introduced. A simulated example and the application to intracranial electroencephalogram data are used to illustrate the theoretical results.

Blind identification of nonlinear models with non-Gaussian inputs

NASA Astrophysics Data System (ADS)

Prakriya, Shankar; Pasupathy, Subbarayan; Hatzinakos, Dimitrios

1995-12-01

Some methods are proposed for the blind identification of finite-order discrete-time nonlinear models with non-Gaussian circular inputs. The nonlinear models consist of two finite memory linear time invariant (LTI) filters separated by a zero-memory nonlinearity (ZMNL) of the polynomial type (the LTI-ZMNL-LTI models). The linear subsystems are allowed to be of non-minimum phase (NMP). The methods base their estimates of the impulse responses on slices of the N plus 1th order polyspectra of the output sequence. It is shown that the identification of LTI-ZMNL systems requires only a 1-D moment or polyspectral slice. The coefficients of the ZMNL are not estimated, and need not be known. The order of the nonlinearity can, in theory, be estimated from the received signal. These methods possess several noise and interference suppression characteristics, and have applications in modeling nonlinearly amplified QAM/QPSK signals in digital satellite and microwave communications.

Output feedback control of a quadrotor UAV using neural networks.

PubMed

Dierks, Travis; Jagannathan, Sarangapani

2010-01-01

In this paper, a new nonlinear controller for a quadrotor unmanned aerial vehicle (UAV) is proposed using neural networks (NNs) and output feedback. The assumption on the availability of UAV dynamics is not always practical, especially in an outdoor environment. Therefore, in this work, an NN is introduced to learn the complete dynamics of the UAV online, including uncertain nonlinear terms like aerodynamic friction and blade flapping. Although a quadrotor UAV is underactuated, a novel NN virtual control input scheme is proposed which allows all six degrees of freedom (DOF) of the UAV to be controlled using only four control inputs. Furthermore, an NN observer is introduced to estimate the translational and angular velocities of the UAV, and an output feedback control law is developed in which only the position and the attitude of the UAV are considered measurable. It is shown using Lyapunov theory that the position, orientation, and velocity tracking errors, the virtual control and observer estimation errors, and the NN weight estimation errors for each NN are all semiglobally uniformly ultimately bounded (SGUUB) in the presence of bounded disturbances and NN functional reconstruction errors while simultaneously relaxing the separation principle. The effectiveness of proposed output feedback control scheme is then demonstrated in the presence of unknown nonlinear dynamics and disturbances, and simulation results are included to demonstrate the theoretical conjecture.

Sobol' sensitivity analysis for stressor impacts on honeybee ...

EPA Pesticide Factsheets

We employ Monte Carlo simulation and nonlinear sensitivity analysis techniques to describe the dynamics of a bee exposure model, VarroaPop. Daily simulations are performed of hive population trajectories, taking into account queen strength, foraging success, mite impacts, weather, colony resources, population structure, and other important variables. This allows us to test the effects of defined pesticide exposure scenarios versus controlled simulations that lack pesticide exposure. The daily resolution of the model also allows us to conditionally identify sensitivity metrics. We use the variancebased global decomposition sensitivity analysis method, Sobol’, to assess firstand secondorder parameter sensitivities within VarroaPop, allowing us to determine how variance in the output is attributed to each of the input variables across different exposure scenarios. Simulations with VarroaPop indicate queen strength, forager life span and pesticide toxicity parameters are consistent, critical inputs for colony dynamics. Further analysis also reveals that the relative importance of these parameters fluctuates throughout the simulation period according to the status of other inputs. Our preliminary results show that model variability is conditional and can be attributed to different parameters depending on different timescales. By using sensitivity analysis to assess model output and variability, calibrations of simulation models can be better informed to yield more

Estimation of Hidden State Variables of the Intracranial System Using Constrained Nonlinear Kalman Filters

PubMed Central

Nenov, Valeriy; Bergsneider, Marvin; Glenn, Thomas C.; Vespa, Paul; Martin, Neil

2007-01-01

Impeded by the rigid skull, assessment of physiological variables of the intracranial system is difficult. A hidden state estimation approach is used in the present work to facilitate the estimation of unobserved variables from available clinical measurements including intracranial pressure (ICP) and cerebral blood flow velocity (CBFV). The estimation algorithm is based on a modified nonlinear intracranial mathematical model, whose parameters are first identified in an offline stage using a nonlinear optimization paradigm. Following the offline stage, an online filtering process is performed using a nonlinear Kalman filter (KF)-like state estimator that is equipped with a new way of deriving the Kalman gain satisfying the physiological constraints on the state variables. The proposed method is then validated by comparing different state estimation methods and input/output (I/O) configurations using simulated data. It is also applied to a set of CBFV, ICP and arterial blood pressure (ABP) signal segments from brain injury patients. The results indicated that the proposed constrained nonlinear KF achieved the best performance among the evaluated state estimators and that the state estimator combined with the I/O configuration that has ICP as the measured output can potentially be used to estimate CBFV continuously. Finally, the state estimator combined with the I/O configuration that has both ICP and CBFV as outputs can potentially estimate the lumped cerebral arterial radii, which are not measurable in a typical clinical environment. PMID:17281533

Replica symmetric evaluation of the information transfer in a two-layer network in the presence of continuous and discrete stimuli.

PubMed

Del Prete, Valeria; Treves, Alessandro

2002-04-01

In a previous paper we have evaluated analytically the mutual information between the firing rates of N independent units and a set of multidimensional continuous and discrete stimuli, for a finite population size and in the limit of large noise. Here, we extend the analysis to the case of two interconnected populations, where input units activate output ones via Gaussian weights and a threshold linear transfer function. We evaluate the information carried by a population of M output units, again about continuous and discrete correlates. The mutual information is evaluated solving saddle-point equations under the assumption of replica symmetry, a method that, by taking into account only the term linear in N of the input information, is equivalent to assuming the noise to be large. Within this limitation, we analyze the dependence of the information on the ratio M/N, on the selectivity of the input units and on the level of the output noise. We show analytically, and confirm numerically, that in the limit of a linear transfer function and of a small ratio between output and input noise, the output information approaches asymptotically the information carried in input. Finally, we show that the information loss in output does not depend much on the structure of the stimulus, whether purely continuous, purely discrete or mixed, but only on the position of the threshold nonlinearity, and on the ratio between input and output noise.

Input-output-controlled nonlinear equation solvers

NASA Technical Reports Server (NTRS)

Padovan, Joseph

1988-01-01

To upgrade the efficiency and stability of the successive substitution (SS) and Newton-Raphson (NR) schemes, the concept of input-output-controlled solvers (IOCS) is introduced. By employing the formal properties of the constrained version of the SS and NR schemes, the IOCS algorithm can handle indefiniteness of the system Jacobian, can maintain iterate monotonicity, and provide for separate control of load incrementation and iterate excursions, as well as having other features. To illustrate the algorithmic properties, the results for several benchmark examples are presented. These define the associated numerical efficiency and stability of the IOCS.

Implementation of a digital evaluation platform to analyze bifurcation based nonlinear amplifiers

NASA Astrophysics Data System (ADS)

Feldkord, Sven; Reit, Marco; Mathis, Wolfgang

2016-09-01

Recently, nonlinear amplifiers based on the supercritical Andronov-Hopf bifurcation have become a focus of attention, especially in the modeling of the mammalian hearing organ. In general, to gain deeper insights in the input-output behavior, the analysis of bifurcation based amplifiers requires a flexible framework to exchange equations and adjust certain parameters. A DSP implementation is presented which is capable to analyze various amplifier systems. Amplifiers based on the Andronov-Hopf and Neimark-Sacker bifurcations are implemented and compared exemplarily. It is shown that the Neimark-Sacker system remarkably outperforms the Andronov-Hopf amplifier regarding the CPU usage. Nevertheless, both show a similar input-output behavior over a wide parameter range. Combined with an USB-based control interface connected to a PC, the digital framework provides a powerful instrument to analyze bifurcation based amplifiers.

Quasi-minimal active disturbance rejection control of MIMO perturbed linear systems based on differential neural networks and the attractive ellipsoid method.

PubMed

Salgado, Iván; Mera-Hernández, Manuel; Chairez, Isaac

2017-11-01

This study addresses the problem of designing an output-based controller to stabilize multi-input multi-output (MIMO) systems in the presence of parametric disturbances as well as uncertainties in the state model and output noise measurements. The controller design includes a linear state transformation which separates uncertainties matched to the control input and the unmatched ones. A differential neural network (DNN) observer produces a nonlinear approximation of the matched perturbation and the unknown states simultaneously in the transformed coordinates. This study proposes the use of the Attractive Ellipsoid Method (AEM) to optimize the gains of the controller and the gain observer in the DNN structure. As a consequence, the obtained control input minimizes the convergence zone for the estimation error. Moreover, the control design uses the estimated disturbance provided by the DNN to obtain a better performance in the stabilization task in comparison with a quasi-minimal output feedback controller based on a Luenberger observer and a sliding mode controller. Numerical results pointed out the advantages obtained by the nonlinear control based on the DNN observer. The first example deals with the stabilization of an academic linear MIMO perturbed system and the second example stabilizes the trajectories of a DC-motor into a predefined operation point. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

General model and control of an n rotor helicopter

NASA Astrophysics Data System (ADS)

Sidea, A. G.; Yding Brogaard, R.; Andersen, N. A.; Ravn, O.

2014-12-01

The purpose of this study was to create a dynamic, nonlinear mathematical model of a multirotor that would be valid for different numbers of rotors. Furthermore, a set of Single Input Single Output (SISO) controllers were implemented for attitude control. Both model and controllers were tested experimentally on a quadcopter. Using the combined model and controllers, simple system simulation and control is possible, by replacing the physical values for the individual systems.

NLCC controller for SEPIC-based micro-wind energy conversion system

NASA Astrophysics Data System (ADS)

Justin Nayagam, Brintha Jane; Sathi, Rama Reddy; Olimuthu, Divya

2017-04-01

The growth of the power industry is gaining greater momentum as the usage of the non-conventional energy sources that include fuel, solar, and wind energies, increases. Wind energy conversion systems (WECSs) are gaining more popularity and are expected to be able to control the power at the output. This paper describes the current control (CC), non-linear carrier charge control (NLCCC), and fuzzy logic control (FLC) applied to the single-ended primary inductor converter (SEPIC)-based WECS. The current controller has an inherent overcurrent protection with better line noise rejection. The pulses for the switch of the SEPIC are obtained by comparing the current flowing through it with the virtual current reference. FLC is also investigated for the micro-wind energy conversion system (μWECS), since it improves the damping characteristics of WECS over a wide range of operating points. This cannot attain the unity power factor rectification. In this paper, NLCCC is proposed for high-power factor rectifier-based SEPIC in continuous conduction mode (CCM) for μWECS. The proposed converter provides an output voltage with low input current ripple due to the presence of the inductor at the input side. By comparing the signal proportional to the integral of switch current with a periodic non-linear carrier wave, the duty ratio of the converter switch is determined for the NLCC controller. By selecting the shape of the periodic non-linear carrier wave the input-line current can be made to follow the input-line voltage. This work employs a parabolic carrier waveform generator. The output voltage is regulated for changes in the wind speed. The results obtained prove the effectiveness of the NLCC controller in improving the power factor.

Discrete time learning control in nonlinear systems

NASA Technical Reports Server (NTRS)

Longman, Richard W.; Chang, Chi-Kuang; Phan, Minh

1992-01-01

In this paper digital learning control methods are developed primarily for use in single-input, single-output nonlinear dynamic systems. Conditions for convergence of the basic form of learning control based on integral control concepts are given, and shown to be satisfied by a large class of nonlinear problems. It is shown that it is not the gross nonlinearities of the differential equations that matter in the convergence, but rather the much smaller nonlinearities that can manifest themselves during the short time interval of one sample time. New algorithms are developed that eliminate restrictions on the size of the learning gain, and on knowledge of the appropriate sign of the learning gain, for convergence to zero error in tracking a feasible desired output trajectory. It is shown that one of the new algorithms can give guaranteed convergence in the presence of actuator saturation constraints, and indicate when the requested trajectory is beyond the actuator capabilities.

On a Model of a Nonlinear Feedback System for River Flow Prediction

NASA Astrophysics Data System (ADS)

Ozaki, T.

1980-02-01

A nonlinear system with feedback is proposed as a dynamic model for the hydrological system, whose input is the rainfall and whose output is the discharge of river flow. Parameters and orders of the model are estimated using Akaike's information criterion. Its application to the prediction of daily discharges of Kanna River and Bird Creek is discussed.

A nonlinear control method based on ANFIS and multiple models for a class of SISO nonlinear systems and its application.

PubMed

Zhang, Yajun; Chai, Tianyou; Wang, Hong

2011-11-01

This paper presents a novel nonlinear control strategy for a class of uncertain single-input and single-output discrete-time nonlinear systems with unstable zero-dynamics. The proposed method combines adaptive-network-based fuzzy inference system (ANFIS) with multiple models, where a linear robust controller, an ANFIS-based nonlinear controller and a switching mechanism are integrated using multiple models technique. It has been shown that the linear controller can ensure the boundedness of the input and output signals and the nonlinear controller can improve the dynamic performance of the closed loop system. Moreover, it has also been shown that the use of the switching mechanism can simultaneously guarantee the closed loop stability and improve its performance. As a result, the controller has the following three outstanding features compared with existing control strategies. First, this method relaxes the assumption of commonly-used uniform boundedness on the unmodeled dynamics and thus enhances its applicability. Second, since ANFIS is used to estimate and compensate the effect caused by the unmodeled dynamics, the convergence rate of neural network learning has been increased. Third, a "one-to-one mapping" technique is adapted to guarantee the universal approximation property of ANFIS. The proposed controller is applied to a numerical example and a pulverizing process of an alumina sintering system, respectively, where its effectiveness has been justified.

Spacecraft nonlinear control

NASA Technical Reports Server (NTRS)

Sheen, Jyh-Jong; Bishop, Robert H.

1992-01-01

The feedback linearization technique is applied to the problem of spacecraft attitude control and momentum management with control moment gyros (CMGs). The feedback linearization consists of a coordinate transformation, which transforms the system to a companion form, and a nonlinear feedback control law to cancel the nonlinear dynamics resulting in a linear equivalent model. Pole placement techniques are then used to place the closed-loop poles. The coordinate transformation proposed here evolves from three output functions of relative degree four, three, and two, respectively. The nonlinear feedback control law is presented. Stability in a neighborhood of a controllable torque equilibrium attitude (TEA) is guaranteed and this fact is demonstrated by the simulation results. An investigation of the nonlinear control law shows that singularities exist in the state space outside the neighborhood of the controllable TEA. The nonlinear control law is simplified by a standard linearization technique and it is shown that the linearized nonlinear controller provides a natural way to select control gains for the multiple-input, multiple-output system. Simulation results using the linearized nonlinear controller show good performance relative to the nonlinear controller in the neighborhood of the TEA.

Data-based fault-tolerant control of high-speed trains with traction/braking notch nonlinearities and actuator failures.

PubMed

Song, Qi; Song, Yong-Duan

2011-12-01

This paper investigates the position and velocity tracking control problem of high-speed trains with multiple vehicles connected through couplers. A dynamic model reflecting nonlinear and elastic impacts between adjacent vehicles as well as traction/braking nonlinearities and actuation faults is derived. Neuroadaptive fault-tolerant control algorithms are developed to account for various factors such as input nonlinearities, actuator failures, and uncertain impacts of in-train forces in the system simultaneously. The resultant control scheme is essentially independent of system model and is primarily data-driven because with the appropriate input-output data, the proposed control algorithms are capable of automatically generating the intermediate control parameters, neuro-weights, and the compensation signals, literally producing the traction/braking force based upon input and response data only--the whole process does not require precise information on system model or system parameter, nor human intervention. The effectiveness of the proposed approach is also confirmed through numerical simulations.

Optimal nonlinear codes for the perception of natural colours.

PubMed

von der Twer, T; MacLeod, D I

2001-08-01

We discuss how visual nonlinearity can be optimized for the precise representation of environmental inputs. Such optimization leads to neural signals with a compressively nonlinear input-output function the gradient of which is matched to the cube root of the probability density function (PDF) of the environmental input values (and not to the PDF directly as in histogram equalization). Comparisons between theory and psychophysical and electrophysiological data are roughly consistent with the idea that parvocellular (P) cells are optimized for precision representation of colour: their contrast-response functions span a range appropriately matched to the environmental distribution of natural colours along each dimension of colour space. Thus P cell codes for colour may have been selected to minimize error in the perceptual estimation of stimulus parameters for natural colours. But magnocellular (M) cells have a much stronger than expected saturating nonlinearity; this supports the view that the function of M cells is mainly to detect boundaries rather than to specify contrast or lightness.

Robust decentralized hybrid adaptive output feedback fuzzy control for a class of large-scale MIMO nonlinear systems and its application to AHS.

PubMed

Huang, Yi-Shao; Liu, Wel-Ping; Wu, Min; Wang, Zheng-Wu

2014-09-01

This paper presents a novel observer-based decentralized hybrid adaptive fuzzy control scheme for a class of large-scale continuous-time multiple-input multiple-output (MIMO) uncertain nonlinear systems whose state variables are unmeasurable. The scheme integrates fuzzy logic systems, state observers, and strictly positive real conditions to deal with three issues in the control of a large-scale MIMO uncertain nonlinear system: algorithm design, controller singularity, and transient response. Then, the design of the hybrid adaptive fuzzy controller is extended to address a general large-scale uncertain nonlinear system. It is shown that the resultant closed-loop large-scale system keeps asymptotically stable and the tracking error converges to zero. The better characteristics of our scheme are demonstrated by simulations. Copyright © 2014. Published by Elsevier Ltd.

Multiscale Bayesian neural networks for soil water content estimation

NASA Astrophysics Data System (ADS)

Jana, Raghavendra B.; Mohanty, Binayak P.; Springer, Everett P.

2008-08-01

Artificial neural networks (ANN) have been used for some time now to estimate soil hydraulic parameters from other available or more easily measurable soil properties. However, most such uses of ANNs as pedotransfer functions (PTFs) have been at matching spatial scales (1:1) of inputs and outputs. This approach assumes that the outputs are only required at the same scale as the input data. Unfortunately, this is rarely true. Different hydrologic, hydroclimatic, and contaminant transport models require soil hydraulic parameter data at different spatial scales, depending upon their grid sizes. While conventional (deterministic) ANNs have been traditionally used in these studies, the use of Bayesian training of ANNs is a more recent development. In this paper, we develop a Bayesian framework to derive soil water retention function including its uncertainty at the point or local scale using PTFs trained with coarser-scale Soil Survey Geographic (SSURGO)-based soil data. The approach includes an ANN trained with Bayesian techniques as a PTF tool with training and validation data collected across spatial extents (scales) in two different regions in the United States. The two study areas include the Las Cruces Trench site in the Rio Grande basin of New Mexico, and the Southern Great Plains 1997 (SGP97) hydrology experimental region in Oklahoma. Each region-specific Bayesian ANN is trained using soil texture and bulk density data from the SSURGO database (scale 1:24,000), and predictions of the soil water contents at different pressure heads with point scale data (1:1) inputs are made. The resulting outputs are corrected for bias using both linear and nonlinear correction techniques. The results show good agreement between the soil water content values measured at the point scale and those predicted by the Bayesian ANN-based PTFs for both the study sites. Overall, Bayesian ANNs coupled with nonlinear bias correction are found to be very suitable tools for deriving soil hydraulic parameters at the local/fine scale from soil physical properties at coarser-scale and across different spatial extents. This approach could potentially be used for soil hydraulic properties estimation and downscaling.

A Reconfiguration Scheme for Accommodating Actuator Failures in Multi-Input, Multi-Output Flight Control Systems

NASA Technical Reports Server (NTRS)

Siwakosit, W.; Hess, R. A.; Bacon, Bart (Technical Monitor); Burken, John (Technical Monitor)

2000-01-01

A multi-input, multi-output reconfigurable flight control system design utilizing a robust controller and an adaptive filter is presented. The robust control design consists of a reduced-order, linear dynamic inversion controller with an outer-loop compensation matrix derived from Quantitative Feedback Theory (QFT). A principle feature of the scheme is placement of the adaptive filter in series with the QFT compensator thus exploiting the inherent robustness of the nominal flight control system in the presence of plant uncertainties. An example of the scheme is presented in a pilot-in-the-loop computer simulation using a simplified model of the lateral-directional dynamics of the NASA F18 High Angle of Attack Research Vehicle (HARV) that included nonlinear anti-wind up logic and actuator limitations. Prediction of handling qualities and pilot-induced oscillation tendencies in the presence of these nonlinearities is included in the example.

Automatic guidance control of an articulated all-wheel-steered vehicle

NASA Astrophysics Data System (ADS)

Kim, Young Chol; Yun, Kyong-Han; Min, Kyung-Deuk

2014-04-01

This paper presents automatic guidance control of a single-articulated all-wheel-steered vehicle being developed by the Korea Railroad Research Institute. The vehicle has an independent drive motor on each wheel except for the front axle. The guidance controller is designed so that the vehicle follows the given reference path within permissible lateral deviations. We use a three-input/three-output linearised model derived from the nonlinear dynamic model of the vehicle. For the purpose of simplifying the controller and making it tunable, we consider a decentralised control configuration. We first design a second-order decoupling compensator for the two-input/two-output system that is strongly coupled and then design a first-order controller for each decoupled feedback loop by using the characteristic ratio assignment method. The simulation results for the nonlinear dynamic model indicate that the proposed control configuration successfully achieves the design objectives.

Computer modeling of multiple-channel input signals and intermodulation losses caused by nonlinear traveling wave tube amplifiers

NASA Technical Reports Server (NTRS)

Stankiewicz, N.

1982-01-01

The multiple channel input signal to a soft limiter amplifier as a traveling wave tube is represented as a finite, linear sum of Gaussian functions in the frequency domain. Linear regression is used to fit the channel shapes to a least squares residual error. Distortions in output signal, namely intermodulation products, are produced by the nonlinear gain characteristic of the amplifier and constitute the principal noise analyzed in this study. The signal to noise ratios are calculated for various input powers from saturation to 10 dB below saturation for two specific distributions of channels. A criterion for the truncation of the series expansion of the nonlinear transfer characteristic is given. It is found that he signal to noise ratios are very sensitive to the coefficients used in this expansion. Improper or incorrect truncation of the series leads to ambiguous results in the signal to noise ratios.

Effect of shaping sensor data on pilot response

NASA Technical Reports Server (NTRS)

Bailey, Roger M.

1990-01-01

The pilot of a modern jet aircraft is subjected to varying workloads while being responsible for multiple, ongoing tasks. The ability to associate the pilot's responses with the task/situation, by modifying the way information is presented relative to the task, could provide a means of reducing workload. To examine the feasibility of this concept, a real time simulation study was undertaken to determine whether preprocessing of sensor data would affect pilot response. Results indicated that preprocessing could be an effective way to tailor the pilot's response to displayed data. The effects of three transformations or shaping functions were evaluated with respect to the pilot's ability to predict and detect out-of-tolerance conditions while monitoring an electronic engine display. Two nonlinear transformations, on being the inverse of the other, were compared to a linear transformation. Results indicate that a nonlinear transformation that increases the rate-or-change of output relative to input tends to advance the prediction response and improve the detection response, while a nonlinear transformation that decreases the rate-of-change of output relative to input tends to lengthen the prediction response and make detection more difficult.

Modeling of an intelligent pressure sensor using functional link artificial neural networks.

PubMed

Patra, J C; van den Bos, A

2000-01-01

A capacitor pressure sensor (CPS) is modeled for accurate readout of applied pressure using a novel artificial neural network (ANN). The proposed functional link ANN (FLANN) is a computationally efficient nonlinear network and is capable of complex nonlinear mapping between its input and output pattern space. The nonlinearity is introduced into the FLANN by passing the input pattern through a functional expansion unit. Three different polynomials such as, Chebyschev, Legendre and power series have been employed in the FLANN. The FLANN offers computational advantage over a multilayer perceptron (MLP) for similar performance in modeling of the CPS. The prime aim of the present paper is to develop an intelligent model of the CPS involving less computational complexity, so that its implementation can be economical and robust. It is shown that, over a wide temperature variation ranging from -50 to 150 degrees C, the maximum error of estimation of pressure remains within +/- 3%. With the help of computer simulation, the performance of the three types of FLANN models has been compared to that of an MLP based model.

Active disturbance rejection control based robust output feedback autopilot design for airbreathing hypersonic vehicles.

PubMed

Tian, Jiayi; Zhang, Shifeng; Zhang, Yinhui; Li, Tong

2018-03-01

Since motion control plant (y (n) =f(⋅)+d) was repeatedly used to exemplify how active disturbance rejection control (ADRC) works when it was proposed, the integral chain system subject to matched disturbances is always regarded as a canonical form and even misconstrued as the only form that ADRC is applicable to. In this paper, a systematic approach is first presented to apply ADRC to a generic nonlinear uncertain system with mismatched disturbances and a robust output feedback autopilot for an airbreathing hypersonic vehicle (AHV) is devised based on that. The key idea is to employ the feedback linearization (FL) and equivalent input disturbance (EID) technique to decouple nonlinear uncertain system into several subsystems in canonical form, thus it would be much easy to directly design classical/improved linear/nonlinear ADRC controller for each subsystem. It is noticed that all disturbances are taken into account when implementing FL rather than just omitting that in previous research, which greatly enhances controllers' robustness against external disturbances. For autopilot design, ADRC strategy enables precise tracking for velocity and altitude reference command in the presence of severe parametric perturbations and atmospheric disturbances only using measurable output information. Bounded-input-bounded-output (BIBO) stable is analyzed for closed-loop system. To illustrate the feasibility and superiority of this novel design, a series of comparative simulations with some prominent and representative methods are carried out on a benchmark longitudinal AHV model. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Robust control of a parallel hybrid drivetrain with a CVT

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mayer, T.; Schroeder, D.

1996-09-01

In this paper the design of a robust control system for a parallel hybrid drivetrain is presented. The drivetrain is based on a continuously variable transmission (CVT) and is therefore a highly nonlinear multiple-input-multiple-output system (MIMO-System). Input-Output-Linearization offers the possibility of linearizing and of decoupling the system. Since for example the vehicle mass varies with the load and the efficiency of the gearbox depends strongly on the actual working point, an exact linearization of the plant will mostly fail. Therefore a robust control algorithm based on sliding mode is used to control the drivetrain.

Principles of cell-free genetic circuit assembly.

PubMed

Noireaux, Vincent; Bar-Ziv, Roy; Libchaber, Albert

2003-10-28

Cell-free genetic circuit elements were constructed in a transcription-translation extract. We engineered transcriptional activation and repression cascades, in which the protein product of each stage is the input required to drive or block the following stage. Although we can find regions of linear response for single stages, cascading to subsequent stages requires working in nonlinear regimes. Substantial time delays and dramatic decreases in output production are incurred with each additional stage because of a bottleneck at the translation machinery. Faster turnover of RNA message can relieve competition between genes and stabilize output against variations in input and parameters.

An 11-bit and 39 ps resolution time-to-digital converter for ADPLL in digital television

NASA Astrophysics Data System (ADS)

Liu, Wei; (Ruth) Li, Wei; Ren, P.; Lin, C. L.; Zhang, Shengdong; Wang, Yangyuan

2010-04-01

We propose and demonstrate an 11-bit time-to-digital converter (TDC) for all-digital phase-locked loops (ADPLLs) in digital television. The proposed TDC converts the width of the input pulse into digital output with the tap space of the outputs of a free-running ring oscillator (FRO) being the conversion resolution. The FRO is in a structure of coiled cell array and the TDC core is symmetrical in the input structure. This leads to equally spaced taps in the reference clocks and thereby a high TDC conversion linearity. The TDC is fabricated in 0.13 μm CMOS process and the chip area is 0.025 mm2. The measurement results show that the TDC has a conversion resolution of 39 ps at 1.2 V power supply and a 4.5 ns dead time in the 11-bits output case. Both the differential non-linearity (DNL) and integral non-linearity (INL) are below 0.5 LSB. The power consumption of the whole circuit is 4.2 mW.

Reinforcement-learning-based output-feedback control of nonstrict nonlinear discrete-time systems with application to engine emission control.

PubMed

Shih, Peter; Kaul, Brian C; Jagannathan, Sarangapani; Drallmeier, James A

2009-10-01

A novel reinforcement-learning-based output adaptive neural network (NN) controller, which is also referred to as the adaptive-critic NN controller, is developed to deliver the desired tracking performance for a class of nonlinear discrete-time systems expressed in nonstrict feedback form in the presence of bounded and unknown disturbances. The adaptive-critic NN controller consists of an observer, a critic, and two action NNs. The observer estimates the states and output, and the two action NNs provide virtual and actual control inputs to the nonlinear discrete-time system. The critic approximates a certain strategic utility function, and the action NNs minimize the strategic utility function and control inputs. All NN weights adapt online toward minimization of a performance index, utilizing the gradient-descent-based rule, in contrast with iteration-based adaptive-critic schemes. Lyapunov functions are used to show the stability of the closed-loop tracking error, weights, and observer estimates. Separation and certainty equivalence principles, persistency of excitation condition, and linearity in the unknown parameter assumption are not needed. Experimental results on a spark ignition (SI) engine operating lean at an equivalence ratio of 0.75 show a significant (25%) reduction in cyclic dispersion in heat release with control, while the average fuel input changes by less than 1% compared with the uncontrolled case. Consequently, oxides of nitrogen (NO(x)) drop by 30%, and unburned hydrocarbons drop by 16% with control. Overall, NO(x)'s are reduced by over 80% compared with stoichiometric levels.

Control design methods for floating wind turbines for optimal disturbance rejection

NASA Astrophysics Data System (ADS)

Lemmer, Frank; Schlipf, David; Cheng, Po Wen

2016-09-01

An analysis of the floating wind turbine as a multi-input-multi-output system investigating the effect of the control inputs on the system outputs is shown. These effects are compared to the ones of the disturbances from wind and waves in order to give insights for the selection of the control layout. The frequencies with the largest impact on the outputs due to limited effect of the controlled variables are identified. Finally, an optimal controller is designed as a benchmark and compared to a conventional PI-controller using only the rotor speed as input. Here, the previously found system properties, especially the difficulties to damp responses to wave excitation, are confirmed and verified through a spectral analysis with realistic environmental conditions. This comparison also assesses the quality of the employed simplified linear simulation model compared to the nonlinear model and shows that such an efficient frequency-domain evaluation for control design is feasible.

Control of nonlinear flexible space structures

NASA Astrophysics Data System (ADS)

Shi, Jianjun

With the advances made in computer technology and efficiency of numerical algorithms over last decade, the MPC strategies have become quite popular among control community. However, application of MPC or GPC to flexible space structure control has not been explored adequately in the literature. The work presented in this thesis primarily focuses on application of GPC to control of nonlinear flexible space structures. This thesis is particularly devoted to the development of various approximate dynamic models, design and assessment of candidate controllers, and extensive numerical simulations for a realistic multibody flexible spacecraft, namely, Jupiter Icy Moons Orbiter (JIMO)---a Prometheus class of spacecraft proposed by NASA for deep space exploratory missions. A stable GPC algorithm is developed for Multi-Input-Multi-Output (MIMO) systems. An end-point weighting (penalty) is used in the GPC cost function to guarantee the nominal stability of the closed-loop system. A method is given to compute the desired end-point state from the desired output trajectory. The methodologies based on Fake Algebraic Riccati Equation (FARE) and constrained nonlinear optimization, are developed for synthesis of state weighting matrix. This makes this formulation more practical. A stable reconfigurable GPC architecture is presented and its effectiveness is demonstrated on both aircraft as well as spacecraft model. A representative in-orbit maneuver is used for assessing the performance of various control strategies using various design models. Different approximate dynamic models used for analysis include linear single body flexible structure, nonlinear single body flexible structure, and nonlinear multibody flexible structure. The control laws evaluated include traditional GPC, feedback linearization-based GPC (FLGPC), reconfigurable GPC, and nonlinear dissipative control. These various control schemes are evaluated for robust stability and robust performance in the presence of parametric uncertainties and input disturbances. Finally, the conclusions are made with regard to the efficacy of these controllers and potential directions for future research.

Machine Learning Classification of Heterogeneous Fields to Estimate Physical Responses

NASA Astrophysics Data System (ADS)

McKenna, S. A.; Akhriev, A.; Alzate, C.; Zhuk, S.

2017-12-01

The promise of machine learning to enhance physics-based simulation is examined here using the transient pressure response to a pumping well in a heterogeneous aquifer. 10,000 random fields of log10 hydraulic conductivity (K) are created and conditioned on a single K measurement at the pumping well. Each K-field is used as input to a forward simulation of drawdown (pressure decline). The differential equations governing groundwater flow to the well serve as a non-linear transform of the input K-field to an output drawdown field. The results are stored and the data set is split into training and testing sets for classification. A Euclidean distance measure between any two fields is calculated and the resulting distances between all pairs of fields define a similarity matrix. Similarity matrices are calculated for both input K-fields and the resulting drawdown fields at the end of the simulation. The similarity matrices are then used as input to spectral clustering to determine groupings of similar input and output fields. Additionally, the similarity matrix is used as input to multi-dimensional scaling to visualize the clustering of fields in lower dimensional spaces. We examine the ability to cluster both input K-fields and output drawdown fields separately with the goal of identifying K-fields that create similar drawdowns and, conversely, given a set of simulated drawdown fields, identify meaningful clusters of input K-fields. Feature extraction based on statistical parametric mapping provides insight into what features of the fields drive the classification results. The final goal is to successfully classify input K-fields into the correct output class, and also, given an output drawdown field, be able to infer the correct class of input field that created it.

Boolean Modeling of Neural Systems with Point-Process Inputs and Outputs. Part I: Theory and Simulations

PubMed Central

Marmarelis, Vasilis Z.; Zanos, Theodoros P.; Berger, Theodore W.

2010-01-01

This paper presents a new modeling approach for neural systems with point-process (spike) inputs and outputs that utilizes Boolean operators (i.e. modulo 2 multiplication and addition that correspond to the logical AND and OR operations respectively, as well as the AND_NOT logical operation representing inhibitory effects). The form of the employed mathematical models is akin to a “Boolean-Volterra” model that contains the product terms of all relevant input lags in a hierarchical order, where terms of order higher than first represent nonlinear interactions among the various lagged values of each input point-process or among lagged values of various inputs (if multiple inputs exist) as they reflect on the output. The coefficients of this Boolean-Volterra model are also binary variables that indicate the presence or absence of the respective term in each specific model/system. Simulations are used to explore the properties of such models and the feasibility of their accurate estimation from short data-records in the presence of noise (i.e. spurious spikes). The results demonstrate the feasibility of obtaining reliable estimates of such models, with excitatory and inhibitory terms, in the presence of considerable noise (spurious spikes) in the outputs and/or the inputs in a computationally efficient manner. A pilot application of this approach to an actual neural system is presented in the companion paper (Part II). PMID:19517238

Hierarchical cluster-based partial least squares regression (HC-PLSR) is an efficient tool for metamodelling of nonlinear dynamic models.

PubMed

Tøndel, Kristin; Indahl, Ulf G; Gjuvsland, Arne B; Vik, Jon Olav; Hunter, Peter; Omholt, Stig W; Martens, Harald

2011-06-01

Deterministic dynamic models of complex biological systems contain a large number of parameters and state variables, related through nonlinear differential equations with various types of feedback. A metamodel of such a dynamic model is a statistical approximation model that maps variation in parameters and initial conditions (inputs) to variation in features of the trajectories of the state variables (outputs) throughout the entire biologically relevant input space. A sufficiently accurate mapping can be exploited both instrumentally and epistemically. Multivariate regression methodology is a commonly used approach for emulating dynamic models. However, when the input-output relations are highly nonlinear or non-monotone, a standard linear regression approach is prone to give suboptimal results. We therefore hypothesised that a more accurate mapping can be obtained by locally linear or locally polynomial regression. We present here a new method for local regression modelling, Hierarchical Cluster-based PLS regression (HC-PLSR), where fuzzy C-means clustering is used to separate the data set into parts according to the structure of the response surface. We compare the metamodelling performance of HC-PLSR with polynomial partial least squares regression (PLSR) and ordinary least squares (OLS) regression on various systems: six different gene regulatory network models with various types of feedback, a deterministic mathematical model of the mammalian circadian clock and a model of the mouse ventricular myocyte function. Our results indicate that multivariate regression is well suited for emulating dynamic models in systems biology. The hierarchical approach turned out to be superior to both polynomial PLSR and OLS regression in all three test cases. The advantage, in terms of explained variance and prediction accuracy, was largest in systems with highly nonlinear functional relationships and in systems with positive feedback loops. HC-PLSR is a promising approach for metamodelling in systems biology, especially for highly nonlinear or non-monotone parameter to phenotype maps. The algorithm can be flexibly adjusted to suit the complexity of the dynamic model behaviour, inviting automation in the metamodelling of complex systems.

Hierarchical Cluster-based Partial Least Squares Regression (HC-PLSR) is an efficient tool for metamodelling of nonlinear dynamic models

PubMed Central

2011-01-01

Background Deterministic dynamic models of complex biological systems contain a large number of parameters and state variables, related through nonlinear differential equations with various types of feedback. A metamodel of such a dynamic model is a statistical approximation model that maps variation in parameters and initial conditions (inputs) to variation in features of the trajectories of the state variables (outputs) throughout the entire biologically relevant input space. A sufficiently accurate mapping can be exploited both instrumentally and epistemically. Multivariate regression methodology is a commonly used approach for emulating dynamic models. However, when the input-output relations are highly nonlinear or non-monotone, a standard linear regression approach is prone to give suboptimal results. We therefore hypothesised that a more accurate mapping can be obtained by locally linear or locally polynomial regression. We present here a new method for local regression modelling, Hierarchical Cluster-based PLS regression (HC-PLSR), where fuzzy C-means clustering is used to separate the data set into parts according to the structure of the response surface. We compare the metamodelling performance of HC-PLSR with polynomial partial least squares regression (PLSR) and ordinary least squares (OLS) regression on various systems: six different gene regulatory network models with various types of feedback, a deterministic mathematical model of the mammalian circadian clock and a model of the mouse ventricular myocyte function. Results Our results indicate that multivariate regression is well suited for emulating dynamic models in systems biology. The hierarchical approach turned out to be superior to both polynomial PLSR and OLS regression in all three test cases. The advantage, in terms of explained variance and prediction accuracy, was largest in systems with highly nonlinear functional relationships and in systems with positive feedback loops. Conclusions HC-PLSR is a promising approach for metamodelling in systems biology, especially for highly nonlinear or non-monotone parameter to phenotype maps. The algorithm can be flexibly adjusted to suit the complexity of the dynamic model behaviour, inviting automation in the metamodelling of complex systems. PMID:21627852

Bilinear modeling and nonlinear estimation

NASA Technical Reports Server (NTRS)

Dwyer, Thomas A. W., III; Karray, Fakhreddine; Bennett, William H.

1989-01-01

New methods are illustrated for online nonlinear estimation applied to the lateral deflection of an elastic beam on board measurements of angular rates and angular accelerations. The development of the filter equations, together with practical issues of their numerical solution as developed from global linearization by nonlinear output injection are contrasted with the usual method of the extended Kalman filter (EKF). It is shown how nonlinear estimation due to gyroscopic coupling can be implemented as an adaptive covariance filter using off-the-shelf Kalman filter algorithms. The effect of the global linearization by nonlinear output injection is to introduce a change of coordinates in which only the process noise covariance is to be updated in online implementation. This is in contrast to the computational approach which arises in EKF methods arising by local linearization with respect to the current conditional mean. Processing refinements for nonlinear estimation based on optimal, nonlinear interpolation between observations are also highlighted. In these methods the extrapolation of the process dynamics between measurement updates is obtained by replacing a transition matrix with an operator spline that is optimized off-line from responses to selected test inputs.

Neural networks: What non-linearity to choose

NASA Technical Reports Server (NTRS)

Kreinovich, Vladik YA.; Quintana, Chris

1991-01-01

Neural networks are now one of the most successful learning formalisms. Neurons transform inputs (x(sub 1),...,x(sub n)) into an output f(w(sub 1)x(sub 1) + ... + w(sub n)x(sub n)), where f is a non-linear function and w, are adjustable weights. What f to choose? Usually the logistic function is chosen, but sometimes the use of different functions improves the practical efficiency of the network. The problem of choosing f as a mathematical optimization problem is formulated and solved under different optimality criteria. As a result, a list of functions f that are optimal under these criteria are determined. This list includes both the functions that were empirically proved to be the best for some problems, and some new functions that may be worth trying.

The Multiple Correspondence Analysis Method and Brain Functional Connectivity: Its Application to the Study of the Non-linear Relationships of Motor Cortex and Basal Ganglia.

PubMed

Rodriguez-Sabate, Clara; Morales, Ingrid; Sanchez, Alberto; Rodriguez, Manuel

2017-01-01

The complexity of basal ganglia (BG) interactions is often condensed into simple models mainly based on animal data and that present BG in closed-loop cortico-subcortical circuits of excitatory/inhibitory pathways which analyze the incoming cortical data and return the processed information to the cortex. This study was aimed at identifying functional relationships in the BG motor-loop of 24 healthy-subjects who provided written, informed consent and whose BOLD-activity was recorded by MRI methods. The analysis of the functional interaction between these centers by correlation techniques and multiple linear regression showed non-linear relationships which cannot be suitably addressed with these methods. The multiple correspondence analysis (MCA), an unsupervised multivariable procedure which can identify non-linear interactions, was used to study the functional connectivity of BG when subjects were at rest. Linear methods showed different functional interactions expected according to current BG models. MCA showed additional functional interactions which were not evident when using lineal methods. Seven functional configurations of BG were identified with MCA, two involving the primary motor and somatosensory cortex, one involving the deepest BG (external-internal globus pallidum, subthalamic nucleus and substantia nigral), one with the input-output BG centers (putamen and motor thalamus), two linking the input-output centers with other BG (external pallidum and subthalamic nucleus), and one linking the external pallidum and the substantia nigral. The results provide evidence that the non-linear MCA and linear methods are complementary and should be best used in conjunction to more fully understand the nature of functional connectivity of brain centers.

Characteristic operator functions for quantum input-plant-output models and coherent control

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gough, John E.

We introduce the characteristic operator as the generalization of the usual concept of a transfer function of linear input-plant-output systems to arbitrary quantum nonlinear Markovian input-output models. This is intended as a tool in the characterization of quantum feedback control systems that fits in with the general theory of networks. The definition exploits the linearity of noise differentials in both the plant Heisenberg equations of motion and the differential form of the input-output relations. Mathematically, the characteristic operator is a matrix of dimension equal to the number of outputs times the number of inputs (which must coincide), but with entriesmore » that are operators of the plant system. In this sense, the characteristic operator retains details of the effective plant dynamical structure and is an essentially quantum object. We illustrate the relevance to model reduction and simplification definition by showing that the convergence of the characteristic operator in adiabatic elimination limit models requires the same conditions and assumptions appearing in the work on limit quantum stochastic differential theorems of Bouten and Silberfarb [Commun. Math. Phys. 283, 491-505 (2008)]. This approach also shows in a natural way that the limit coefficients of the quantum stochastic differential equations in adiabatic elimination problems arise algebraically as Schur complements and amounts to a model reduction where the fast degrees of freedom are decoupled from the slow ones and eliminated.« less

Incomplete data based parameter identification of nonlinear and time-variant oscillators with fractional derivative elements

NASA Astrophysics Data System (ADS)

Kougioumtzoglou, Ioannis A.; dos Santos, Ketson R. M.; Comerford, Liam

2017-09-01

Various system identification techniques exist in the literature that can handle non-stationary measured time-histories, or cases of incomplete data, or address systems following a fractional calculus modeling. However, there are not many (if any) techniques that can address all three aforementioned challenges simultaneously in a consistent manner. In this paper, a novel multiple-input/single-output (MISO) system identification technique is developed for parameter identification of nonlinear and time-variant oscillators with fractional derivative terms subject to incomplete non-stationary data. The technique utilizes a representation of the nonlinear restoring forces as a set of parallel linear sub-systems. In this regard, the oscillator is transformed into an equivalent MISO system in the wavelet domain. Next, a recently developed L1-norm minimization procedure based on compressive sensing theory is applied for determining the wavelet coefficients of the available incomplete non-stationary input-output (excitation-response) data. Finally, these wavelet coefficients are utilized to determine appropriately defined time- and frequency-dependent wavelet based frequency response functions and related oscillator parameters. Several linear and nonlinear time-variant systems with fractional derivative elements are used as numerical examples to demonstrate the reliability of the technique even in cases of noise corrupted and incomplete data.

Computer model of one-dimensional equilibrium controlled sorption processes

USGS Publications Warehouse

Grove, D.B.; Stollenwerk, K.G.

1984-01-01

A numerical solution to the one-dimensional solute-transport equation with equilibrium-controlled sorption and a first-order irreversible-rate reaction is presented. The computer code is written in FORTRAN language, with a variety of options for input and output for user ease. Sorption reactions include Langmuir, Freundlich, and ion-exchange, with or without equal valance. General equations describing transport and reaction processes are solved by finite-difference methods, with nonlinearities accounted for by iteration. Complete documentation of the code, with examples, is included. (USGS)

A fuzzy controller with nonlinear control rules is the sum of a global nonlinear controller and a local nonlinear PI-like controller

NASA Technical Reports Server (NTRS)

Ying, Hao

1993-01-01

The fuzzy controllers studied in this paper are the ones that employ N trapezoidal-shaped members for input fuzzy sets, Zadeh fuzzy logic and a centroid defuzzification algorithm for output fuzzy set. The author analytically proves that the structure of the fuzzy controllers is the sum of a global nonlinear controller and a local nonlinear proportional-integral-like controller. If N approaches infinity, the global controller becomes a nonlinear controller while the local controller disappears. If linear control rules are used, the global controller becomes a global two-dimensional multilevel relay which approaches a global linear proportional-integral (PI) controller as N approaches infinity.

A methodology for designing robust multivariable nonlinear control systems. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Grunberg, D. B.

1986-01-01

A new methodology is described for the design of nonlinear dynamic controllers for nonlinear multivariable systems providing guarantees of closed-loop stability, performance, and robustness. The methodology is an extension of the Linear-Quadratic-Gaussian with Loop-Transfer-Recovery (LQG/LTR) methodology for linear systems, thus hinging upon the idea of constructing an approximate inverse operator for the plant. A major feature of the methodology is a unification of both the state-space and input-output formulations. In addition, new results on stability theory, nonlinear state estimation, and optimal nonlinear regulator theory are presented, including the guaranteed global properties of the extended Kalman filter and optimal nonlinear regulators.

Robustness of controllability and observability of linear time-varying systems with application to the emergency control of power systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sastry, S. S.; Desoer, C. A.

1980-01-01

Fixed point methods from nonlinear anaysis are used to establish conditions under which the uniform complete controllability of linear time-varying systems is preserved under non-linear perturbations in the state dynamics and the zero-input uniform complete observability of linear time-varying systems is preserved under non-linear perturbation in the state dynamics and output read out map. Algorithms for computing the specific input to steer the perturbed systems from a given initial state to a given final state are also presented. As an application, a very specific emergency control of an interconnected power system is formulated as a steering problem and it ismore » shown that this emergency control is indeed possible in finite time.« less

Acousto-Optical Method of Encoding and Visualization of Underwater Space

DTIC Science & Technology

2014-01-27

neurons which are mathematically described as coupled nonlinear oscillators that are slightly unstable. They have a property called ’ Self - Referential ... self - regulating process which is represented by Equation (5) in the ensuing description. [0083] The input/output circuitry 64 outputs signals that...other words, self -correcting dynamics of the Na and Ca ions in the membranes are closely related to the sensing and the flopping of motion actuators

Research on the output bit error rate of 2DPSK signal based on stochastic resonance theory

NASA Astrophysics Data System (ADS)

Yan, Daqin; Wang, Fuzhong; Wang, Shuo

2017-12-01

Binary differential phase-shift keying (2DPSK) signal is mainly used for high speed data transmission. However, the bit error rate of digital signal receiver is high in the case of wicked channel environment. In view of this situation, a novel method based on stochastic resonance (SR) is proposed, which is aimed to reduce the bit error rate of 2DPSK signal by coherent demodulation receiving. According to the theory of SR, a nonlinear receiver model is established, which is used to receive 2DPSK signal under small signal-to-noise ratio (SNR) circumstances (between -15 dB and 5 dB), and compared with the conventional demodulation method. The experimental results demonstrate that when the input SNR is in the range of -15 dB to 5 dB, the output bit error rate of nonlinear system model based on SR has a significant decline compared to the conventional model. It could reduce 86.15% when the input SNR equals -7 dB. Meanwhile, the peak value of the output signal spectrum is 4.25 times as that of the conventional model. Consequently, the output signal of the system is more likely to be detected and the accuracy can be greatly improved.

A Combined Adaptive Neural Network and Nonlinear Model Predictive Control for Multirate Networked Industrial Process Control.

PubMed

Wang, Tong; Gao, Huijun; Qiu, Jianbin

2016-02-01

This paper investigates the multirate networked industrial process control problem in double-layer architecture. First, the output tracking problem for sampled-data nonlinear plant at device layer with sampling period T(d) is investigated using adaptive neural network (NN) control, and it is shown that the outputs of subsystems at device layer can track the decomposed setpoints. Then, the outputs and inputs of the device layer subsystems are sampled with sampling period T(u) at operation layer to form the index prediction, which is used to predict the overall performance index at lower frequency. Radial basis function NN is utilized as the prediction function due to its approximation ability. Then, considering the dynamics of the overall closed-loop system, nonlinear model predictive control method is proposed to guarantee the system stability and compensate the network-induced delays and packet dropouts. Finally, a continuous stirred tank reactor system is given in the simulation part to demonstrate the effectiveness of the proposed method.

The role of pulvinar in the transmission of information in the visual hierarchy.

PubMed

Cortes, Nelson; van Vreeswijk, Carl

2012-01-01

VISUAL RECEPTIVE FIELD (RF) ATTRIBUTES IN VISUAL CORTEX OF PRIMATES HAVE BEEN EXPLAINED MAINLY FROM CORTICAL CONNECTIONS: visual RFs progress from simple to complex through cortico-cortical pathways from lower to higher levels in the visual hierarchy. This feedforward flow of information is paired with top-down processes through the feedback pathway. Although the hierarchical organization explains the spatial properties of RFs, is unclear how a non-linear transmission of activity through the visual hierarchy can yield smooth contrast response functions in all level of the hierarchy. Depending on the gain, non-linear transfer functions create either a bimodal response to contrast, or no contrast dependence of the response in the highest level of the hierarchy. One possible mechanism to regulate this transmission of visual contrast information from low to high level involves an external component that shortcuts the flow of information through the hierarchy. A candidate for this shortcut is the Pulvinar nucleus of the thalamus. To investigate representation of stimulus contrast a hierarchical model network of ten cortical areas is examined. In each level of the network, the activity from the previous layer is integrated and then non-linearly transmitted to the next level. The arrangement of interactions creates a gradient from simple to complex RFs of increasing size as one moves from lower to higher cortical levels. The visual input is modeled as a Gaussian random input, whose width codes for the contrast. This input is applied to the first area. The output activity ratio among different contrast values is analyzed for the last level to observe sensitivity to a contrast and contrast invariant tuning. For a purely cortical system, the output of the last area can be approximately contrast invariant, but the sensitivity to contrast is poor. To account for an alternative visual processing pathway, non-reciprocal connections from and to a parallel pulvinar like structure of nine areas is coupled to the system. Compared to the pure feedforward model, cortico-pulvino-cortical output presents much more sensitivity to contrast and has a similar level of contrast invariance of the tuning.

The Role of Pulvinar in the Transmission of Information in the Visual Hierarchy

PubMed Central

Cortes, Nelson; van Vreeswijk, Carl

2012-01-01

Visual receptive field (RF) attributes in visual cortex of primates have been explained mainly from cortical connections: visual RFs progress from simple to complex through cortico-cortical pathways from lower to higher levels in the visual hierarchy. This feedforward flow of information is paired with top-down processes through the feedback pathway. Although the hierarchical organization explains the spatial properties of RFs, is unclear how a non-linear transmission of activity through the visual hierarchy can yield smooth contrast response functions in all level of the hierarchy. Depending on the gain, non-linear transfer functions create either a bimodal response to contrast, or no contrast dependence of the response in the highest level of the hierarchy. One possible mechanism to regulate this transmission of visual contrast information from low to high level involves an external component that shortcuts the flow of information through the hierarchy. A candidate for this shortcut is the Pulvinar nucleus of the thalamus. To investigate representation of stimulus contrast a hierarchical model network of ten cortical areas is examined. In each level of the network, the activity from the previous layer is integrated and then non-linearly transmitted to the next level. The arrangement of interactions creates a gradient from simple to complex RFs of increasing size as one moves from lower to higher cortical levels. The visual input is modeled as a Gaussian random input, whose width codes for the contrast. This input is applied to the first area. The output activity ratio among different contrast values is analyzed for the last level to observe sensitivity to a contrast and contrast invariant tuning. For a purely cortical system, the output of the last area can be approximately contrast invariant, but the sensitivity to contrast is poor. To account for an alternative visual processing pathway, non-reciprocal connections from and to a parallel pulvinar like structure of nine areas is coupled to the system. Compared to the pure feedforward model, cortico-pulvino-cortical output presents much more sensitivity to contrast and has a similar level of contrast invariance of the tuning. PMID:22654750

Linear models of activation cascades: analytical solutions and coarse-graining of delayed signal transduction

PubMed Central

Desikan, Radhika

2016-01-01

Cellular signal transduction usually involves activation cascades, the sequential activation of a series of proteins following the reception of an input signal. Here, we study the classic model of weakly activated cascades and obtain analytical solutions for a variety of inputs. We show that in the special but important case of optimal gain cascades (i.e. when the deactivation rates are identical) the downstream output of the cascade can be represented exactly as a lumped nonlinear module containing an incomplete gamma function with real parameters that depend on the rates and length of the cascade, as well as parameters of the input signal. The expressions obtained can be applied to the non-identical case when the deactivation rates are random to capture the variability in the cascade outputs. We also show that cascades can be rearranged so that blocks with similar rates can be lumped and represented through our nonlinear modules. Our results can be used both to represent cascades in computational models of differential equations and to fit data efficiently, by reducing the number of equations and parameters involved. In particular, the length of the cascade appears as a real-valued parameter and can thus be fitted in the same manner as Hill coefficients. Finally, we show how the obtained nonlinear modules can be used instead of delay differential equations to model delays in signal transduction. PMID:27581482

Genetics-based control of a mimo boiler-turbine plant

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dimeo, R.M.; Lee, K.Y.

1994-12-31

A genetic algorithm is used to develop an optimal controller for a non-linear, multi-input/multi-output boiler-turbine plant. The algorithm is used to train a control system for the plant over a wide operating range in an effort to obtain better performance. The results of the genetic algorithm`s controller designed from the linearized plant model at a nominal operating point. Because the genetic algorithm is well-suited to solving traditionally difficult optimization problems it is found that the algorithm is capable of developing the controller based on input/output information only. This controller achieves a performance comparable to the standard linear quadratic regulator.

Further observations on the relationship of EMG and muscle force

NASA Technical Reports Server (NTRS)

Agarwal, G. C.; Cecchini, L. R.; Gottlieb, G. L.

1972-01-01

Human skeletal muscle may be regarded as an electro-mechanical transducer. Its physiological input is a neural signal originating at the alpha motoneurons in the spinal cord and its output is force and muscle contraction, these both being dependent on the external load. Some experimental data taken during voluntary efforts around the ankle joint and by direct electrical stimulation of the nerve are described. Some of these experiments are simulated by an analog model, the input of which is recorded physiological soleus muscle EMG. The output is simulated foot torque. Limitations of a linear model and effect of some nonlinearities are discussed.

Adaptive Neural Network-Based Event-Triggered Control of Single-Input Single-Output Nonlinear Discrete-Time Systems.

PubMed

Sahoo, Avimanyu; Xu, Hao; Jagannathan, Sarangapani

2016-01-01

This paper presents a novel adaptive neural network (NN) control of single-input and single-output uncertain nonlinear discrete-time systems under event sampled NN inputs. In this control scheme, the feedback signals are transmitted, and the NN weights are tuned in an aperiodic manner at the event sampled instants. After reviewing the NN approximation property with event sampled inputs, an adaptive state estimator (SE), consisting of linearly parameterized NNs, is utilized to approximate the unknown system dynamics in an event sampled context. The SE is viewed as a model and its approximated dynamics and the state vector, during any two events, are utilized for the event-triggered controller design. An adaptive event-trigger condition is derived by using both the estimated NN weights and a dead-zone operator to determine the event sampling instants. This condition both facilitates the NN approximation and reduces the transmission of feedback signals. The ultimate boundedness of both the NN weight estimation error and the system state vector is demonstrated through the Lyapunov approach. As expected, during an initial online learning phase, events are observed more frequently. Over time with the convergence of the NN weights, the inter-event times increase, thereby lowering the number of triggered events. These claims are illustrated through the simulation results.

Data-driven robust approximate optimal tracking control for unknown general nonlinear systems using adaptive dynamic programming method.

PubMed

Zhang, Huaguang; Cui, Lili; Zhang, Xin; Luo, Yanhong

2011-12-01

In this paper, a novel data-driven robust approximate optimal tracking control scheme is proposed for unknown general nonlinear systems by using the adaptive dynamic programming (ADP) method. In the design of the controller, only available input-output data is required instead of known system dynamics. A data-driven model is established by a recurrent neural network (NN) to reconstruct the unknown system dynamics using available input-output data. By adding a novel adjustable term related to the modeling error, the resultant modeling error is first guaranteed to converge to zero. Then, based on the obtained data-driven model, the ADP method is utilized to design the approximate optimal tracking controller, which consists of the steady-state controller and the optimal feedback controller. Further, a robustifying term is developed to compensate for the NN approximation errors introduced by implementing the ADP method. Based on Lyapunov approach, stability analysis of the closed-loop system is performed to show that the proposed controller guarantees the system state asymptotically tracking the desired trajectory. Additionally, the obtained control input is proven to be close to the optimal control input within a small bound. Finally, two numerical examples are used to demonstrate the effectiveness of the proposed control scheme.

Effect of pulse width on near-infrared supercontinuum generation in nonlinear fiber amplifier

NASA Astrophysics Data System (ADS)

Song, Rui; Lei, Cheng-Min; Chen, Sheng-Ping; Wang, Ze-Feng; Hou, Jing

2015-08-01

The effect of pulse width on near-infrared supercontinuum generation in nonlinear fiber amplifier is investigated theoretically and experimentally. The complex Ginzburg-Landau equation and adaptive split-step Fourier method are used to simulate the propagation of pulses with different pulse widths in the fiber amplifier, and the results show that a longer pulse is more profitable in near-infrared supercontinuum generation if the central wavelength of the input laser lies in the normal dispersion region of the gain fiber. A four-stage master oscillator power amplifier configuration is adopted and the output spectra under picosecond and nanosecond input pulses are compared with each other. The experimental results are in good accordance with the simulations which can provide some guidance for further optimization of the system. Project supported by the National Natural Science Foundation of China (Grant Nos. 11404404 and 11274385) and the Outstanding Youth Fund Project of Hunan Province and the Fund of Innovation of National University of Defense Technology, China (Grant No. B120701).

Photon transport in a dissipative chain of nonlinear cavities

NASA Astrophysics Data System (ADS)

Biella, Alberto; Mazza, Leonardo; Carusotto, Iacopo; Rossini, Davide; Fazio, Rosario

2015-05-01

By means of numerical simulations and the input-output formalism, we study photon transport through a chain of coupled nonlinear optical cavities subject to uniform dissipation. Photons are injected from one end of the chain by means of a coherent source. The propagation through the array of cavities is sensitive to the interplay between the photon hopping strength and the local nonlinearity in each cavity. We characterize photon transport by studying the populations and the photon correlations as a function of the cavity position. When complemented with input-output theory, these quantities provide direct information about photon transmission through the system. The position of single-photon and multiphoton resonances directly reflects the structure of the many-body energy levels. This shows how a study of transport along a coupled cavity array can provide rich information about the strongly correlated (many-body) states of light even in presence of dissipation. The numerical algorithm we use, based on the time-evolving block decimation scheme adapted to mixed states, allows us to simulate large arrays (up to 60 cavities). The scaling of photon transmission with the number of cavities does depend on the structure of the many-body photon states inside the array.

Logarithmic and power law input-output relations in sensory systems with fold-change detection.

PubMed

Adler, Miri; Mayo, Avi; Alon, Uri

2014-08-01

Two central biophysical laws describe sensory responses to input signals. One is a logarithmic relationship between input and output, and the other is a power law relationship. These laws are sometimes called the Weber-Fechner law and the Stevens power law, respectively. The two laws are found in a wide variety of human sensory systems including hearing, vision, taste, and weight perception; they also occur in the responses of cells to stimuli. However the mechanistic origin of these laws is not fully understood. To address this, we consider a class of biological circuits exhibiting a property called fold-change detection (FCD). In these circuits the response dynamics depend only on the relative change in input signal and not its absolute level, a property which applies to many physiological and cellular sensory systems. We show analytically that by changing a single parameter in the FCD circuits, both logarithmic and power-law relationships emerge; these laws are modified versions of the Weber-Fechner and Stevens laws. The parameter that determines which law is found is the steepness (effective Hill coefficient) of the effect of the internal variable on the output. This finding applies to major circuit architectures found in biological systems, including the incoherent feed-forward loop and nonlinear integral feedback loops. Therefore, if one measures the response to different fold changes in input signal and observes a logarithmic or power law, the present theory can be used to rule out certain FCD mechanisms, and to predict their cooperativity parameter. We demonstrate this approach using data from eukaryotic chemotaxis signaling.

Parameter reduction in nonlinear state-space identification of hysteresis

NASA Astrophysics Data System (ADS)

Fakhrizadeh Esfahani, Alireza; Dreesen, Philippe; Tiels, Koen; Noël, Jean-Philippe; Schoukens, Johan

2018-05-01

Recent work on black-box polynomial nonlinear state-space modeling for hysteresis identification has provided promising results, but struggles with a large number of parameters due to the use of multivariate polynomials. This drawback is tackled in the current paper by applying a decoupling approach that results in a more parsimonious representation involving univariate polynomials. This work is carried out numerically on input-output data generated by a Bouc-Wen hysteretic model and follows up on earlier work of the authors. The current article discusses the polynomial decoupling approach and explores the selection of the number of univariate polynomials with the polynomial degree. We have found that the presented decoupling approach is able to reduce the number of parameters of the full nonlinear model up to about 50%, while maintaining a comparable output error level.

Nonlinear aspects of infrasonic pressure transfer into the perilymph.

PubMed

Krukowski, B; Carlborg, B; Densert, O

1980-06-01

The perilymphatic pressure was studied in response to various low frequency pressure changes in the ear canal. The pressure transfer was analysed and found to be nonlinear in many aspects. The pressure response was found to contain two time constants representing the inner ear pressure regulating mechanisms. The time constants showed an asymmetry in response to positive and negative going inputs--the effects to some extent proportional to input levels. Further nonlinearities were found when infrasonic sine waves were applied to the ear. Harmonic distortion and modulation appeared. When short bursts of infrasound were introduced a clear d.c. shift was observed as a consequence of an asymmetry in the response to positive and negative going pressure inputs. A temporary change in mean perilymphatic pressure was thus achieved and continued throughout the duration of the signal. At very low frequencies a distinct phase shift was detected in the sine waves. This appeared as a phase lead, breaking the continuity of the output sine wave.

Model-Free Primitive-Based Iterative Learning Control Approach to Trajectory Tracking of MIMO Systems With Experimental Validation.

PubMed

Radac, Mircea-Bogdan; Precup, Radu-Emil; Petriu, Emil M

2015-11-01

This paper proposes a novel model-free trajectory tracking of multiple-input multiple-output (MIMO) systems by the combination of iterative learning control (ILC) and primitives. The optimal trajectory tracking solution is obtained in terms of previously learned solutions to simple tasks called primitives. The library of primitives that are stored in memory consists of pairs of reference input/controlled output signals. The reference input primitives are optimized in a model-free ILC framework without using knowledge of the controlled process. The guaranteed convergence of the learning scheme is built upon a model-free virtual reference feedback tuning design of the feedback decoupling controller. Each new complex trajectory to be tracked is decomposed into the output primitives regarded as basis functions. The optimal reference input for the control system to track the desired trajectory is next recomposed from the reference input primitives. This is advantageous because the optimal reference input is computed straightforward without the need to learn from repeated executions of the tracking task. In addition, the optimization problem specific to trajectory tracking of square MIMO systems is decomposed in a set of optimization problems assigned to each separate single-input single-output control channel that ensures a convenient model-free decoupling. The new model-free primitive-based ILC approach is capable of planning, reasoning, and learning. A case study dealing with the model-free control tuning for a nonlinear aerodynamic system is included to validate the new approach. The experimental results are given.

Uncertainty importance analysis using parametric moment ratio functions.

PubMed

Wei, Pengfei; Lu, Zhenzhou; Song, Jingwen

2014-02-01

This article presents a new importance analysis framework, called parametric moment ratio function, for measuring the reduction of model output uncertainty when the distribution parameters of inputs are changed, and the emphasis is put on the mean and variance ratio functions with respect to the variances of model inputs. The proposed concepts efficiently guide the analyst to achieve a targeted reduction on the model output mean and variance by operating on the variances of model inputs. The unbiased and progressive unbiased Monte Carlo estimators are also derived for the parametric mean and variance ratio functions, respectively. Only a set of samples is needed for implementing the proposed importance analysis by the proposed estimators, thus the computational cost is free of input dimensionality. An analytical test example with highly nonlinear behavior is introduced for illustrating the engineering significance of the proposed importance analysis technique and verifying the efficiency and convergence of the derived Monte Carlo estimators. Finally, the moment ratio function is applied to a planar 10-bar structure for achieving a targeted 50% reduction of the model output variance. © 2013 Society for Risk Analysis.

A Neural Network-Based Gait Phase Classification Method Using Sensors Equipped on Lower Limb Exoskeleton Robots

PubMed Central

Jung, Jun-Young; Heo, Wonho; Yang, Hyundae; Park, Hyunsub

2015-01-01

An exact classification of different gait phases is essential to enable the control of exoskeleton robots and detect the intentions of users. We propose a gait phase classification method based on neural networks using sensor signals from lower limb exoskeleton robots. In such robots, foot sensors with force sensing registers are commonly used to classify gait phases. We describe classifiers that use the orientation of each lower limb segment and the angular velocities of the joints to output the current gait phase. Experiments to obtain the input signals and desired outputs for the learning and validation process are conducted, and two neural network methods (a multilayer perceptron and nonlinear autoregressive with external inputs (NARX)) are used to develop an optimal classifier. Offline and online evaluations using four criteria are used to compare the performance of the classifiers. The proposed NARX-based method exhibits sufficiently good performance to replace foot sensors as a means of classifying gait phases. PMID:26528986

A Neural Network-Based Gait Phase Classification Method Using Sensors Equipped on Lower Limb Exoskeleton Robots.

PubMed

Jung, Jun-Young; Heo, Wonho; Yang, Hyundae; Park, Hyunsub

2015-10-30

An exact classification of different gait phases is essential to enable the control of exoskeleton robots and detect the intentions of users. We propose a gait phase classification method based on neural networks using sensor signals from lower limb exoskeleton robots. In such robots, foot sensors with force sensing registers are commonly used to classify gait phases. We describe classifiers that use the orientation of each lower limb segment and the angular velocities of the joints to output the current gait phase. Experiments to obtain the input signals and desired outputs for the learning and validation process are conducted, and two neural network methods (a multilayer perceptron and nonlinear autoregressive with external inputs (NARX)) are used to develop an optimal classifier. Offline and online evaluations using four criteria are used to compare the performance of the classifiers. The proposed NARX-based method exhibits sufficiently good performance to replace foot sensors as a means of classifying gait phases.

Dynamics identification of a piezoelectric vibrational energy harvester by image analysis with a high speed camera

NASA Astrophysics Data System (ADS)

Wolszczak, Piotr; Łygas, Krystian; Litak, Grzegorz

2018-07-01

This study investigates dynamic responses of a nonlinear vibration energy harvester. The nonlinear mechanical resonator consists of a flexible beam moving like an inverted pendulum between amplitude limiters. It is coupled with a piezoelectric converter, and excited kinematically. Consequently, the mechanical energy input is converted into the electrical power output on the loading resistor included in an electric circuit attached to the piezoelectric electrodes. The curvature of beam mode shapes as well as deflection of the whole beam are examined using a high speed camera. The visual identification results are compared with the voltage output generated by the piezoelectric element for corresponding frequency sweeps and analyzed by the Hilbert transform.

Including long-range dependence in integrate-and-fire models of the high interspike-interval variability of cortical neurons.

PubMed

Jackson, B Scott

2004-10-01

Many different types of integrate-and-fire models have been designed in order to explain how it is possible for a cortical neuron to integrate over many independent inputs while still producing highly variable spike trains. Within this context, the variability of spike trains has been almost exclusively measured using the coefficient of variation of interspike intervals. However, another important statistical property that has been found in cortical spike trains and is closely associated with their high firing variability is long-range dependence. We investigate the conditions, if any, under which such models produce output spike trains with both interspike-interval variability and long-range dependence similar to those that have previously been measured from actual cortical neurons. We first show analytically that a large class of high-variability integrate-and-fire models is incapable of producing such outputs based on the fact that their output spike trains are always mathematically equivalent to renewal processes. This class of models subsumes a majority of previously published models, including those that use excitation-inhibition balance, correlated inputs, partial reset, or nonlinear leakage to produce outputs with high variability. Next, we study integrate-and-fire models that have (nonPoissonian) renewal point process inputs instead of the Poisson point process inputs used in the preceding class of models. The confluence of our analytical and simulation results implies that the renewal-input model is capable of producing high variability and long-range dependence comparable to that seen in spike trains recorded from cortical neurons, but only if the interspike intervals of the inputs have infinite variance, a physiologically unrealistic condition. Finally, we suggest a new integrate-and-fire model that does not suffer any of the previously mentioned shortcomings. By analyzing simulation results for this model, we show that it is capable of producing output spike trains with interspike-interval variability and long-range dependence that match empirical data from cortical spike trains. This model is similar to the other models in this study, except that its inputs are fractional-gaussian-noise-driven Poisson processes rather than renewal point processes. In addition to this model's success in producing realistic output spike trains, its inputs have long-range dependence similar to that found in most subcortical neurons in sensory pathways, including the inputs to cortex. Analysis of output spike trains from simulations of this model also shows that a tight balance between the amounts of excitation and inhibition at the inputs to cortical neurons is not necessary for high interspike-interval variability at their outputs. Furthermore, in our analysis of this model, we show that the superposition of many fractional-gaussian-noise-driven Poisson processes does not approximate a Poisson process, which challenges the common assumption that the total effect of a large number of inputs on a neuron is well represented by a Poisson process.

Adaptive optimal input design and parametric estimation of nonlinear dynamical systems: application to neuronal modeling.

PubMed

Madi, Mahmoud K; Karameh, Fadi N

2018-05-11

Many physical models of biological processes including neural systems are characterized by parametric nonlinear dynamical relations between driving inputs, internal states, and measured outputs of the process. Fitting such models using experimental data (data assimilation) is a challenging task since the physical process often operates in a noisy, possibly non-stationary environment; moreover, conducting multiple experiments under controlled and repeatable conditions can be impractical, time consuming or costly. The accuracy of model identification, therefore, is dictated principally by the quality and dynamic richness of collected data over single or few experimental sessions. Accordingly, it is highly desirable to design efficient experiments that, by exciting the physical process with smart inputs, yields fast convergence and increased accuracy of the model. We herein introduce an adaptive framework in which optimal input design is integrated with Square root Cubature Kalman Filters (OID-SCKF) to develop an online estimation procedure that first, converges significantly quicker, thereby permitting model fitting over shorter time windows, and second, enhances model accuracy when only few process outputs are accessible. The methodology is demonstrated on common nonlinear models and on a four-area neural mass model with noisy and limited measurements. Estimation quality (speed and accuracy) is benchmarked against high-performance SCKF-based methods that commonly employ dynamically rich informed inputs for accurate model identification. For all the tested models, simulated single-trial and ensemble averages showed that OID-SCKF exhibited (i) faster convergence of parameter estimates and (ii) lower dependence on inter-trial noise variability with gains up to around 1000 msec in speed and 81% increase in variability for the neural mass models. In terms of accuracy, OID-SCKF estimation was superior, and exhibited considerably less variability across experiments, in identifying model parameters of (a) systems with challenging model inversion dynamics and (b) systems with fewer measurable outputs that directly relate to the underlying processes. Fast and accurate identification therefore carries particular promise for modeling of transient (short-lived) neuronal network dynamics using a spatially under-sampled set of noisy measurements, as is commonly encountered in neural engineering applications. © 2018 IOP Publishing Ltd.

Bayesian nonlinear structural FE model and seismic input identification for damage assessment of civil structures

NASA Astrophysics Data System (ADS)

Astroza, Rodrigo; Ebrahimian, Hamed; Li, Yong; Conte, Joel P.

2017-09-01

A methodology is proposed to update mechanics-based nonlinear finite element (FE) models of civil structures subjected to unknown input excitation. The approach allows to jointly estimate unknown time-invariant model parameters of a nonlinear FE model of the structure and the unknown time histories of input excitations using spatially-sparse output response measurements recorded during an earthquake event. The unscented Kalman filter, which circumvents the computation of FE response sensitivities with respect to the unknown model parameters and unknown input excitations by using a deterministic sampling approach, is employed as the estimation tool. The use of measurement data obtained from arrays of heterogeneous sensors, including accelerometers, displacement sensors, and strain gauges is investigated. Based on the estimated FE model parameters and input excitations, the updated nonlinear FE model can be interrogated to detect, localize, classify, and assess damage in the structure. Numerically simulated response data of a three-dimensional 4-story 2-by-1 bay steel frame structure with six unknown model parameters subjected to unknown bi-directional horizontal seismic excitation, and a three-dimensional 5-story 2-by-1 bay reinforced concrete frame structure with nine unknown model parameters subjected to unknown bi-directional horizontal seismic excitation are used to illustrate and validate the proposed methodology. The results of the validation studies show the excellent performance and robustness of the proposed algorithm to jointly estimate unknown FE model parameters and unknown input excitations.

The determination of third order linear models from a seventh order nonlinear jet engine model

NASA Technical Reports Server (NTRS)

Lalonde, Rick J.; Hartley, Tom T.; De Abreu-Garcia, J. Alex

1989-01-01

Results are presented that demonstrate how good reduced-order models can be obtained directly by recursive parameter identification using input/output (I/O) data of high-order nonlinear systems. Three different methods of obtaining a third-order linear model from a seventh-order nonlinear turbojet engine model are compared. The first method is to obtain a linear model from the original model and then reduce the linear model by standard reduction techniques such as residualization and balancing. The second method is to identify directly a third-order linear model by recursive least-squares parameter estimation using I/O data of the original model. The third method is to obtain a reduced-order model from the original model and then linearize the reduced model. Frequency responses are used as the performance measure to evaluate the reduced models. The reduced-order models along with their Bode plots are presented for comparison purposes.

Multi input single output model predictive control of non-linear bio-polymerization process

DOE Office of Scientific and Technical Information (OSTI.GOV)

Arumugasamy, Senthil Kumar; Ahmad, Z.

This paper focuses on Multi Input Single Output (MISO) Model Predictive Control of bio-polymerization process in which mechanistic model is developed and linked with the feedforward neural network model to obtain a hybrid model (Mechanistic-FANN) of lipase-catalyzed ring-opening polymerization of ε-caprolactone (ε-CL) for Poly (ε-caprolactone) production. In this research, state space model was used, in which the input to the model were the reactor temperatures and reactor impeller speeds and the output were the molecular weight of polymer (M{sub n}) and polymer polydispersity index. State space model for MISO created using System identification tool box of Matlab™. This state spacemore » model is used in MISO MPC. Model predictive control (MPC) has been applied to predict the molecular weight of the biopolymer and consequently control the molecular weight of biopolymer. The result shows that MPC is able to track reference trajectory and give optimum movement of manipulated variable.« less

Attenuation, dispersion and nonlinearity effects in graphene-based waveguides

PubMed Central

Mota, João Cesar Moura; Sombra, Antonio Sergio Bezerra

2015-01-01

Summary We simulated and analyzed in detail the behavior of ultrashort optical pulses, which are typically used in telecommunications, propagating through graphene-based nanoribbon waveguides. In this work, we showed the changes that occur in the Gaussian and hyperbolic secant input pulses due to the attenuation, high-order dispersive effects and nonlinear effects. We concluded that it is possible to control the shape of the output pulses with the value of the input signal power and the chemical potential of the graphene nanoribbon. We believe that the obtained results will be highly relevant since they can be applied to other nanophotonic devices, for example, filters, modulators, antennas, switches and other devices. PMID:26171299

Motion video compression system with neural network having winner-take-all function

NASA Technical Reports Server (NTRS)

Fang, Wai-Chi (Inventor); Sheu, Bing J. (Inventor)

1997-01-01

A motion video data system includes a compression system, including an image compressor, an image decompressor correlative to the image compressor having an input connected to an output of the image compressor, a feedback summing node having one input connected to an output of the image decompressor, a picture memory having an input connected to an output of the feedback summing node, apparatus for comparing an image stored in the picture memory with a received input image and deducing therefrom pixels having differences between the stored image and the received image and for retrieving from the picture memory a partial image including the pixels only and applying the partial image to another input of the feedback summing node, whereby to produce at the output of the feedback summing node an updated decompressed image, a subtraction node having one input connected to received the received image and another input connected to receive the partial image so as to generate a difference image, the image compressor having an input connected to receive the difference image whereby to produce a compressed difference image at the output of the image compressor.

Quality factor concept in piezoceramic transformer performance description.

PubMed

Mezheritsky, Alex V

2006-02-01

A new general approach based on the quality factor concept to piezoceramic transformer (PT) performance description is proposed. The system's quality factor, material elastic anisotropy, and coupling factors of the input and output sections of an electrically excited and electrically loaded PT fully characterize its resonance and near-resonance behavior. The PT efficiency, transformation ratio, and input and output power were analytically analyzed and simulated as functions of the load and frequency for the simplest classical Langevin-type and Rosen-type PT designs. A new formulation of the electrical input impedance allows one to separate the power consumed by PT from the power transferred into the load. The system's PT quality factor takes into account losses in each PT "input-output-load" functional components. The loading process is changing PT input electrical impedance on the way that under loading the minimum series impedance is increasing and the maximum parallel impedance is decreasing coincidentally. The quality-factors ratio, between the states of fully loaded and nonloaded PT, is one of the best measures of PTs dynamic performance--practically, the lower the ratio is, the better PT efficiency. A simple and effective method for the loaded PT quality factor determination is proposed. As was found, a piezoceramic with low piezoelectric anisotropy is required to provide maximum PT efficiency and higher corresponding voltage gain. Limitations on the PT output voltage and power, caused by nonlinear effects in piezoceramics, were established.

Validation of a new modal performance measure for flexible controllers design

DOE Office of Scientific and Technical Information (OSTI.GOV)

Simo, J.B.; Tahan, S.A.; Kamwa, I.

1996-05-01

A new modal performance measure for power system stabilizer (PSS) optimization is proposed in this paper. The new method is based on modifying the square envelopes of oscillating modes, in order to take into account their damping ratios while minimizing the performance index. This criteria is applied to flexible controllers optimal design, on a multi-input-multi-output (MIMO) reduced-order model of a prototype power system. The multivariable model includes four generators, each having one input and one output. Linear time-response simulation and transient stability analysis with a nonlinear package confirm the superiority of the proposed criteria and illustrate its effectiveness in decentralizedmore » control.« less

All-optical phase discrimination using SOA.

PubMed

Power, Mark J; Webb, Roderick P; Manning, Robert J

2013-11-04

We describe the first experimental demonstration of a novel all-optical phase discrimination technique, which can separate the two orthogonal phase components of a signal onto different frequencies. This method exploits nonlinear mixing in a semiconductor optical amplifier (SOA) to separate a 10.65 Gbaud QPSK signal into two 10.65 Gb/s BPSK signals which are then demodulated using a delay interferometer (DI). Eye diagrams and spectral measurements verify correct operation and a conversion efficiency greater than 9 dB is observed on both output BPSK channels when compared with the input QPSK signal.

Fundamental Mechanisms of NeuroInformation Processing: Inverse Problems and Spike Processing

DTIC Science & Technology

2016-08-04

platform called Neurokernel for collaborative development of comprehensive models of the brain of the fruit fly Drosophila melanogaster and their execution...example. We investigated the following nonlinear identification problem: given both the input signal u and the time sequence (tk)k2Z at the output of...from a time sequence is to be contrasted with existing methods for rate-based models in neuroscience. In such models the output of the system is taken

Development of model reference adaptive control theory for electric power plant control applications

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mabius, L.E.

1982-09-15

The scope of this effort includes the theoretical development of a multi-input, multi-output (MIMO) Model Reference Control (MRC) algorithm, (i.e., model following control law), Model Reference Adaptive Control (MRAC) algorithm and the formulation of a nonlinear model of a typical electric power plant. Previous single-input, single-output MRAC algorithm designs have been generalized to MIMO MRAC designs using the MIMO MRC algorithm. This MRC algorithm, which has been developed using Command Generator Tracker methodologies, represents the steady state behavior (in the adaptive sense) of the MRAC algorithm. The MRC algorithm is a fundamental component in the MRAC design and stability analysis.more » An enhanced MRC algorithm, which has been developed for systems with more controls than regulated outputs, alleviates the MRC stability constraint of stable plant transmission zeroes. The nonlinear power plant model is based on the Cromby model with the addition of a governor valve management algorithm, turbine dynamics and turbine interactions with extraction flows. An application of the MRC algorithm to a linearization of this model demonstrates its applicability to power plant systems. In particular, the generated power changes at 7% per minute while throttle pressure and temperature, reheat temperature and drum level are held constant with a reasonable level of control. The enhanced algorithm reduces significantly control fluctuations without modifying the output response.« less

Identification of multivariable nonlinear systems in the presence of colored noises using iterative hierarchical least squares algorithm.

PubMed

Jafari, Masoumeh; Salimifard, Maryam; Dehghani, Maryam

2014-07-01

This paper presents an efficient method for identification of nonlinear Multi-Input Multi-Output (MIMO) systems in the presence of colored noises. The method studies the multivariable nonlinear Hammerstein and Wiener models, in which, the nonlinear memory-less block is approximated based on arbitrary vector-based basis functions. The linear time-invariant (LTI) block is modeled by an autoregressive moving average with exogenous (ARMAX) model which can effectively describe the moving average noises as well as the autoregressive and the exogenous dynamics. According to the multivariable nature of the system, a pseudo-linear-in-the-parameter model is obtained which includes two different kinds of unknown parameters, a vector and a matrix. Therefore, the standard least squares algorithm cannot be applied directly. To overcome this problem, a Hierarchical Least Squares Iterative (HLSI) algorithm is used to simultaneously estimate the vector and the matrix of unknown parameters as well as the noises. The efficiency of the proposed identification approaches are investigated through three nonlinear MIMO case studies. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Identification of nonlinear normal modes of engineering structures under broadband forcing

NASA Astrophysics Data System (ADS)

Noël, Jean-Philippe; Renson, L.; Grappasonni, C.; Kerschen, G.

2016-06-01

The objective of the present paper is to develop a two-step methodology integrating system identification and numerical continuation for the experimental extraction of nonlinear normal modes (NNMs) under broadband forcing. The first step processes acquired input and output data to derive an experimental state-space model of the structure. The second step converts this state-space model into a model in modal space from which NNMs are computed using shooting and pseudo-arclength continuation. The method is demonstrated using noisy synthetic data simulated on a cantilever beam with a hardening-softening nonlinearity at its free end.

Multivariable robust adaptive sliding mode control of an industrial boiler-turbine in the presence of modeling imprecisions and external disturbances: A comparison with type-I servo controller.

PubMed

Ghabraei, Soheil; Moradi, Hamed; Vossoughi, Gholamreza

2015-09-01

To guarantee the safety and efficient performance of the power plant, a robust controller for the boiler-turbine unit is needed. In this paper, a robust adaptive sliding mode controller (RASMC) is proposed to control a nonlinear multi-input multi-output (MIMO) model of industrial boiler-turbine unit, in the presence of unknown bounded uncertainties and external disturbances. To overcome the coupled nonlinearities and investigate the zero dynamics, input-output linearization is performed, and then the new decoupled inputs are derived. To tackle the uncertainties and external disturbances, appropriate adaption laws are introduced. For constructing the RASMC, suitable sliding surface is considered. To guarantee the sliding motion occurrence, appropriate control laws are constructed. Then the robustness and stability of the proposed RASMC is proved via Lyapunov stability theory. To compare the performance of the purposed RASMC with traditional control schemes, a type-I servo controller is designed. To evaluate the performance of the proposed control schemes, simulation studies on nonlinear MIMO dynamic system in the presence of high frequency bounded uncertainties and external disturbances are conducted and compared. Comparison of the results reveals the superiority of proposed RASMC over the traditional control schemes. RAMSC acts efficiently in disturbance rejection and keeping the system behavior in desirable tracking objectives, without the existence of unstable quasi-periodic solutions. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

An offline approach for output-only Bayesian identification of stochastic nonlinear systems using unscented Kalman filtering

NASA Astrophysics Data System (ADS)

Erazo, Kalil; Nagarajaiah, Satish

2017-06-01

In this paper an offline approach for output-only Bayesian identification of stochastic nonlinear systems is presented. The approach is based on a re-parameterization of the joint posterior distribution of the parameters that define a postulated state-space stochastic model class. In the re-parameterization the state predictive distribution is included, marginalized, and estimated recursively in a state estimation step using an unscented Kalman filter, bypassing state augmentation as required by existing online methods. In applications expectations of functions of the parameters are of interest, which requires the evaluation of potentially high-dimensional integrals; Markov chain Monte Carlo is adopted to sample the posterior distribution and estimate the expectations. The proposed approach is suitable for nonlinear systems subjected to non-stationary inputs whose realization is unknown, and that are modeled as stochastic processes. Numerical verification and experimental validation examples illustrate the effectiveness and advantages of the approach, including: (i) an increased numerical stability with respect to augmented-state unscented Kalman filtering, avoiding divergence of the estimates when the forcing input is unmeasured; (ii) the ability to handle arbitrary prior and posterior distributions. The experimental validation of the approach is conducted using data from a large-scale structure tested on a shake table. It is shown that the approach is robust to inherent modeling errors in the description of the system and forcing input, providing accurate prediction of the dynamic response when the excitation history is unknown.

Multi-Layered Feedforward Neural Networks for Image Segmentation

DTIC Science & Technology

1991-12-01

the Gram-Schmidt Network ...................... 80 xi Preface WILLIAM SHAKESPEARE 1564-1616 Is this a dagger which I see before me, The handle toward...any input-output mapping with a single hidden layer of non-linear nodes, the result may be like proving that a monkey could write Hamlet . Certainly it

Optimal control of nonlinear continuous-time systems in strict-feedback form.

PubMed

Zargarzadeh, Hassan; Dierks, Travis; Jagannathan, Sarangapani

2015-10-01

This paper proposes a novel optimal tracking control scheme for nonlinear continuous-time systems in strict-feedback form with uncertain dynamics. The optimal tracking problem is transformed into an equivalent optimal regulation problem through a feedforward adaptive control input that is generated by modifying the standard backstepping technique. Subsequently, a neural network-based optimal control scheme is introduced to estimate the cost, or value function, over an infinite horizon for the resulting nonlinear continuous-time systems in affine form when the internal dynamics are unknown. The estimated cost function is then used to obtain the optimal feedback control input; therefore, the overall optimal control input for the nonlinear continuous-time system in strict-feedback form includes the feedforward plus the optimal feedback terms. It is shown that the estimated cost function minimizes the Hamilton-Jacobi-Bellman estimation error in a forward-in-time manner without using any value or policy iterations. Finally, optimal output feedback control is introduced through the design of a suitable observer. Lyapunov theory is utilized to show the overall stability of the proposed schemes without requiring an initial admissible controller. Simulation examples are provided to validate the theoretical results.

Nonlinear Modeling and Control of a Propellant Mixer

NASA Technical Reports Server (NTRS)

Barbieri, Enrique; Richter, Hanz; Figueroa, Fernando

2003-01-01

A mixing chamber used in rocket engine combustion testing at NASA Stennis Space Center is modeled by a second order nonlinear MIMO system. The mixer is used to condition the thermodynamic properties of cryogenic liquid propellant by controlled injection of the same substance in the gaseous phase. The three inputs of the mixer are the positions of the valves regulating the liquid and gas flows at the inlets, and the position of the exit valve regulating the flow of conditioned propellant. The outputs to be tracked and/or regulated are mixer internal pressure, exit mass flow, and exit temperature. The outputs must conform to test specifications dictated by the type of rocket engine or component being tested downstream of the mixer. Feedback linearization is used to achieve tracking and regulation of the outputs. It is shown that the system is minimum-phase provided certain conditions on the parameters are satisfied. The conditions are shown to have physical interpretation.

Modeling the Afferent Dynamics of the Baroreflex Control System

PubMed Central

Mahdi, Adam; Sturdy, Jacob; Ottesen, Johnny T.; Olufsen, Mette S.

2013-01-01

In this study we develop a modeling framework for predicting baroreceptor firing rate as a function of blood pressure. We test models within this framework both quantitatively and qualitatively using data from rats. The models describe three components: arterial wall deformation, stimulation of mechanoreceptors located in the BR nerve-endings, and modulation of the action potential frequency. The three sub-systems are modeled individually following well-established biological principles. The first submodel, predicting arterial wall deformation, uses blood pressure as an input and outputs circumferential strain. The mechanoreceptor stimulation model, uses circumferential strain as an input, predicting receptor deformation as an output. Finally, the neural model takes receptor deformation as an input predicting the BR firing rate as an output. Our results show that nonlinear dependence of firing rate on pressure can be accounted for by taking into account the nonlinear elastic properties of the artery wall. This was observed when testing the models using multiple experiments with a single set of parameters. We find that to model the response to a square pressure stimulus, giving rise to post-excitatory depression, it is necessary to include an integrate-and-fire model, which allows the firing rate to cease when the stimulus falls below a given threshold. We show that our modeling framework in combination with sensitivity analysis and parameter estimation can be used to test and compare models. Finally, we demonstrate that our preferred model can exhibit all known dynamics and that it is advantageous to combine qualitative and quantitative analysis methods. PMID:24348231

Identification of Linear and Nonlinear Aerodynamic Impulse Responses Using Digital Filter Techniques

NASA Technical Reports Server (NTRS)

Silva, Walter A.

1997-01-01

This paper discusses the mathematical existence and the numerically-correct identification of linear and nonlinear aerodynamic impulse response functions. Differences between continuous-time and discrete-time system theories, which permit the identification and efficient use of these functions, will be detailed. Important input/output definitions and the concept of linear and nonlinear systems with memory will also be discussed. It will be shown that indicial (step or steady) responses (such as Wagner's function), forced harmonic responses (such as Theodorsen's function or those from doublet lattice theory), and responses to random inputs (such as gusts) can all be obtained from an aerodynamic impulse response function. This paper establishes the aerodynamic impulse response function as the most fundamental, and, therefore, the most computationally efficient, aerodynamic function that can be extracted from any given discrete-time, aerodynamic system. The results presented in this paper help to unify the understanding of classical two-dimensional continuous-time theories with modern three-dimensional, discrete-time theories. First, the method is applied to the nonlinear viscous Burger's equation as an example. Next the method is applied to a three-dimensional aeroelastic model using the CAP-TSD (Computational Aeroelasticity Program - Transonic Small Disturbance) code and then to a two-dimensional model using the CFL3D Navier-Stokes code. Comparisons of accuracy and computational cost savings are presented. Because of its mathematical generality, an important attribute of this methodology is that it is applicable to a wide range of nonlinear, discrete-time problems.

Identification of Linear and Nonlinear Aerodynamic Impulse Responses Using Digital Filter Techniques

NASA Technical Reports Server (NTRS)

Silva, Walter A.

1997-01-01

This paper discusses the mathematical existence and the numerically-correct identification of linear and nonlinear aerodynamic impulse response functions. Differences between continuous-time and discrete-time system theories, which permit the identification and efficient use of these functions, will be detailed. Important input/output definitions and the concept of linear and nonlinear systems with memory will also be discussed. It will be shown that indicial (step or steady) responses (such as Wagner's function), forced harmonic responses (such as Tbeodorsen's function or those from doublet lattice theory), and responses to random inputs (such as gusts) can all be obtained from an aerodynamic impulse response function. This paper establishes the aerodynamic impulse response function as the most fundamental, and, therefore, the most computationally efficient, aerodynamic function that can be extracted from any given discrete-time, aerodynamic system. The results presented in this paper help to unify the understanding of classical two-dimensional continuous-time theories with modem three-dimensional, discrete-time theories. First, the method is applied to the nonlinear viscous Burger's equation as an example. Next the method is applied to a three-dimensional aeroelastic model using the CAP-TSD (Computational Aeroelasticity Program - Transonic Small Disturbance) code and then to a two-dimensional model using the CFL3D Navier-Stokes code. Comparisons of accuracy and computational cost savings are presented. Because of its mathematical generality, an important attribute of this methodology is that it is applicable to a wide range of nonlinear, discrete-time problems.

Cooperative Adaptive Output Regulation for Second-Order Nonlinear Multiagent Systems With Jointly Connected Switching Networks.

PubMed

Liu, Wei; Huang, Jie

2018-03-01

This paper studies the cooperative global robust output regulation problem for a class of heterogeneous second-order nonlinear uncertain multiagent systems with jointly connected switching networks. The main contributions consist of the following three aspects. First, we generalize the result of the adaptive distributed observer from undirected jointly connected switching networks to directed jointly connected switching networks. Second, by performing a new coordinate and input transformation, we convert our problem into the cooperative global robust stabilization problem of a more complex augmented system via the distributed internal model principle. Third, we solve the stabilization problem by a distributed state feedback control law. Our result is illustrated by the leader-following consensus problem for a group of Van der Pol oscillators.

Nonlinear interferometry approach to photonic sequential logic

NASA Astrophysics Data System (ADS)

Mabuchi, Hideo

2011-10-01

Motivated by rapidly advancing capabilities for extensive nanoscale patterning of optical materials, I propose an approach to implementing photonic sequential logic that exploits circuit-scale phase coherence for efficient realizations of fundamental components such as a NAND-gate-with-fanout and a bistable latch. Kerr-nonlinear optical resonators are utilized in combination with interference effects to drive the binary logic. Quantum-optical input-output models are characterized numerically using design parameters that yield attojoule-scale energy separation between the latch states.

Formalism of photons in a nonlinear microring resonator

NASA Astrophysics Data System (ADS)

Tran, Quang Loc; Yupapin, Preecha

2018-03-01

In this paper, using short Gaussian pulses input from a monochromatic light source, we simulate the photon distribution and analyse the output gate's signals of PANDA nonlinear ring resonator. The present analysis is restricted to directional couplers characterized by two parameters, the power coupling coefficient κ and power coupling loss γ. Add/drop filters are also employed and investigated for the suitable to implement in the practical communication system. The experiment was conducted by using the combination of Lumerical FDTD Solutions and Lumerical MODE Solutions software.

Nonlinear system theory: another look at dependence.

PubMed

Wu, Wei Biao

2005-10-04

Based on the nonlinear system theory, we introduce previously undescribed dependence measures for stationary causal processes. Our physical and predictive dependence measures quantify the degree of dependence of outputs on inputs in physical systems. The proposed dependence measures provide a natural framework for a limit theory for stationary processes. In particular, under conditions with quite simple forms, we present limit theorems for partial sums, empirical processes, and kernel density estimates. The conditions are mild and easily verifiable because they are directly related to the data-generating mechanisms.

A nonlinear control scheme based on dynamic evolution path theory for improved dynamic performance of boost PFC converter working on nonlinear features.

PubMed

Mohanty, Pratap Ranjan; Panda, Anup Kumar

2016-11-01

This paper is concerned to performance improvement of boost PFC converter under large random load fluctuation, ensuring unity power factor (UPF) at source end and regulated voltage at load side. To obtain such performance, a nonlinear controller based on dynamic evolution path theory is designed and its robustness is examined under both heavy and light loading condition. In this paper, %THD and zero-cross-over dead-zone of input current is significantly reduced. Also, very less response time of input current and output voltage to that of load and reference variation is remarked. A simulation model of proposed system is designed and it is realized using dSPACE 1104 signal processor for a 390V DC , 500W prototype. The relevant experimental and simulation waveforms are presented. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Analysis of Discontinuity Induced Bifurcations in a Dual Input DC-DC Converter

NASA Astrophysics Data System (ADS)

Giaouris, Damian; Banerjee, Soumitro; Mandal, Kuntal; Al-Hindawi, Mohammed M.; Abusorrah, Abdullah; Al-Turki, Yusuf; El Aroudi, Abdelali

DC-DC power converters with multiple inputs and a single output are used in numerous applications where multiple sources, e.g. two or more renewable energy sources and/or a battery, feed a single load. In this work, a classical boost converter topology with two input branches connected to two different sources is chosen, with each branch independently being controlled by a separate peak current mode controller. We demonstrate for the first time that even though this converter is similar to other well known topologies that have been studied before, it exhibits many complex nonlinear behaviors that are not found in any other standard PWM controlled power converter. The system undergoes period incrementing cascade as a parameter is varied, with discontinuous hard transitions between consecutive periodicities. We show that the system can be described by a discontinuous map, which explains the observed bifurcation phenomena. The results have been experimentally validated.

Modeling the control of the central nervous system over the cardiovascular system using support vector machines.

PubMed

Díaz, José; Acosta, Jesús; González, Rafael; Cota, Juan; Sifuentes, Ernesto; Nebot, Àngela

2018-02-01

The control of the central nervous system (CNS) over the cardiovascular system (CS) has been modeled using different techniques, such as fuzzy inductive reasoning, genetic fuzzy systems, neural networks, and nonlinear autoregressive techniques; the results obtained so far have been significant, but not solid enough to describe the control response of the CNS over the CS. In this research, support vector machines (SVMs) are used to predict the response of a branch of the CNS, specifically, the one that controls an important part of the cardiovascular system. To do this, five models are developed to emulate the output response of five controllers for the same input signal, the carotid sinus blood pressure (CSBP). These controllers regulate parameters such as heart rate, myocardial contractility, peripheral and coronary resistance, and venous tone. The models are trained using a known set of input-output response in each controller; also, there is a set of six input-output signals for testing each proposed model. The input signals are processed using an all-pass filter, and the accuracy performance of the control models is evaluated using the percentage value of the normalized mean square error (MSE). Experimental results reveal that SVM models achieve a better estimation of the dynamical behavior of the CNS control compared to others modeling systems. The main results obtained show that the best case is for the peripheral resistance controller, with a MSE of 1.20e-4%, while the worst case is for the heart rate controller, with a MSE of 1.80e-3%. These novel models show a great reliability in fitting the output response of the CNS which can be used as an input to the hemodynamic system models in order to predict the behavior of the heart and blood vessels in response to blood pressure variations. Copyright © 2017 Elsevier Ltd. All rights reserved.

Adaptive output feedback NN control of a class of discrete-time MIMO nonlinear systems with unknown control directions.

PubMed

Li, Yanan; Yang, Chenguang; Ge, Shuzhi Sam; Lee, Tong Heng

2011-04-01

In this paper, adaptive neural network (NN) control is investigated for a class of block triangular multiinput-multioutput nonlinear discrete-time systems with each subsystem in pure-feedback form with unknown control directions. These systems are of couplings in every equation of each subsystem, and different subsystems may have different orders. To avoid the noncausal problem in the control design, the system is transformed into a predictor form by rigorous derivation. By exploring the properties of the block triangular form, implicit controls are developed for each subsystem such that the couplings of inputs and states among subsystems have been completely decoupled. The radial basis function NN is employed to approximate the unknown control. Each subsystem achieves a semiglobal uniformly ultimately bounded stability with the proposed control, and simulation results are presented to demonstrate its efficiency.

A third-order class-D amplifier with and without ripple compensation

NASA Astrophysics Data System (ADS)

Cox, Stephen M.; du Toit Mouton, H.

2018-06-01

We analyse the nonlinear behaviour of a third-order class-D amplifier, and demonstrate the remarkable effectiveness of the recently introduced ripple compensation (RC) technique in reducing the audio distortion of the device. The amplifier converts an input audio signal to a high-frequency train of rectangular pulses, whose widths are modulated according to the input signal (pulse-width modulation) and employs negative feedback. After determining the steady-state operating point for constant input and calculating its stability, we derive a small-signal model (SSM), which yields in closed form the transfer function relating (infinitesimal) input and output disturbances. This SSM shows how the RC technique is able to linearise the small-signal response of the device. We extend this SSM through a fully nonlinear perturbation calculation of the dynamics of the amplifier, based on the disparity in time scales between the pulse train and the audio signal. We obtain the nonlinear response of the amplifier to a general audio signal, avoiding the linearisation inherent in the SSM; we thereby more precisely quantify the reduction in distortion achieved through RC. Finally, simulations corroborate our theoretical predictions and illustrate the dramatic deterioration in performance that occurs when the amplifier is operated in an unstable regime. The perturbation calculation is rather general, and may be adapted to quantify the way in which other nonlinear negative-feedback pulse-modulated devices track a time-varying input signal that slowly modulates the system parameters.

Study of Environmental Data Complexity using Extreme Learning Machine

NASA Astrophysics Data System (ADS)

Leuenberger, Michael; Kanevski, Mikhail

2017-04-01

The main goals of environmental data science using machine learning algorithm deal, in a broad sense, around the calibration, the prediction and the visualization of hidden relationship between input and output variables. In order to optimize the models and to understand the phenomenon under study, the characterization of the complexity (at different levels) should be taken into account. Therefore, the identification of the linear or non-linear behavior between input and output variables adds valuable information for the knowledge of the phenomenon complexity. The present research highlights and investigates the different issues that can occur when identifying the complexity (linear/non-linear) of environmental data using machine learning algorithm. In particular, the main attention is paid to the description of a self-consistent methodology for the use of Extreme Learning Machines (ELM, Huang et al., 2006), which recently gained a great popularity. By applying two ELM models (with linear and non-linear activation functions) and by comparing their efficiency, quantification of the linearity can be evaluated. The considered approach is accompanied by simulated and real high dimensional and multivariate data case studies. In conclusion, the current challenges and future development in complexity quantification using environmental data mining are discussed. References - Huang, G.-B., Zhu, Q.-Y., Siew, C.-K., 2006. Extreme learning machine: theory and applications. Neurocomputing 70 (1-3), 489-501. - Kanevski, M., Pozdnoukhov, A., Timonin, V., 2009. Machine Learning for Spatial Environmental Data. EPFL Press; Lausanne, Switzerland, p.392. - Leuenberger, M., Kanevski, M., 2015. Extreme Learning Machines for spatial environmental data. Computers and Geosciences 85, 64-73.

Adaptive control of Parkinson's state based on a nonlinear computational model with unknown parameters.

PubMed

Su, Fei; Wang, Jiang; Deng, Bin; Wei, Xi-Le; Chen, Ying-Yuan; Liu, Chen; Li, Hui-Yan

2015-02-01

The objective here is to explore the use of adaptive input-output feedback linearization method to achieve an improved deep brain stimulation (DBS) algorithm for closed-loop control of Parkinson's state. The control law is based on a highly nonlinear computational model of Parkinson's disease (PD) with unknown parameters. The restoration of thalamic relay reliability is formulated as the desired outcome of the adaptive control methodology, and the DBS waveform is the control input. The control input is adjusted in real time according to estimates of unknown parameters as well as the feedback signal. Simulation results show that the proposed adaptive control algorithm succeeds in restoring the relay reliability of the thalamus, and at the same time achieves accurate estimation of unknown parameters. Our findings point to the potential value of adaptive control approach that could be used to regulate DBS waveform in more effective treatment of PD.

Adaptive fuzzy prescribed performance control for MIMO nonlinear systems with unknown control direction and unknown dead-zone inputs.

PubMed

Shi, Wuxi; Luo, Rui; Li, Baoquan

2017-01-01

In this study, an adaptive fuzzy prescribed performance control approach is developed for a class of uncertain multi-input and multi-output (MIMO) nonlinear systems with unknown control direction and unknown dead-zone inputs. The properties of symmetric matrix are exploited to design adaptive fuzzy prescribed performance controller, and a Nussbaum-type function is incorporated in the controller to estimate the unknown control direction. This method has two prominent advantages: it does not require the priori knowledge of control direction and only three parameters need to be updated on-line for this MIMO systems. It is proved that all the signals in the resulting closed-loop system are bounded and that the tracking errors converge to a small residual set with the prescribed performance bounds. The effectiveness of the proposed approach is validated by simulation results. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Preserving information in neural transmission.

PubMed

Sincich, Lawrence C; Horton, Jonathan C; Sharpee, Tatyana O

2009-05-13

Along most neural pathways, the spike trains transmitted from one neuron to the next are altered. In the process, neurons can either achieve a more efficient stimulus representation, or extract some biologically important stimulus parameter, or succeed at both. We recorded the inputs from single retinal ganglion cells and the outputs from connected lateral geniculate neurons in the macaque to examine how visual signals are relayed from retina to cortex. We found that geniculate neurons re-encoded multiple temporal stimulus features to yield output spikes that carried more information about stimuli than was available in each input spike. The coding transformation of some relay neurons occurred with no decrement in information rate, despite output spike rates that averaged half the input spike rates. This preservation of transmitted information was achieved by the short-term summation of inputs that geniculate neurons require to spike. A reduced model of the retinal and geniculate visual responses, based on two stimulus features and their associated nonlinearities, could account for >85% of the total information available in the spike trains and the preserved information transmission. These results apply to neurons operating on a single time-varying input, suggesting that synaptic temporal integration can alter the temporal receptive field properties to create a more efficient representation of visual signals in the thalamus than the retina.

iTOUGH2 v7.1

DOE Office of Scientific and Technical Information (OSTI.GOV)

FINSTERLE, STEFAN; JUNG, YOOJIN; KOWALSKY, MICHAEL

2016-09-15

iTOUGH2 (inverse TOUGH2) provides inverse modeling capabilities for TOUGH2, a simulator for multi-dimensional, multi-phase, multi-component, non-isothermal flow and transport in fractured porous media. iTOUGH2 performs sensitivity analyses, data-worth analyses, parameter estimation, and uncertainty propagation analyses in geosciences and reservoir engineering and other application areas. iTOUGH2 supports a number of different combinations of fluids and components (equation-of-state (EOS) modules). In addition, the optimization routines implemented in iTOUGH2 can also be used for sensitivity analysis, automatic model calibration, and uncertainty quantification of any external code that uses text-based input and output files using the PEST protocol. iTOUGH2 solves the inverse problem bymore » minimizing a non-linear objective function of the weighted differences between model output and the corresponding observations. Multiple minimization algorithms (derivative-free, gradient-based, and second-order; local and global) are available. iTOUGH2 also performs Latin Hypercube Monte Carlo simulations for uncertainty propagation analyses. A detailed residual and error analysis is provided. This upgrade includes (a) global sensitivity analysis methods, (b) dynamic memory allocation (c) additional input features and output analyses, (d) increased forward simulation capabilities, (e) parallel execution on multicore PCs and Linux clusters, and (f) bug fixes. More details can be found at http://esd.lbl.gov/iTOUGH2.« less

Measuring efficiency of university-industry Ph.D. projects using best worst method.

PubMed

Salimi, Negin; Rezaei, Jafar

A collaborative Ph.D. project, carried out by a doctoral candidate, is a type of collaboration between university and industry. Due to the importance of such projects, researchers have considered different ways to evaluate the success, with a focus on the outputs of these projects. However, what has been neglected is the other side of the coin-the inputs. The main aim of this study is to incorporate both the inputs and outputs of these projects into a more meaningful measure called efficiency. A ratio of the weighted sum of outputs over the weighted sum of inputs identifies the efficiency of a Ph.D. The weights of the inputs and outputs can be identified using a multi-criteria decision-making (MCDM) method. Data on inputs and outputs are collected from 51 Ph.D. candidates who graduated from Eindhoven University of Technology. The weights are identified using a new MCDM method called Best Worst Method (BWM). Because there may be differences in the opinion of Ph.D. candidates and supervisors on weighing the inputs and outputs, data for BWM are collected from both groups. It is interesting to see that there are differences in the level of efficiency from the two perspectives, because of the weight differences. Moreover, a comparison between the efficiency scores of these projects and their success scores reveals differences that may have significant implications. A sensitivity analysis divulges the most contributing inputs and outputs.

Piecewise compensation for the nonlinear error of fiber-optic gyroscope scale factor

NASA Astrophysics Data System (ADS)

Zhang, Yonggang; Wu, Xunfeng; Yuan, Shun; Wu, Lei

2013-08-01

Fiber-Optic Gyroscope (FOG) scale factor nonlinear error will result in errors in Strapdown Inertial Navigation System (SINS). In order to reduce nonlinear error of FOG scale factor in SINS, a compensation method is proposed in this paper based on curve piecewise fitting of FOG output. Firstly, reasons which can result in FOG scale factor error are introduced and the definition of nonlinear degree is provided. Then we introduce the method to divide the output range of FOG into several small pieces, and curve fitting is performed in each output range of FOG to obtain scale factor parameter. Different scale factor parameters of FOG are used in different pieces to improve FOG output precision. These parameters are identified by using three-axis turntable, and nonlinear error of FOG scale factor can be reduced. Finally, three-axis swing experiment of SINS verifies that the proposed method can reduce attitude output errors of SINS by compensating the nonlinear error of FOG scale factor and improve the precision of navigation. The results of experiments also demonstrate that the compensation scheme is easy to implement. It can effectively compensate the nonlinear error of FOG scale factor with slightly increased computation complexity. This method can be used in inertial technology based on FOG to improve precision.

Nonlinear versus Ordinary Adaptive Control of Continuous Stirred-Tank Reactor

PubMed Central

Dostal, Petr

2015-01-01

Unfortunately, the major group of the systems in industry has nonlinear behavior and control of such processes with conventional control approaches with fixed parameters causes problems and suboptimal or unstable control results. An adaptive control is one way to how we can cope with nonlinearity of the system. This contribution compares classic adaptive control and its modification with Wiener system. This configuration divides nonlinear controller into the dynamic linear part and the static nonlinear part. The dynamic linear part is constructed with the use of polynomial synthesis together with the pole-placement method and the spectral factorization. The static nonlinear part uses static analysis of the controlled plant for introducing the mathematical nonlinear description of the relation between the controlled output and the change of the control input. Proposed controller is tested by the simulations on the mathematical model of the continuous stirred-tank reactor with cooling in the jacket as a typical nonlinear system. PMID:26346878

Nonlinear analysis of a rotor-bearing system using describing functions

NASA Astrophysics Data System (ADS)

Maraini, Daniel; Nataraj, C.

2018-04-01

This paper presents a technique for modelling the nonlinear behavior of a rotor-bearing system with Hertzian contact, clearance, and rotating unbalance. The rotor-bearing system is separated into linear and nonlinear components, and the nonlinear bearing force is replaced with an equivalent describing function gain. The describing function captures the relationship between the amplitude of the fundamental input to the nonlinearity and the fundamental output. The frequency response is constructed for various values of the clearance parameter, and the results show the presence of a jump resonance in bearings with both clearance and preload. Nonlinear hardening type behavior is observed in the case with clearance and softening behavior is observed for the case with preload. Numerical integration is also carried out on the nonlinear equations of motion showing strong agreement with the approximate solution. This work could easily be extended to include additional nonlinearities that arise from defects, providing a powerful diagnostic tool.

A novel interval type-2 fractional order fuzzy PID controller: Design, performance evaluation, and its optimal time domain tuning.

PubMed

Kumar, Anupam; Kumar, Vijay

2017-05-01

In this paper, a novel concept of an interval type-2 fractional order fuzzy PID (IT2FO-FPID) controller, which requires fractional order integrator and fractional order differentiator, is proposed. The incorporation of Takagi-Sugeno-Kang (TSK) type interval type-2 fuzzy logic controller (IT2FLC) with fractional controller of PID-type is investigated for time response measure due to both unit step response and unit load disturbance. The resulting IT2FO-FPID controller is examined on different delayed linear and nonlinear benchmark plants followed by robustness analysis. In order to design this controller, fractional order integrator-differentiator operators are considered as design variables including input-output scaling factors. A new hybridized algorithm named as artificial bee colony-genetic algorithm (ABC-GA) is used to optimize the parameters of the controller while minimizing weighted sum of integral of time absolute error (ITAE) and integral of square of control output (ISCO). To assess the comparative performance of the IT2FO-FPID, authors compared it against existing controllers, i.e., interval type-2 fuzzy PID (IT2-FPID), type-1 fractional order fuzzy PID (T1FO-FPID), type-1 fuzzy PID (T1-FPID), and conventional PID controllers. Furthermore, to show the effectiveness of the proposed controller, the perturbed processes along with the larger dead time are tested. Moreover, the proposed controllers are also implemented on multi input multi output (MIMO), coupled, and highly complex nonlinear two-link robot manipulator system in presence of un-modeled dynamics. Finally, the simulation results explicitly indicate that the performance of the proposed IT2FO-FPID controller is superior to its conventional counterparts in most of the cases. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Sliding Mode Control of a Thermal Mixing Process

NASA Technical Reports Server (NTRS)

Richter, Hanz; Figueroa, Fernando

2004-01-01

In this paper we consider the robust control of a thermal mixer using multivariable Sliding Mode Control (SMC). The mixer consists of a mixing chamber, hot and cold fluid valves, and an exit valve. The commanded positions of the three valves are the available control inputs, while the controlled variables are total mass flow rate, chamber pressure and the density of the mixture inside the chamber. Unsteady thermodynamics and linear valve models are used in deriving a 5th order nonlinear system with three inputs and three outputs, An SMC controller is designed to achieve robust output tracking in the presence of unknown energy losses between the chamber and the environment. The usefulness of the technique is illustrated with a simulation.

An efficient approach to ARMA modeling of biological systems with multiple inputs and delays

NASA Technical Reports Server (NTRS)

Perrott, M. H.; Cohen, R. J.

1996-01-01

This paper presents a new approach to AutoRegressive Moving Average (ARMA or ARX) modeling which automatically seeks the best model order to represent investigated linear, time invariant systems using their input/output data. The algorithm seeks the ARMA parameterization which accounts for variability in the output of the system due to input activity and contains the fewest number of parameters required to do so. The unique characteristics of the proposed system identification algorithm are its simplicity and efficiency in handling systems with delays and multiple inputs. We present results of applying the algorithm to simulated data and experimental biological data In addition, a technique for assessing the error associated with the impulse responses calculated from estimated ARMA parameterizations is presented. The mapping from ARMA coefficients to impulse response estimates is nonlinear, which complicates any effort to construct confidence bounds for the obtained impulse responses. Here a method for obtaining a linearization of this mapping is derived, which leads to a simple procedure to approximate the confidence bounds.

Analysis of Pull-In Instability of Geometrically Nonlinear Microbeam Using Radial Basis Artificial Neural Network Based on Couple Stress Theory

PubMed Central

Heidari, Mohammad; Heidari, Ali; Homaei, Hadi

2014-01-01

The static pull-in instability of beam-type microelectromechanical systems (MEMS) is theoretically investigated. Two engineering cases including cantilever and double cantilever microbeam are considered. Considering the midplane stretching as the source of the nonlinearity in the beam behavior, a nonlinear size-dependent Euler-Bernoulli beam model is used based on a modified couple stress theory, capable of capturing the size effect. By selecting a range of geometric parameters such as beam lengths, width, thickness, gaps, and size effect, we identify the static pull-in instability voltage. A MAPLE package is employed to solve the nonlinear differential governing equations to obtain the static pull-in instability voltage of microbeams. Radial basis function artificial neural network with two functions has been used for modeling the static pull-in instability of microcantilever beam. The network has four inputs of length, width, gap, and the ratio of height to scale parameter of beam as the independent process variables, and the output is static pull-in voltage of microbeam. Numerical data, employed for training the network, and capabilities of the model have been verified in predicting the pull-in instability behavior. The output obtained from neural network model is compared with numerical results, and the amount of relative error has been calculated. Based on this verification error, it is shown that the radial basis function of neural network has the average error of 4.55% in predicting pull-in voltage of cantilever microbeam. Further analysis of pull-in instability of beam under different input conditions has been investigated and comparison results of modeling with numerical considerations shows a good agreement, which also proves the feasibility and effectiveness of the adopted approach. The results reveal significant influences of size effect and geometric parameters on the static pull-in instability voltage of MEMS. PMID:24860602

Adaptive Model Predictive Control of Diesel Engine Selective Catalytic Reduction (SCR) Systems

ERIC Educational Resources Information Center

McKinley, Thomas L.

2009-01-01

Selective catalytic reduction or SCR is coming into worldwide use for diesel engine emissions reduction for on- and off-highway vehicles. These applications are characterized by broad operating range as well as rapid and unpredictable changes in operating conditions. Significant nonlinearity, input and output constraints, and stringent performance…

Data-based virtual unmodeled dynamics driven multivariable nonlinear adaptive switching control.

PubMed

Chai, Tianyou; Zhang, Yajun; Wang, Hong; Su, Chun-Yi; Sun, Jing

2011-12-01

For a complex industrial system, its multivariable and nonlinear nature generally make it very difficult, if not impossible, to obtain an accurate model, especially when the model structure is unknown. The control of this class of complex systems is difficult to handle by the traditional controller designs around their operating points. This paper, however, explores the concepts of controller-driven model and virtual unmodeled dynamics to propose a new design framework. The design consists of two controllers with distinct functions. First, using input and output data, a self-tuning controller is constructed based on a linear controller-driven model. Then the output signals of the controller-driven model are compared with the true outputs of the system to produce so-called virtual unmodeled dynamics. Based on the compensator of the virtual unmodeled dynamics, the second controller based on a nonlinear controller-driven model is proposed. Those two controllers are integrated by an adaptive switching control algorithm to take advantage of their complementary features: one offers stabilization function and another provides improved performance. The conditions on the stability and convergence of the closed-loop system are analyzed. Both simulation and experimental tests on a heavily coupled nonlinear twin-tank system are carried out to confirm the effectiveness of the proposed method.

Solving the two-dimensional Fokker-Planck equation for strongly correlated neurons

NASA Astrophysics Data System (ADS)

Deniz, Taşkın; Rotter, Stefan

2017-01-01

Pairs of neurons in brain networks often share much of the input they receive from other neurons. Due to essential nonlinearities of the neuronal dynamics, the consequences for the correlation of the output spike trains are generally not well understood. Here we analyze the case of two leaky integrate-and-fire neurons using an approach which is nonperturbative with respect to the degree of input correlation. Our treatment covers both weakly and strongly correlated dynamics, generalizing previous results based on linear response theory.

An Asynchronous Recurrent Network of Cellular Automaton-Based Neurons and Its Reproduction of Spiking Neural Network Activities.

PubMed

Matsubara, Takashi; Torikai, Hiroyuki

2016-04-01

Modeling and implementation approaches for the reproduction of input-output relationships in biological nervous tissues contribute to the development of engineering and clinical applications. However, because of high nonlinearity, the traditional modeling and implementation approaches encounter difficulties in terms of generalization ability (i.e., performance when reproducing an unknown data set) and computational resources (i.e., computation time and circuit elements). To overcome these difficulties, asynchronous cellular automaton-based neuron (ACAN) models, which are described as special kinds of cellular automata that can be implemented as small asynchronous sequential logic circuits have been proposed. This paper presents a novel type of such ACAN and a theoretical analysis of its excitability. This paper also presents a novel network of such neurons, which can mimic input-output relationships of biological and nonlinear ordinary differential equation model neural networks. Numerical analyses confirm that the presented network has a higher generalization ability than other major modeling and implementation approaches. In addition, Field-Programmable Gate Array-implementations confirm that the presented network requires lower computational resources.

High temperature composite analyzer (HITCAN) user's manual, version 1.0

NASA Technical Reports Server (NTRS)

Lackney, J. J.; Singhal, S. N.; Murthy, P. L. N.; Gotsis, P.

1993-01-01

This manual describes 'how-to-use' the computer code, HITCAN (HIgh Temperature Composite ANalyzer). HITCAN is a general purpose computer program for predicting nonlinear global structural and local stress-strain response of arbitrarily oriented, multilayered high temperature metal matrix composite structures. This code combines composite mechanics and laminate theory with an internal data base for material properties of the constituents (matrix, fiber and interphase). The thermo-mechanical properties of the constituents are considered to be nonlinearly dependent on several parameters including temperature, stress and stress rate. The computation procedure for the analysis of the composite structures uses the finite element method. HITCAN is written in FORTRAN 77 computer language and at present has been configured and executed on the NASA Lewis Research Center CRAY XMP and YMP computers. This manual describes HlTCAN's capabilities and limitations followed by input/execution/output descriptions and example problems. The input is described in detail including (1) geometry modeling, (2) types of finite elements, (3) types of analysis, (4) material data, (5) types of loading, (6) boundary conditions, (7) output control, (8) program options, and (9) data bank.

Optimization of light quality from color mixing light-emitting diode systems for general lighting

NASA Astrophysics Data System (ADS)

Thorseth, Anders

2012-03-01

Given the problem of metamerisms inherent in color mixing in light-emitting diode (LED) systems with more than three distinct colors, a method for optimizing the spectral output of multicolor LED system with regards to standardized light quality parameters has been developed. The composite spectral power distribution from the LEDs are simulated using spectral radiometric measurements of single commercially available LEDs for varying input power, to account for the efficiency droop and other non-linear effects in electrical power vs. light output. The method uses electrical input powers as input parameters in a randomized steepest decent optimization. The resulting spectral power distributions are evaluated with regard to the light quality using the standard characteristics: CIE color rendering index, correlated color temperature and chromaticity distance. The results indicate Pareto optimal boundaries for each system, mapping the capabilities of the simulated lighting systems with regard to the light quality characteristics.

A comprehensive study of the delay vector variance method for quantification of nonlinearity in dynamical systems

PubMed Central

Mandic, D. P.; Ryan, K.; Basu, B.; Pakrashi, V.

2016-01-01

Although vibration monitoring is a popular method to monitor and assess dynamic structures, quantification of linearity or nonlinearity of the dynamic responses remains a challenging problem. We investigate the delay vector variance (DVV) method in this regard in a comprehensive manner to establish the degree to which a change in signal nonlinearity can be related to system nonlinearity and how a change in system parameters affects the nonlinearity in the dynamic response of the system. A wide range of theoretical situations are considered in this regard using a single degree of freedom (SDOF) system to obtain numerical benchmarks. A number of experiments are then carried out using a physical SDOF model in the laboratory. Finally, a composite wind turbine blade is tested for different excitations and the dynamic responses are measured at a number of points to extend the investigation to continuum structures. The dynamic responses were measured using accelerometers, strain gauges and a Laser Doppler vibrometer. This comprehensive study creates a numerical and experimental benchmark for structurally dynamical systems where output-only information is typically available, especially in the context of DVV. The study also allows for comparative analysis between different systems driven by the similar input. PMID:26909175

Wideband electromagnetic energy harvesting from ambient vibrations

NASA Astrophysics Data System (ADS)

Mallick, Dhiman; Podder, Pranay; Roy, Saibal

2015-06-01

Different bandwidth widening schemes of electromagnetic energy harvesters have been reported in this work. The devices are fabricated on FR4 substrate using laser micromachining techniques. The linear device operate in a narrow band around the resonance; in order to tune resonant frequency of the device electrically, two different types of complex load topologies are adopted. Using capacitive load, the resonant frequency is tuned in the low frequency direction whereas using inductive load, the resonant frequency is tuned in the high frequency direction. An overall tuning range of ˜2.4 Hz is obtained at 0.3g though the output power dropped significantly over the tuning range. In order to improve the off-resonance performance, nonlinear oscillation based systems are adopted. A specially designed spring arm with fixed-guided configuration produced single well nonlinear monostable configuration. With increasing input acceleration, wider bandwidth is obtained with such a system as large displacement, stretching nonlinearity comes into play and 9.55 Hz bandwidth is obtained at 0.5g. The repulsive force between one static and one vibrating oppositely polarized magnets are used to generate bistable nonlinear potential system. The distance between the mentioned magnets is varied between 4 to 10 mm to produce tunable nonlinearity with a maximum half power bandwidth over 3 Hz at 0.5g.

Control theory-based regulation of hippocampal CA1 nonlinear dynamics.

PubMed

Hsiao, Min-Chi; Song, Dong; Berger, Theodore W

2008-01-01

We are developing a biomimetic electronic neural prosthesis to replace regions of the hippocampal brain area that have been damaged by disease or insult. Our previous study has shown that the VLSI implementation of a CA3 nonlinear dynamic model can functionally replace the CA3 subregion of the hippocampal slice. As a result, the propagation of temporal patterns of activity from DG-->VLSI-->CA1 reproduces the activity observed experimentally in the biological DG-->CA3-->CA1 circuit. In this project, we incorporate an open-loop controller to optimize the output (CA1) response. Specifically, we seek to optimize the stimulation signal to CA1 using a predictive dentate gyrus (DG)-CA1 nonlinear model (i.e., DG-CA1 trajectory model) and a CA1 input-output model (i.e., CA1 plant model), such that the ultimate CA1 response (i.e., desired output) can be first predicted by the DG-CA1 trajectory model and then transformed to the desired stimulation through the inversed CA1 plant model. Lastly, the desired CA1 output is evoked by the estimated optimal stimulation. This study will be the first stage of formulating an integrated modeling-control strategy for the hippocampal neural prosthetic system.

New Uses for Sensitivity Analysis: How Different Movement Tasks Effect Limb Model Parameter Sensitivity

NASA Technical Reports Server (NTRS)

Winters, J. M.; Stark, L.

1984-01-01

Original results for a newly developed eight-order nonlinear limb antagonistic muscle model of elbow flexion and extension are presented. A wider variety of sensitivity analysis techniques are used and a systematic protocol is established that shows how the different methods can be used efficiently to complement one another for maximum insight into model sensitivity. It is explicitly shown how the sensitivity of output behaviors to model parameters is a function of the controller input sequence, i.e., of the movement task. When the task is changed (for instance, from an input sequence that results in the usual fast movement task to a slower movement that may also involve external loading, etc.) the set of parameters with high sensitivity will in general also change. Such task-specific use of sensitivity analysis techniques identifies the set of parameters most important for a given task, and even suggests task-specific model reduction possibilities.

Optimal input selection for neural machine interfaces predicting multiple non-explicit outputs.

PubMed

Krepkovich, Eileen T; Perreault, Eric J

2008-01-01

This study implemented a novel algorithm that optimally selects inputs for neural machine interface (NMI) devices intended to control multiple outputs and evaluated its performance on systems lacking explicit output. NMIs often incorporate signals from multiple physiological sources and provide predictions for multidimensional control, leading to multiple-input multiple-output systems. Further, NMIs often are used with subjects who have motor disabilities and thus lack explicit motor outputs. Our algorithm was tested on simulated multiple-input multiple-output systems and on electromyogram and kinematic data collected from healthy subjects performing arm reaches. Effects of output noise in simulated systems indicated that the algorithm could be useful for systems with poor estimates of the output states, as is true for systems lacking explicit motor output. To test efficacy on physiological data, selection was performed using inputs from one subject and outputs from a different subject. Selection was effective for these cases, again indicating that this algorithm will be useful for predictions where there is no motor output, as often is the case for disabled subjects. Further, prediction results generalized for different movement types not used for estimation. These results demonstrate the efficacy of this algorithm for the development of neural machine interfaces.

A compositional framework for reaction networks

NASA Astrophysics Data System (ADS)

Baez, John C.; Pollard, Blake S.

Reaction networks, or equivalently Petri nets, are a general framework for describing processes in which entities of various kinds interact and turn into other entities. In chemistry, where the reactions are assigned ‘rate constants’, any reaction network gives rise to a nonlinear dynamical system called its ‘rate equation’. Here we generalize these ideas to ‘open’ reaction networks, which allow entities to flow in and out at certain designated inputs and outputs. We treat open reaction networks as morphisms in a category. Composing two such morphisms connects the outputs of the first to the inputs of the second. We construct a functor sending any open reaction network to its corresponding ‘open dynamical system’. This provides a compositional framework for studying the dynamics of reaction networks. We then turn to statics: that is, steady state solutions of open dynamical systems. We construct a ‘black-boxing’ functor that sends any open dynamical system to the relation that it imposes between input and output variables in steady states. This extends our earlier work on black-boxing for Markov processes.

Analog nonlinear MIMO receiver for optical mode division multiplexing transmission.

PubMed

Spalvieri, Arnaldo; Boffi, Pierpaolo; Pecorino, Simone; Barletta, Luca; Magarini, Maurizio; Gatto, Alberto; Martelli, Paolo; Martinelli, Mario

2013-10-21

The complexity and the power consumption of digital signal processing are crucial issues in optical transmission systems based on mode division multiplexing and coherent multiple-input multiple-output (MIMO) processing at the receiver. In this paper the inherent characteristic of spatial separation between fiber modes is exploited, getting a MIMO system where joint demultiplexing and detection is based on spatially separated photodetectors. After photodetection, one has a MIMO system with nonlinear crosstalk between modes. The paper shows that the nonlinear crosstalk can be dealt with by a low-complexity and non-adaptive detection scheme, at least in the cases presented in the paper.

When causality does not imply correlation: more spadework at the foundations of scientific psychology.

PubMed

Marken, Richard S; Horth, Brittany

2011-06-01

Experimental research in psychology is based on an open-loop causal model which assumes that sensory input causes behavioral output. This model was tested in a tracking experiment where participants were asked to control a cursor, keeping it aligned with a target by moving a mouse to compensate for disturbances of differing difficulty. Since cursor movements (inputs) are the only observable cause of mouse movements (outputs), the open-loop model predicts that there will be a correlation between input and output that increases as tracking performance improves. In fact, the correlation between sensory input and motor output is very low regardless of the quality of tracking performance; causality, in terms of the effect of input on output, does not seem to imply correlation in this situation. This surprising result can be explained by a closed-loop model which assumes that input is causing output while output is causing input.

Estimation of Psychophysical Thresholds Based on Neural Network Analysis of DPOAE Input/Output Functions

NASA Astrophysics Data System (ADS)

Naghibolhosseini, Maryam; Long, Glenis

2011-11-01

The distortion product otoacoustic emission (DPOAE) input/output (I/O) function may provide a potential tool for evaluating cochlear compression. Hearing loss causes an increase in the level of the sound that is just audible for the person, which affects the cochlea compression and thus the dynamic range of hearing. Although the slope of the I/O function is highly variable when the total DPOAE is used, separating the nonlinear-generator component from the reflection component reduces this variability. We separated the two components using least squares fit (LSF) analysis of logarithmic sweeping tones, and confirmed that the separated generator component provides more consistent I/O functions than the total DPOAE. In this paper we estimated the slope of the I/O functions of the generator components at different sound levels using LSF analysis. An artificial neural network (ANN) was used to estimate psychophysical thresholds using the estimated slopes of the I/O functions. DPOAE I/O functions determined in this way may help to estimate hearing thresholds and cochlear health.

Laser beam propagation through bulk nonlinear media: Numerical simulation and experiment

NASA Astrophysics Data System (ADS)

Kovsh, Dmitriy I.

This dissertation describes our efforts in modeling the propagation of high intensity laser pulses through optical systems consisting of one or multiple nonlinear elements. These nonlinear elements can be up to 103 times thicker than the depth of focus of the laser beam, so that the beam size changes drastically within the medium. The set of computer codes developed are organized in a software package (NLO_BPM). The ultrafast nonlinearities of the bound-electronic n2 and two-photon absorption as well as time dependent excited-state, free-carrier and thermal nonlinearities are included in the codes for modeling propagation of picosecond to nanosecond pulses and pulse trains. Various cylindrically symmetric spatial distributions of the input beam are modeled. We use the cylindrical symmetry typical of laser outputs to reduce the CPU and memory requirements making modeling a real- time task on PC's. The hydrodynamic equations describing the rarefaction of the medium due to heating and electrostriction are solved in the transient regime to determine refractive index changes on a nanosecond time scale. This effect can be simplified in some cases by an approximation that assumes an instantaneous expansion. We also find that the index change obtained from the photo-acoustic equation overshoots its steady-state value once the ratio between the pulse width and the acoustic transit time is greater than unity. We numerically study the sensitivity of the closed- aperture Z-scan experiment to nonlinear refraction for various input beam profiles. If the beam has a ring structure with a minimum (or zero) on axis in the far field, the sensitivity of Z-scan measurements can be increased by up to one order of magnitude. The linear propagation module integrated with the nonlinear beam propagation codes allows the simulation of typical experiments such as Z-scan and optical limiting experiments. We have used these codes to model the performance of optical limiters. We study two of the most promising limiter designs: the monolithic self-protective semiconductor limiter (MONOPOL) and a multi-cell tandem limiter based on a liquid solution of reverse saturable absorbing organic dye. The numerical outputs show good agreement with experimental results up to input energies where nonlinear scattering becomes significant.

Output Feedback Distributed Containment Control for High-Order Nonlinear Multiagent Systems.

PubMed

Li, Yafeng; Hua, Changchun; Wu, Shuangshuang; Guan, Xinping

2017-01-31

In this paper, we study the problem of output feedback distributed containment control for a class of high-order nonlinear multiagent systems under a fixed undirected graph and a fixed directed graph, respectively. Only the output signals of the systems can be measured. The novel reduced order dynamic gain observer is constructed to estimate the unmeasured state variables of the system with the less conservative condition on nonlinear terms than traditional Lipschitz one. Via the backstepping method, output feedback distributed nonlinear controllers for the followers are designed. By means of the novel first virtual controllers, we separate the estimated state variables of different agents from each other. Consequently, the designed controllers show independence on the estimated state variables of neighbors except outputs information, and the dynamics of each agent can be greatly different, which make the design method have a wider class of applications. Finally, a numerical simulation is presented to illustrate the effectiveness of the proposed method.

Precision absolute-value amplifier for a precision voltmeter

DOEpatents

Hearn, W.E.; Rondeau, D.J.

1982-10-19

Bipolar inputs are afforded by the plus inputs of first and second differential input amplifiers. A first gain determining resistor is connected between the minus inputs of the differential amplifiers. First and second diodes are connected between the respective minus inputs and the respective outputs of the differential amplifiers. First and second FETs have their gates connected to the outputs of the amplifiers, while their respective source and drain circuits are connected between the respective minus inputs and an output lead extending to a load resistor. The output current through the load resistor is proportional to the absolute value of the input voltage difference between the bipolar input terminals. A third differential amplifier has its plus input terminal connected to the load resistor. A second gain determining resistor is connected between the minus input of the third differential amplifier and a voltage source. A third FET has its gate connected to the output of the third amplifier. The source and drain circuit of the third transistor is connected between the minus input of the third amplifier and a voltage-frequency converter, constituting an output device. A polarity detector is also provided, comprising a pair of transistors having their inputs connected to the outputs of the first and second differential amplifiers. The outputs of the polarity detector are connected to gates which switch the output of the voltage-frequency converter between up and down counting outputs.

Precision absolute value amplifier for a precision voltmeter

DOEpatents

Hearn, William E.; Rondeau, Donald J.

1985-01-01

Bipolar inputs are afforded by the plus inputs of first and second differential input amplifiers. A first gain determining resister is connected between the minus inputs of the differential amplifiers. First and second diodes are connected between the respective minus inputs and the respective outputs of the differential amplifiers. First and second FETs have their gates connected to the outputs of the amplifiers, while their respective source and drain circuits are connected between the respective minus inputs and an output lead extending to a load resister. The output current through the load resister is proportional to the absolute value of the input voltage difference between the bipolar input terminals. A third differential amplifier has its plus input terminal connected to the load resister. A second gain determining resister is connected between the minus input of the third differential amplifier and a voltage source. A third FET has its gate connected to the output of the third amplifier. The source and drain circuit of the third transistor is connected between the minus input of the third amplifier and a voltage-frequency converter, constituting an output device. A polarity detector is also provided, comprising a pair of transistors having their inputs connected to the outputs of the first and second differential amplifiers. The outputs of the polarity detector are connected to gates which switch the output of the voltage-frequency converter between up and down counting outputs.

Soft sensor modelling by time difference, recursive partial least squares and adaptive model updating

NASA Astrophysics Data System (ADS)

Fu, Y.; Yang, W.; Xu, O.; Zhou, L.; Wang, J.

2017-04-01

To investigate time-variant and nonlinear characteristics in industrial processes, a soft sensor modelling method based on time difference, moving-window recursive partial least square (PLS) and adaptive model updating is proposed. In this method, time difference values of input and output variables are used as training samples to construct the model, which can reduce the effects of the nonlinear characteristic on modelling accuracy and retain the advantages of recursive PLS algorithm. To solve the high updating frequency of the model, a confidence value is introduced, which can be updated adaptively according to the results of the model performance assessment. Once the confidence value is updated, the model can be updated. The proposed method has been used to predict the 4-carboxy-benz-aldehyde (CBA) content in the purified terephthalic acid (PTA) oxidation reaction process. The results show that the proposed soft sensor modelling method can reduce computation effectively, improve prediction accuracy by making use of process information and reflect the process characteristics accurately.

Formulation d'un modele mathematique par des techniques d'estimation de parametres a partir de donnees de vol pour l'helicoptere Bell 427 et l'avion F/A-18 servant a la recherches en aeroservoelasticite

NASA Astrophysics Data System (ADS)

Nadeau-Beaulieu, Michel

In this thesis, three mathematical models are built from flight test data for different aircraft design applications: a ground dynamics model for the Bell 427 helicopter, a prediction model for the rotor and engine parameters for the same helicopter type and a simulation model for the aeroelastic deflections of the F/A-18. In the ground dynamics application, the model structure is derived from physics where the normal force between the helicopter and the ground is modelled as a vertical spring and the frictional force is modelled with static and dynamic friction coefficients. The ground dynamics model coefficients are optimized to ensure that the model matches the landing data within the FAA (Federal Aviation Administration) tolerance bands for a level D flight simulator. In the rotor and engine application, rotors torques (main and tail), the engine torque and main rotor speed are estimated using a state-space model. The model inputs are nonlinear terms derived from the pilot control inputs and the helicopter states. The model parameters are identified using the subspace method and are further optimised with the Levenberg-Marquardt minimisation algorithm. The model built with the subspace method provides an excellent estimate of the outputs within the FAA tolerance bands. The F/A-18 aeroelastic state-space model is built from flight test. The research concerning this model is divided in two parts. Firstly, the deflection of a given structural surface on the aircraft following a differential ailerons control input is represented by a Multiple Inputs Single Outputs linear model whose inputs are the ailerons positions and the structural surfaces deflections. Secondly, a single state-space model is used to represent the deflection of the aircraft wings and trailing edge flaps following any control input. In this case the model is made non-linear by multiplying model inputs into higher order terms and using these terms as the inputs of the state-space equations. In both cases, the identification method is the subspace method. Most fit coefficients between the estimated and the measured signals are above 73% and most correlation coefficient are higher than 90%.

A computer program to obtain time-correlated gust loads for nonlinear aircraft using the matched-filter-based method

NASA Technical Reports Server (NTRS)

Scott, Robert C.; Pototzky, Anthony S.; Perry, Boyd, III

1994-01-01

NASA Langley Research Center has, for several years, conducted research in the area of time-correlated gust loads for linear and nonlinear aircraft. The results of this work led NASA to recommend that the Matched-Filter-Based One-Dimensional Search Method be used for gust load analyses of nonlinear aircraft. This manual describes this method, describes a FORTRAN code which performs this method, and presents example calculations for a sample nonlinear aircraft model. The name of the code is MFD1DS (Matched-Filter-Based One-Dimensional Search). The program source code, the example aircraft equations of motion, a sample input file, and a sample program output are all listed in the appendices.

Jump resonant frequency islands in nonlinear feedback control systems

NASA Technical Reports Server (NTRS)

Koenigsberg, W. D.; Dunn, J. C.

1975-01-01

A new type of jump resonance is predicted and observed in certain nonlinear feedback control systems. The new jump resonance characteristic is described as a 'frequency island' due to the fact that a portion of the input-output transfer characteristic is disjoint from the main body. The presence of such frequency islands was predicted by using a sinusoidal describing function characterization of the dynamics of an inertial gyro employing nonlinear ternary rebalance logic. While the general conditions under which such islands are possible has not been examined, a numerical approach is presented which can aid in establishing their presence. The existence of the frequency islands predicted for the ternary rebalanced gyro was confirmed by simulating the nonlinear system and measuring the transfer function.

A simultaneous all-optical half/full-subtraction strategy using cascaded highly nonlinear fibers

NASA Astrophysics Data System (ADS)

Singh, Karamdeep; Kaur, Gurmeet; Singh, Maninder Lal

2018-02-01

Using non-linear effects such as cross-gain modulation (XGM) and cross-phase modulation (XPM) inside two highly non-linear fibres (HNLF) arranged in cascaded configuration, a simultaneous half/full-subtracter is proposed. The proposed simultaneous half/full-subtracter design is attractive due to several features such as input data pattern independence and usage of minimal number of non-linear elements i.e. HNLFs. Proof of concept simulations have been conducted at 100 Gbps rate, indicating fine performance, as extinction ratio (dB) > 6.28 dB and eye opening factors (EO) > 77.1072% are recorded for each implemented output. The proposed simultaneous half/full-subtracter can be used as a key component in all-optical information processing circuits.

A simple nonlinear element model

NASA Astrophysics Data System (ADS)

Mikhailov, S. G.; Rudenko, O. V.

2017-05-01

We study experimentally the behavior of a nonlinear element, a light plate pressed to the opening in the cavity of an acoustic resonator. Measurements of field oscillations inside and outside the cavity have shown that for large amplitudes, they become essentially anharmonic. The time dependences of displacement of the plate with increasing amplitude of the exciting voltage demonstrates a gradual change in the shape of vibrations from harmonic to half-period oscillation. A constant component appears in the cavity: rarefaction or outflow of the medium through the orifice. We construct a theory for nonlinear oscillations of a plate taking into account its different elastic reactions to compression and rarefaction with allowance for monopole radiation by the small-wave-size plate or radiation of a plane wave by the plate. We calculate the amplitudes of the harmonics and solve the problem of low-frequency stationary noise acting on the plate. We obtain expressions for the correlation function and mean power at the output given a normal random process at the input.

Investigation of a cantilever structured piezoelectric energy harvester used for wearable devices with random vibration input

NASA Astrophysics Data System (ADS)

Bai, Yang; Tofel, Pavel; Hadas, Zdenek; Smilek, Jan; Losak, Petr; Skarvada, Pavel; Macku, Robert

2018-06-01

The capability of using a linear kinetic energy harvester - A cantilever structured piezoelectric energy harvester - to harvest human motions in the real-life activities is investigated. The whole loop of the design, simulation, fabrication and test of the energy harvester is presented. With the smart wristband/watch sized energy harvester, a root mean square of the output power of 50 μW is obtained from the real-life hand-arm motion in human's daily life. Such a power is enough to make some low power consumption sensors to be self-powered. This paper provides a good and reliable comparison to those with nonlinear structures. It also helps the designers to consider whether to choose a nonlinear structure or not in a particular energy harvester based on different application scenarios.

Nonlinear system theory: Another look at dependence

PubMed Central

Wu, Wei Biao

2005-01-01

Based on the nonlinear system theory, we introduce previously undescribed dependence measures for stationary causal processes. Our physical and predictive dependence measures quantify the degree of dependence of outputs on inputs in physical systems. The proposed dependence measures provide a natural framework for a limit theory for stationary processes. In particular, under conditions with quite simple forms, we present limit theorems for partial sums, empirical processes, and kernel density estimates. The conditions are mild and easily verifiable because they are directly related to the data-generating mechanisms. PMID:16179388

Nonlinear optics in the LP(02) higher-order mode of a fiber.

PubMed

Chen, Y; Chen, Z; Wadsworth, W J; Birks, T A

2013-07-29

The distinct disperion properties of higher-order modes in optical fibers permit the nonlinear generation of radiation deeper into the ultraviolet than is possible with the fundamental mode. This is exploited using adiabatic, broadband mode convertors to couple light efficiently from an input fundamental mode and also to return the generated light to an output fundamental mode over a broad spectral range. For example, we generate visible and UV supercontinuum light in the LP(02) mode of a photonic crystal fiber from sub-ns pulses with a wavelength of 532 nm.

High-energy and ultra-wideband tunable terahertz source with DAST crystal via difference frequency generation

NASA Astrophysics Data System (ADS)

He, Yixin; Wang, Yuye; Xu, Degang; Nie, Meitong; Yan, Chao; Tang, Longhuang; Shi, Jia; Feng, Jiachen; Yan, Dexian; Liu, Hongxiang; Teng, Bing; Feng, Hua; Yao, Jianquan

2018-01-01

We have demonstrated a high-energy and broadly tunable monochromatic terahertz (THz) source based on difference frequency generation (DFG) in DAST crystal. A high-energy dual-wavelength optical parametric oscillator with two KTP crystals was constructed as a light source for DFG, where the effect of blue light was first observed accompanying with tunable dual-wavelength pump light due to different nonlinear processes. The THz frequency was tuned randomly in the range of 0.3-19.6 THz. The highest energy of 870 nJ/pulse was obtained at 18.9 THz under the intense pump intensity of 247 MW/cm2. The THz energy dips above 3 THz have been analyzed and mainly attributed to the resonance absorption induced by lattice vibration in DAST crystal. The dependence of THz output on the input energy was studied experimentally, and THz output saturation was observed. Furthermore, tests of transmission spectroscopy of four typical samples were demonstrated with this ultra-wideband THz source.

Effect of change in core width and core refractive index on the switching behavior of a nonlinear Mach-Zehnder interferometer

NASA Astrophysics Data System (ADS)

Srivastava, Arpita; Medhekar, Sarang

2012-09-01

The effect of variation of core width and core refractive index on output versus input (O-I) and transmission coefficient versus input (T-I) characteristics of a nonlinear Mach-Zehnder interferometer (NMZI) were investigated for the first time. Beam propagation method has been used for the present investigation. Change of core width and/or core refractive index adds extra liberty for changing the operating power of an NMZI to a desired value. Moreover, it is revealed for the first time that use of only the O-I or T-I characteristic presents an incomplete picture of NMZI switching; both O-I and T-I characteristics of both balanced/imbalanced NMZIs are indispensible for complete understanding of NMZI switching.

Nonlinear Site Response Validation Studies Using KIK-net Strong Motion Data

NASA Astrophysics Data System (ADS)

Asimaki, D.; Shi, J.

2014-12-01

Earthquake simulations are nowadays producing realistic ground motion time-series in the range of engineering design applications. Of particular significance to engineers are simulations of near-field motions and large magnitude events, for which observations are scarce. With the engineering community slowly adopting the use of simulated ground motions, site response models need to be re-evaluated in terms of their capabilities and limitations to 'translate' the simulated time-series from rock surface output to structural analyses input. In this talk, we evaluate three one-dimensional site response models: linear viscoelastic, equivalent linear and nonlinear. We evaluate the performance of the models by comparing predictions to observations at 30 downhole stations of the Japanese network KIK-Net that have recorded several strong events, including the 2011 Tohoku earthquake. Velocity profiles are used as the only input to all models, while additional parameters such as quality factor, density and nonlinear dynamic soil properties are estimated from empirical correlations. We quantify the differences of ground surface predictions and observations in terms of both seismological and engineering intensity measures, including bias ratios of peak ground response and visual comparisons of elastic spectra, and inelastic to elastic deformation ratio for multiple ductility ratios. We observe that PGV/Vs,30 — as measure of strain— is a better predictor of site nonlinearity than PGA, and that incremental nonlinear analyses are necessary to produce reliable estimates of high-frequency ground motion components at soft sites. We finally discuss the implications of our findings on the parameterization of nonlinear amplification factors in GMPEs, and on the extensive use of equivalent linear analyses in probabilistic seismic hazard procedures.

Data-Driven Zero-Sum Neuro-Optimal Control for a Class of Continuous-Time Unknown Nonlinear Systems With Disturbance Using ADP.

PubMed

Wei, Qinglai; Song, Ruizhuo; Yan, Pengfei

2016-02-01

This paper is concerned with a new data-driven zero-sum neuro-optimal control problem for continuous-time unknown nonlinear systems with disturbance. According to the input-output data of the nonlinear system, an effective recurrent neural network is introduced to reconstruct the dynamics of the nonlinear system. Considering the system disturbance as a control input, a two-player zero-sum optimal control problem is established. Adaptive dynamic programming (ADP) is developed to obtain the optimal control under the worst case of the disturbance. Three single-layer neural networks, including one critic and two action networks, are employed to approximate the performance index function, the optimal control law, and the disturbance, respectively, for facilitating the implementation of the ADP method. Convergence properties of the ADP method are developed to show that the system state will converge to a finite neighborhood of the equilibrium. The weight matrices of the critic and the two action networks are also convergent to finite neighborhoods of their optimal ones. Finally, the simulation results will show the effectiveness of the developed data-driven ADP methods.

A control problem for Burgers' equation with bounded input/output

NASA Technical Reports Server (NTRS)

Burns, John A.; Kang, Sungkwon

1990-01-01

A stabilization problem for Burgers' equation is considered. Using linearization, various controllers are constructed which minimize certain weighted energy functionals. These controllers produce the desired degree of stability for the closed-loop nonlinear system. A numerical scheme for computing the feedback gain functional is developed and several numerical experiments are performed to show the theoretical results.

General methodology for nonlinear modeling of neural systems with Poisson point-process inputs.

PubMed

Marmarelis, V Z; Berger, T W

2005-07-01

This paper presents a general methodological framework for the practical modeling of neural systems with point-process inputs (sequences of action potentials or, more broadly, identical events) based on the Volterra and Wiener theories of functional expansions and system identification. The paper clarifies the distinctions between Volterra and Wiener kernels obtained from Poisson point-process inputs. It shows that only the Wiener kernels can be estimated via cross-correlation, but must be defined as zero along the diagonals. The Volterra kernels can be estimated far more accurately (and from shorter data-records) by use of the Laguerre expansion technique adapted to point-process inputs, and they are independent of the mean rate of stimulation (unlike their P-W counterparts that depend on it). The Volterra kernels can also be estimated for broadband point-process inputs that are not Poisson. Useful applications of this modeling approach include cases where we seek to determine (model) the transfer characteristics between one neuronal axon (a point-process 'input') and another axon (a point-process 'output') or some other measure of neuronal activity (a continuous 'output', such as population activity) with which a causal link exists.

Surface tension and contact angles: Molecular origins and associated microstructure

NASA Technical Reports Server (NTRS)

Davis, H. T.

1982-01-01

Gradient theory converts the molecular theory of inhomogeneous fluid into nonlinear boundary value problems for density and stress distributions in fluid interfaces, contact line regions, nuclei and microdroplets, and other fluid microstructures. The relationship between the basic patterns of fluid phase behavior and the occurrence and stability of fluid microstructures was clearly established by the theory. All the inputs of the theory have molecular expressions which are computable from simple models. On another level, the theory becomes a phenomenological framework in which the equation of state of homogeneous fluid and sets of influence parameters of inhomogeneous fluids are the inputs and the structures, stress tensions and contact angles of menisci are the outputs. These outputs, which find applications in the science and technology of drops and bubbles, are discussed.

Prediction of nonlinear soil effects

USGS Publications Warehouse

Hartzell, S.; Bonilla, L.F.; Williams, R.A.

2004-01-01

Mathematical models of soil nonlinearity in common use and recently developed nonlinear codes compared to investigate the range of their predictions. We consider equivalent linear formulations with and without frequency-dependent moduli and damping ratios and nonlinear formulations for total and effective stress. Average velocity profiles to 150 m depth with midrange National Earthquake Hazards Reduction Program site classifications (B, BC, C, D, and E) in the top 30 m are used to compare the response of a wide range of site conditions from rock to soft soil. Nonlinear soil models are compared using the amplification spectrum, calculated as the ratio of surface ground motion to the input motion at the base of the velocity profile. Peak input motions from 0.1g to 0.9g are considered. For site class B, no significant differences exist between the models considered in this article. For site classes BC and C, differences are small at low input motions (0.1g to 0.2g), but become significant at higher input levels. For site classes D and E the overdamping of frequencies above about 4 Hz by the equivalent linear solution with frequency-independent parameters is apparent for the entire range of input motions considered. The equivalent linear formulation with frequency-dependent moduli and damping ratios under damps relative to the nonlinear models considered for site class C with larger input motions and most input levels for site classes D and E. At larger input motions the underdamping for site classes D and E is not as severe as the overdamping with the frequency-independent formulation, but there are still significant differences in the time domain. A nonlinear formulation is recommended for site classes D and E and for site classes BC and C with input motions greater than a few tenths of the acceleration of gravity. The type of nonlinear formulation to use is driven by considerations of the importance of water content and the availability of laboratory soils data. Our average amplification curves from a nonlinear effective stress formulation compare favorably with observed spectral amplification at class D and E sites in the Seattle area for the 2001 Nisqually earthquake.

Robustness of continuous-time adaptive control algorithms in the presence of unmodeled dynamics

NASA Technical Reports Server (NTRS)

Rohrs, C. E.; Valavani, L.; Athans, M.; Stein, G.

1985-01-01

This paper examines the robustness properties of existing adaptive control algorithms to unmodeled plant high-frequency dynamics and unmeasurable output disturbances. It is demonstrated that there exist two infinite-gain operators in the nonlinear dynamic system which determines the time-evolution of output and parameter errors. The pragmatic implications of the existence of such infinite-gain operators is that: (1) sinusoidal reference inputs at specific frequencies and/or (2) sinusoidal output disturbances at any frequency (including dc), can cause the loop gain to increase without bound, thereby exciting the unmodeled high-frequency dynamics, and yielding an unstable control system. Hence, it is concluded that existing adaptive control algorithms as they are presented in the literature referenced in this paper, cannot be used with confidence in practical designs where the plant contains unmodeled dynamics because instability is likely to result. Further understanding is required to ascertain how the currently implemented adaptive systems differ from the theoretical systems studied here and how further theoretical development can improve the robustness of adaptive controllers.

Cross-polarized wave generation (XPW) for ultrafast laser pulse characterization and intensity contrast enhancement

NASA Astrophysics Data System (ADS)

Iliev, Marin

Good pulse quality, high peak power and tunable central wavelength are amongst the most desired qualities in modern lasers. The nonlinear effect cross-polarized wave generation (XPW), can be used in ultrafast laser systems to achieve various pulse quality enhancements. The XPW yield depends on the cube of the input intensity and acts as a spatio-temporal filter. It is orthogonally polarized to the input pulse and highly Gaussian. If the input pulse is well compressed, the output spectrum is smoother and broader. These features make XPW an ideal reference signal in pulse characterization techniques. This thesis presents a detailed analysis of the XPW conversion process, and describes novel applications to pulse characterization and high-quality pulse cleaning. An extensive computer model was developed to describe XPW generation via solution of the full coupled non-linear differential equations. The model accounts for dispersion inside the nonlinear crystal and uses split-step Fourier optics beam propagation to simulate the evolution of the electro-magnetic fields of the pump and XPW through free-space and imaging systems. A novel extension to the self-referenced spectral interferometry (SRSI) pulse characterization technique allows the retrieval of the energy and spectral content of the amplified spontaneous emission (ASE) present in ultrashort pulse amplifier systems. A novel double-pass XPW conversion scheme is presented. In it the beam passes through a single XPW crystal (BaF2) and is re-imaged with a curved mirror. The technique resulted in good (˜30%) efficiency without the spatial aberrations commonly seen in another arrangement that uses two crystals in succession. The modeling sheds light on the complicated nonlinear beam dynamics of the double-crystal conversion, including self- and cross-phase modulation, self-focusing, and the effects of, relative on-axis phase-difference, relative beam sizes, and wave-front curvature matching on seeded XPW conversion. Finally, a design is presented for exploiting the clean-up properties of XPW at the output of an optical parametric generation (OPA) setup in conjunction with an extremely compact prism compressor. The prisms material, separation and geometry are designed carefully to work at the correct wavelength of the OPA setup and are extrapolated to accommodate wavelengths, such as 2mum of parametric wave generation.

Analysis of control system responses for aircraft stability and efficient numerical techniques for real-time simulations

NASA Astrophysics Data System (ADS)

Stroe, Gabriela; Andrei, Irina-Carmen; Frunzulica, Florin

2017-01-01

The objectives of this paper are the study and the implementation of both aerodynamic and propulsion models, as linear interpolations using look-up tables in a database. The aerodynamic and propulsion dependencies on state and control variable have been described by analytic polynomial models. Some simplifying hypotheses were made in the development of the nonlinear aircraft simulations. The choice of a certain technique to use depends on the desired accuracy of the solution and the computational effort to be expended. Each nonlinear simulation includes the full nonlinear dynamics of the bare airframe, with a scaled direct connection from pilot inputs to control surface deflections to provide adequate pilot control. The engine power dynamic response was modeled with an additional state equation as first order lag in the actual power level response to commanded power level was computed as a function of throttle position. The number of control inputs and engine power states varied depending on the number of control surfaces and aircraft engines. The set of coupled, nonlinear, first-order ordinary differential equations that comprise the simulation model can be represented by the vector differential equation. A linear time-invariant (LTI) system representing aircraft dynamics for small perturbations about a reference trim condition is given by the state and output equations present. The gradients are obtained numerically by perturbing each state and control input independently and recording the changes in the trimmed state and output equations. This is done using the numerical technique of central finite differences, including the perturbations of the state and control variables. For a reference trim condition of straight and level flight, linearization results in two decoupled sets of linear, constant-coefficient differential equations for longitudinal and lateral / directional motion. The linearization is valid for small perturbations about the reference trim condition. Experimental aerodynamic and thrust data are used to model the applied aerodynamic and propulsion forces and moments for arbitrary states and controls. There is no closed form solution to such problems, so the equations must be solved using numerical integration. Techniques for solving this initial value problem for ordinary differential equations are employed to obtain approximate solutions at discrete points along the aircraft state trajectory.

Neural-Based Compensation of Nonlinearities in an Airplane Longitudinal Model with Dynamic-Inversion Control

PubMed Central

Li, YuHui; Jin, FeiTeng

2017-01-01

The inversion design approach is a very useful tool for the complex multiple-input-multiple-output nonlinear systems to implement the decoupling control goal, such as the airplane model and spacecraft model. In this work, the flight control law is proposed using the neural-based inversion design method associated with the nonlinear compensation for a general longitudinal model of the airplane. First, the nonlinear mathematic model is converted to the equivalent linear model based on the feedback linearization theory. Then, the flight control law integrated with this inversion model is developed to stabilize the nonlinear system and relieve the coupling effect. Afterwards, the inversion control combined with the neural network and nonlinear portion is presented to improve the transient performance and attenuate the uncertain effects on both external disturbances and model errors. Finally, the simulation results demonstrate the effectiveness of this controller. PMID:29410680

Adaptive NN Control Using Integral Barrier Lyapunov Functionals for Uncertain Nonlinear Block-Triangular Constraint Systems.

PubMed

Liu, Yan-Jun; Tong, Shaocheng; Chen, C L Philip; Li, Dong-Juan

2017-11-01

A neural network (NN) adaptive control design problem is addressed for a class of uncertain multi-input-multi-output (MIMO) nonlinear systems in block-triangular form. The considered systems contain uncertainty dynamics and their states are enforced to subject to bounded constraints as well as the couplings among various inputs and outputs are inserted in each subsystem. To stabilize this class of systems, a novel adaptive control strategy is constructively framed by using the backstepping design technique and NNs. The novel integral barrier Lyapunov functionals (BLFs) are employed to overcome the violation of the full state constraints. The proposed strategy can not only guarantee the boundedness of the closed-loop system and the outputs are driven to follow the reference signals, but also can ensure all the states to remain in the predefined compact sets. Moreover, the transformed constraints on the errors are used in the previous BLF, and accordingly it is required to determine clearly the bounds of the virtual controllers. Thus, it can relax the conservative limitations in the traditional BLF-based controls for the full state constraints. This conservatism can be solved in this paper and it is for the first time to control this class of MIMO systems with the full state constraints. The performance of the proposed control strategy can be verified through a simulation example.

Tailored Excitation for Multivariable Stability-Margin Measurement Applied to the X-31A Nonlinear Simulation

NASA Technical Reports Server (NTRS)

Bosworth, John T.; Burken, John J.

1997-01-01

Safety and productivity of the initial flight test phase of a new vehicle have been enhanced by developing the ability to measure the stability margins of the combined control system and vehicle in flight. One shortcoming of performing this analysis is the long duration of the excitation signal required to provide results over a wide frequency range. For flight regimes such as high angle of attack or hypersonic flight, the ability to maintain flight condition for this time duration is difficult. Significantly reducing the required duration of the excitation input is possible by tailoring the input to excite only the frequency range where the lowest stability margin is expected. For a multiple-input/multiple-output system, the inputs can be simultaneously applied to the control effectors by creating each excitation input with a unique set of frequency components. Chirp-Z transformation algorithms can be used to match the analysis of the results to the specific frequencies used in the excitation input. This report discusses the application of a tailored excitation input to a high-fidelity X-31A linear model and nonlinear simulation. Depending on the frequency range, the results indicate the potential to significantly reduce the time required for stability measurement.

Computation of optimal output-feedback compensators for linear time-invariant systems

NASA Technical Reports Server (NTRS)

Platzman, L. K.

1972-01-01

The control of linear time-invariant systems with respect to a quadratic performance criterion was considered, subject to the constraint that the control vector be a constant linear transformation of the output vector. The optimal feedback matrix, f*, was selected to optimize the expected performance, given the covariance of the initial state. It is first shown that the expected performance criterion can be expressed as the ratio of two multinomials in the element of f. This expression provides the basis for a feasible method of determining f* in the case of single-input single-output systems. A number of iterative algorithms are then proposed for the calculation of f* for multiple input-output systems. For two of these, monotone convergence is proved, but they involve the solution of nonlinear matrix equations at each iteration. Another is proposed involving the solution of Lyapunov equations at each iteration, and the gradual increase of the magnitude of a penalty function. Experience with this algorithm will be needed to determine whether or not it does, indeed, possess desirable convergence properties, and whether it can be used to determine the globally optimal f*.

Control Systems with Normalized and Covariance Adaptation by Optimal Control Modification

NASA Technical Reports Server (NTRS)

Nguyen, Nhan T. (Inventor); Burken, John J. (Inventor); Hanson, Curtis E. (Inventor)

2016-01-01

Disclosed is a novel adaptive control method and system called optimal control modification with normalization and covariance adjustment. The invention addresses specifically to current challenges with adaptive control in these areas: 1) persistent excitation, 2) complex nonlinear input-output mapping, 3) large inputs and persistent learning, and 4) the lack of stability analysis tools for certification. The invention has been subject to many simulations and flight testing. The results substantiate the effectiveness of the invention and demonstrate the technical feasibility for use in modern aircraft flight control systems.

Multivariable control of a rolling spider drone

NASA Astrophysics Data System (ADS)

Lyu, Haifeng

The research and application of Unmanned Aerial Vehicles (UAVs) has been a hot topic recently. A UAV is dened as an aircraft which is designed not to carry a human pilot or operated with remote electronic input by the flight controller. In this thesis, the design of a control system for a quadcopter named Rolling Spider Drone is conducted. The thesis work presents the design of two kinds of controllers that can control the Drone to keep it balanced and track different kinds of input trajectories. The nonlinear mathematical model for the Drone is derived by the Newton-Euler method. The rotational subsystem and translational system are derived to describe the attitude and position motion of Drone. Techniques from linear control theory are employed to linearize the highly coupled and nonlinear quadcopter plant around equilibrium points and apply the linear feedback controller to stabilize the system. The controller is a digital tracking system that deploys LQR for system stability design. Fixed gain and adaptive gain scheduled controllers are developed and compared with different LQR weights. Step references and reference trajectories involving signicant variation for the yaw angle in the xy-plane and three-dimensional spaces are tracked in the simulation. The physical implementation and an output feedback controller are considered for future work.

Performance Analysis for Channel Estimation With 1-Bit ADC and Unknown Quantization Threshold

NASA Astrophysics Data System (ADS)

Stein, Manuel S.; Bar, Shahar; Nossek, Josef A.; Tabrikian, Joseph

2018-05-01

In this work, the problem of signal parameter estimation from measurements acquired by a low-complexity analog-to-digital converter (ADC) with $1$-bit output resolution and an unknown quantization threshold is considered. Single-comparator ADCs are energy-efficient and can be operated at ultra-high sampling rates. For analysis of such systems, a fixed and known quantization threshold is usually assumed. In the symmetric case, i.e., zero hard-limiting offset, it is known that in the low signal-to-noise ratio (SNR) regime the signal processing performance degrades moderately by ${2}/{\\pi}$ ($-1.96$ dB) when comparing to an ideal $\\infty$-bit converter. Due to hardware imperfections, low-complexity $1$-bit ADCs will in practice exhibit an unknown threshold different from zero. Therefore, we study the accuracy which can be obtained with receive data processed by a hard-limiter with unknown quantization level by using asymptotically optimal channel estimation algorithms. To characterize the estimation performance of these nonlinear algorithms, we employ analytic error expressions for different setups while modeling the offset as a nuisance parameter. In the low SNR regime, we establish the necessary condition for a vanishing loss due to missing offset knowledge at the receiver. As an application, we consider the estimation of single-input single-output wireless channels with inter-symbol interference and validate our analysis by comparing the analytic and experimental performance of the studied estimation algorithms. Finally, we comment on the extension to multiple-input multiple-output channel models.

Orientation selectivity in inhibition-dominated networks of spiking neurons: effect of single neuron properties and network dynamics.

PubMed

Sadeh, Sadra; Rotter, Stefan

2015-01-01

The neuronal mechanisms underlying the emergence of orientation selectivity in the primary visual cortex of mammals are still elusive. In rodents, visual neurons show highly selective responses to oriented stimuli, but neighboring neurons do not necessarily have similar preferences. Instead of a smooth map, one observes a salt-and-pepper organization of orientation selectivity. Modeling studies have recently confirmed that balanced random networks are indeed capable of amplifying weakly tuned inputs and generating highly selective output responses, even in absence of feature-selective recurrent connectivity. Here we seek to elucidate the neuronal mechanisms underlying this phenomenon by resorting to networks of integrate-and-fire neurons, which are amenable to analytic treatment. Specifically, in networks of perfect integrate-and-fire neurons, we observe that highly selective and contrast invariant output responses emerge, very similar to networks of leaky integrate-and-fire neurons. We then demonstrate that a theory based on mean firing rates and the detailed network topology predicts the output responses, and explains the mechanisms underlying the suppression of the common-mode, amplification of modulation, and contrast invariance. Increasing inhibition dominance in our networks makes the rectifying nonlinearity more prominent, which in turn adds some distortions to the otherwise essentially linear prediction. An extension of the linear theory can account for all the distortions, enabling us to compute the exact shape of every individual tuning curve in our networks. We show that this simple form of nonlinearity adds two important properties to orientation selectivity in the network, namely sharpening of tuning curves and extra suppression of the modulation. The theory can be further extended to account for the nonlinearity of the leaky model by replacing the rectifier by the appropriate smooth input-output transfer function. These results are robust and do not depend on the state of network dynamics, and hold equally well for mean-driven and fluctuation-driven regimes of activity.

Orientation Selectivity in Inhibition-Dominated Networks of Spiking Neurons: Effect of Single Neuron Properties and Network Dynamics

PubMed Central

Sadeh, Sadra; Rotter, Stefan

2015-01-01

The neuronal mechanisms underlying the emergence of orientation selectivity in the primary visual cortex of mammals are still elusive. In rodents, visual neurons show highly selective responses to oriented stimuli, but neighboring neurons do not necessarily have similar preferences. Instead of a smooth map, one observes a salt-and-pepper organization of orientation selectivity. Modeling studies have recently confirmed that balanced random networks are indeed capable of amplifying weakly tuned inputs and generating highly selective output responses, even in absence of feature-selective recurrent connectivity. Here we seek to elucidate the neuronal mechanisms underlying this phenomenon by resorting to networks of integrate-and-fire neurons, which are amenable to analytic treatment. Specifically, in networks of perfect integrate-and-fire neurons, we observe that highly selective and contrast invariant output responses emerge, very similar to networks of leaky integrate-and-fire neurons. We then demonstrate that a theory based on mean firing rates and the detailed network topology predicts the output responses, and explains the mechanisms underlying the suppression of the common-mode, amplification of modulation, and contrast invariance. Increasing inhibition dominance in our networks makes the rectifying nonlinearity more prominent, which in turn adds some distortions to the otherwise essentially linear prediction. An extension of the linear theory can account for all the distortions, enabling us to compute the exact shape of every individual tuning curve in our networks. We show that this simple form of nonlinearity adds two important properties to orientation selectivity in the network, namely sharpening of tuning curves and extra suppression of the modulation. The theory can be further extended to account for the nonlinearity of the leaky model by replacing the rectifier by the appropriate smooth input-output transfer function. These results are robust and do not depend on the state of network dynamics, and hold equally well for mean-driven and fluctuation-driven regimes of activity. PMID:25569445

Design of Linear Control System for Wind Turbine Blade Fatigue Testing

NASA Astrophysics Data System (ADS)

Toft, Anders; Roe-Poulsen, Bjarke; Christiansen, Rasmus; Knudsen, Torben

2016-09-01

This paper proposes a linear method for wind turbine blade fatigue testing at Siemens Wind Power. The setup consists of a blade, an actuator (motor and load mass) that acts on the blade with a sinusoidal moment, and a distribution of strain gauges to measure the blade flexure. Based on the frequency of the sinusoidal input, the blade will start oscillating with a given gain, hence the objective of the fatigue test is to make the blade oscillate with a controlled amplitude. The system currently in use is based on frequency control, which involves some non-linearities that make the system difficult to control. To make a linear controller, a different approach has been chosen, namely making a controller which is not regulating on the input frequency, but on the input amplitude. A non-linear mechanical model for the blade and the motor has been constructed. This model has been simplified based on the desired output, namely the amplitude of the blade. Furthermore, the model has been linearised to make it suitable for linear analysis and control design methods. The controller is designed based on a simplified and linearised model, and its gain parameter determined using pole placement. The model variants have been simulated in the MATLAB toolbox Simulink, which shows that the controller design based on the simple model performs adequately with the non-linear model. Moreover, the developed controller solves the robustness issue found in the existent solution and also reduces the needed energy for actuation as it always operates at the blade eigenfrequency.

A Novel Approach to Implement Takagi-Sugeno Fuzzy Models.

PubMed

Chang, Chia-Wen; Tao, Chin-Wang

2017-09-01

This paper proposes new algorithms based on the fuzzy c-regressing model algorithm for Takagi-Sugeno (T-S) fuzzy modeling of the complex nonlinear systems. A fuzzy c-regression state model (FCRSM) algorithm is a T-S fuzzy model in which the functional antecedent and the state-space-model-type consequent are considered with the available input-output data. The antecedent and consequent forms of the proposed FCRSM consists mainly of two advantages: one is that the FCRSM has low computation load due to only one input variable is considered in the antecedent part; another is that the unknown system can be modeled to not only the polynomial form but also the state-space form. Moreover, the FCRSM can be extended to FCRSM-ND and FCRSM-Free algorithms. An algorithm FCRSM-ND is presented to find the T-S fuzzy state-space model of the nonlinear system when the input-output data cannot be precollected and an assumed effective controller is available. In the practical applications, the mathematical model of controller may be hard to be obtained. In this case, an online tuning algorithm, FCRSM-FREE, is designed such that the parameters of a T-S fuzzy controller and the T-S fuzzy state model of an unknown system can be online tuned simultaneously. Four numerical simulations are given to demonstrate the effectiveness of the proposed approach.

Sensorless H∞ speed-tracking synthesis for surface-mount permanent magnet synchronous motor.

PubMed

Ramírez-Villalobos, Ramón; Aguilar, Luis T; Coria, Luis N

2017-03-01

In this paper, a sensorless speed tracking control is proposed for a surface-mount permanent magnet synchronous motor by using a nonlinear H ∞ -controller via stator currents measurements for feedback. An output feedback nonlinear H ∞ -controller was designed such that the undisturbed system is uniformly asymptotically stable around the desired speed reference, while also the effects of external vanishing and non-vanishing disturbances, noise, and input backlash were attenuated locally. The rotor position was calculated from the causal dynamic output feedback compensator and from the desired speed reference. The existence of the proper solutions of the perturbed differential Riccati equations ensures stabilizability and detectability of the control system. The efficiency of the proposed sensorless controller was supported by numerical simulations. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Adaptive Neural Output Feedback Control for Nonstrict-Feedback Stochastic Nonlinear Systems With Unknown Backlash-Like Hysteresis and Unknown Control Directions.

PubMed

Yu, Zhaoxu; Li, Shugang; Yu, Zhaosheng; Li, Fangfei

2018-04-01

This paper investigates the problem of output feedback adaptive stabilization for a class of nonstrict-feedback stochastic nonlinear systems with both unknown backlashlike hysteresis and unknown control directions. A new linear state transformation is applied to the original system, and then, control design for the new system becomes feasible. By combining the neural network's (NN's) parameterization, variable separation technique, and Nussbaum gain function method, an input-driven observer-based adaptive NN control scheme, which involves only one parameter to be updated, is developed for such systems. All closed-loop signals are bounded in probability and the error signals remain semiglobally bounded in the fourth moment (or mean square). Finally, the effectiveness and the applicability of the proposed control design are verified by two simulation examples.

Implementation of software-based sensor linearization algorithms on low-cost microcontrollers.

PubMed

Erdem, Hamit

2010-10-01

Nonlinear sensors and microcontrollers are used in many embedded system designs. As the input-output characteristic of most sensors is nonlinear in nature, obtaining data from a nonlinear sensor by using an integer microcontroller has always been a design challenge. This paper discusses the implementation of six software-based sensor linearization algorithms for low-cost microcontrollers. The comparative study of the linearization algorithms is performed by using a nonlinear optical distance-measuring sensor. The performance of the algorithms is examined with respect to memory space usage, linearization accuracy and algorithm execution time. The implementation and comparison results can be used for selection of a linearization algorithm based on the sensor transfer function, expected linearization accuracy and microcontroller capacity. Copyright © 2010 ISA. Published by Elsevier Ltd. All rights reserved.

Adaptive Fuzzy Control Design for Stochastic Nonlinear Switched Systems With Arbitrary Switchings and Unmodeled Dynamics.

PubMed

Li, Yongming; Sui, Shuai; Tong, Shaocheng

2017-02-01

This paper deals with the problem of adaptive fuzzy output feedback control for a class of stochastic nonlinear switched systems. The controlled system in this paper possesses unmeasured states, completely unknown nonlinear system functions, unmodeled dynamics, and arbitrary switchings. A state observer which does not depend on the switching signal is constructed to tackle the unmeasured states. Fuzzy logic systems are employed to identify the completely unknown nonlinear system functions. Based on the common Lyapunov stability theory and stochastic small-gain theorem, a new robust adaptive fuzzy backstepping stabilization control strategy is developed. The stability of the closed-loop system on input-state-practically stable in probability is proved. The simulation results are given to verify the efficiency of the proposed fuzzy adaptive control scheme.

Damage detection and quantification in a structural model under seismic excitation using time-frequency analysis

NASA Astrophysics Data System (ADS)

Chan, Chun-Kai; Loh, Chin-Hsiung; Wu, Tzu-Hsiu

2015-04-01

In civil engineering, health monitoring and damage detection are typically carry out by using a large amount of sensors. Typically, most methods require global measurements to extract the properties of the structure. However, some sensors, like LVDT, cannot be used due to in situ limitation so that the global deformation remains unknown. An experiment is used to demonstrate the proposed algorithms: a one-story 2-bay reinforce concrete frame under weak and strong seismic excitation. In this paper signal processing techniques and nonlinear identification are used and applied to the response measurements of seismic response of reinforced concrete structures subject to different level of earthquake excitations. Both modal-based and signal-based system identification and feature extraction techniques are used to study the nonlinear inelastic response of RC frame using both input and output response data or output only measurement. From the signal-based damage identification method, which include the enhancement of time-frequency analysis of acceleration responses and the estimation of permanent deformation using directly from acceleration response data. Finally, local deformation measurement from dense optical tractor is also use to quantify the damage of the RC frame structure.

Electron Flux Models for Different Energies at Geostationary Orbit

NASA Technical Reports Server (NTRS)

Boynton, R. J.; Balikhin, M. A.; Sibeck, D. G.; Walker, S. N.; Billings, S. A.; Ganushkina, N.

2016-01-01

Forecast models were derived for energetic electrons at all energy ranges sampled by the third-generation Geostationary Operational Environmental Satellites (GOES). These models were based on Multi-Input Single-Output Nonlinear Autoregressive Moving Average with Exogenous inputs methodologies. The model inputs include the solar wind velocity, density and pressure, the fraction of time that the interplanetary magnetic field (IMF) was southward, the IMF contribution of a solar wind-magnetosphere coupling function proposed by Boynton et al. (2011b), and the Dst index. As such, this study has deduced five new 1 h resolution models for the low-energy electrons measured by GOES (30-50 keV, 50-100 keV, 100-200 keV, 200-350 keV, and 350-600 keV) and extended the existing >800 keV and >2 MeV Geostationary Earth Orbit electron fluxes models to forecast at a 1 h resolution. All of these models were shown to provide accurate forecasts, with prediction efficiencies ranging between 66.9% and 82.3%.

Tunable room temperature THz sources based on nonlinear mixing in a hybrid optical and THz micro-ring resonator.

PubMed

Sinha, Raju; Karabiyik, Mustafa; Al-Amin, Chowdhury; Vabbina, Phani K; Güney, Durdu Ö; Pala, Nezih

2015-03-24

We propose and systematically investigate a novel tunable, compact room temperature terahertz (THz) source based on difference frequency generation in a hybrid optical and THz micro-ring resonator. We describe detailed design steps of the source capable of generating THz wave in 0.5-10 THz with a tunability resolution of 0.05 THz by using high second order optical susceptibility (χ((2))) in crystals and polymers. In order to enhance THz generation compared to bulk nonlinear material, we employ a nonlinear optical micro-ring resonator with high-Q resonant modes for infrared input waves. Another ring oscillator with the same outer radius underneath the nonlinear ring with an insulation of SiO2 layer supports the generated THz with resonant modes and out-couples them into a THz waveguide. The phase matching condition is satisfied by engineering both the optical and THz resonators with appropriate effective indices. We analytically estimate THz output power of the device by using practical values of susceptibility in available crystals and polymers. The proposed source can enable tunable, compact THz emitters, on-chip integrated spectrometers, inspire a broader use of THz sources and motivate many important potential THz applications in different fields.

Tunable Room Temperature THz Sources Based on Nonlinear Mixing in a Hybrid Optical and THz Micro-Ring Resonator

PubMed Central

Sinha, Raju; Karabiyik, Mustafa; Al-Amin, Chowdhury; Vabbina, Phani K.; Güney, Durdu Ö.; Pala, Nezih

2015-01-01

We propose and systematically investigate a novel tunable, compact room temperature terahertz (THz) source based on difference frequency generation in a hybrid optical and THz micro-ring resonator. We describe detailed design steps of the source capable of generating THz wave in 0.5–10 THz with a tunability resolution of 0.05 THz by using high second order optical susceptibility (χ(2)) in crystals and polymers. In order to enhance THz generation compared to bulk nonlinear material, we employ a nonlinear optical micro-ring resonator with high-Q resonant modes for infrared input waves. Another ring oscillator with the same outer radius underneath the nonlinear ring with an insulation of SiO2 layer supports the generated THz with resonant modes and out-couples them into a THz waveguide. The phase matching condition is satisfied by engineering both the optical and THz resonators with appropriate effective indices. We analytically estimate THz output power of the device by using practical values of susceptibility in available crystals and polymers. The proposed source can enable tunable, compact THz emitters, on-chip integrated spectrometers, inspire a broader use of THz sources and motivate many important potential THz applications in different fields. PMID:25800287

Properties of Miniature Cantilever-Type Ultrasonic Motor Using Lead-Free Array-Type Multilayer Piezoelectric Ceramics of (Sr,Ca)2NaNb5O15 under High Input Power

NASA Astrophysics Data System (ADS)

Doshida, Yutaka; Shimizu, Hiroyuki; Mizuno, Youich; Tamura, Hideki

2012-07-01

The properties of miniature cantilever-type ultrasonic motors using lead-free array-type multilayer piezoelectric ceramics of (Sr,Ca)2NaNb5O15 (SCNN) developed using the design rule were investigated under high input power by comparison with the high-power properties of SCNN ceramics. The frequency dependence of the revolution speed reflected the nonlinear behavior of SCNN ceramics with the hard-spring effect and showed a mirror-reversed image relative to that of the motor of Pb(Zr,Ti)O3 (PZT) ceramics. The output power increased linearly with increasing input power up to 110 mW without heat generation, and the driving properties were almost the same as the expectations under low input power. The output power density characteristics of the motors were high in comparison with those of the commercialized motors of PZT ceramics. It appeared that the motors have a high potential as an environmental friendly piezoelectric device with excellent properties, reflecting the high-power properties of SCNN ceramics.

REXOR 2 rotorcraft simulation model. Volume 1: Engineering documentation

NASA Technical Reports Server (NTRS)

Reaser, J. S.; Kretsinger, P. H.

1978-01-01

A rotorcraft nonlinear simulation called REXOR II, divided into three volumes, is described. The first volume is a development of rotorcraft mechanics and aerodynamics. The second is a development and explanation of the computer code required to implement the equations of motion. The third volume is a user's manual, and contains a description of code input/output as well as operating instructions.

Multi-input multioutput orthogonal frequency division multiplexing radar waveform design for improving the detection performance of space-time adaptive processing

NASA Astrophysics Data System (ADS)

Wang, Hongyan

2017-04-01

This paper addresses the waveform optimization problem for improving the detection performance of multi-input multioutput (MIMO) orthogonal frequency division multiplexing (OFDM) radar-based space-time adaptive processing (STAP) in the complex environment. By maximizing the output signal-to-interference-and-noise-ratio (SINR) criterion, the waveform optimization problem for improving the detection performance of STAP, which is subjected to the constant modulus constraint, is derived. To tackle the resultant nonlinear and complicated optimization issue, a diagonal loading-based method is proposed to reformulate the issue as a semidefinite programming one; thereby, this problem can be solved very efficiently. In what follows, the optimized waveform can be obtained to maximize the output SINR of MIMO-OFDM such that the detection performance of STAP can be improved. The simulation results show that the proposed method can improve the output SINR detection performance considerably as compared with that of uncorrelated waveforms and the existing MIMO-based STAP method.

Prediction of AL and Dst Indices from ACE Measurements Using Hybrid Physics/Black-Box Techniques

NASA Astrophysics Data System (ADS)

Spencer, E.; Rao, A.; Horton, W.; Mays, L.

2008-12-01

ACE measurements of the solar wind velocity, IMF and proton density is used to drive a hybrid Physics/Black- Box model of the nightside magnetosphere. The core physics is contained in a low order nonlinear dynamical model of the nightside magnetosphere called WINDMI. The model is augmented by wavelet based nonlinear mappings between the solar wind quantities and the input into the physics model, followed by further wavelet based mappings of the model output field aligned currents onto the ground based magnetometer measurements of the AL index and Dst index. The black box mappings are introduced at the input stage to account for uncertainties in the way the solar wind quantities are transported from the ACE spacecraft at L1 to the magnetopause. Similar mappings are introduced at the output stage to account for a spatially and temporally varying westward auroral electrojet geometry. The parameters of the model are tuned using a genetic algorithm, and trained using the large geomagnetic storm dataset of October 3-7 2000. It's predictive performance is then evaluated on subsequent storm datasets, in particular the April 15-24 2002 storm. This work is supported by grant NSF 7020201

Automated Cryocooler Monitor and Control System

NASA Technical Reports Server (NTRS)

Britcliffe, Michael J.; Hanscon, Theodore R.; Fowler, Larry E.

2011-01-01

A system was designed to automate cryogenically cooled low-noise amplifier systems used in the NASA Deep Space Network. It automates the entire operation of the system including cool-down, warm-up, and performance monitoring. The system is based on a single-board computer with custom software and hardware to monitor and control the cryogenic operation of the system. The system provides local display and control, and can be operated remotely via a Web interface. The system controller is based on a commercial single-board computer with onboard data acquisition capability. The commercial hardware includes a microprocessor, an LCD (liquid crystal display), seven LED (light emitting diode) displays, a seven-key keypad, an Ethernet interface, 40 digital I/O (input/output) ports, 11 A/D (analog to digital) inputs, four D/A (digital to analog) outputs, and an external relay board to control the high-current devices. The temperature sensors used are commercial silicon diode devices that provide a non-linear voltage output proportional to temperature. The devices are excited with a 10-microamp bias current. The system is capable of monitoring and displaying three temperatures. The vacuum sensors are commercial thermistor devices. The output of the sensors is a non-linear voltage proportional to vacuum pressure in the 1-Torr to 1-millitorr range. Two sensors are used. One measures the vacuum pressure in the cryocooler and the other the pressure at the input to the vacuum pump. The helium pressure sensor is a commercial device that provides a linear voltage output from 1 to 5 volts, corresponding to a gas pressure from 0 to 3.5 MPa (approx. = 500 psig). Control of the vacuum process is accomplished with a commercial electrically operated solenoid valve. A commercial motor starter is used to control the input power of the compressor. The warm-up heaters are commercial power resistors sized to provide the appropriate power for the thermal mass of the particular system, and typically provide 50 watts of heat. There are four basic operating modes. "Cool " mode commands the system to cool to normal operating temperature. "Heat " mode is used to warm the device to a set temperature near room temperature. "Pump " mode is a maintenance function that allows the vacuum system to be operated alone to remove accumulated contaminants from the vacuum area. In "Off " mode, no power is applied to the system.

Evolving RBF neural networks for adaptive soft-sensor design.

PubMed

Alexandridis, Alex

2013-12-01

This work presents an adaptive framework for building soft-sensors based on radial basis function (RBF) neural network models. The adaptive fuzzy means algorithm is utilized in order to evolve an RBF network, which approximates the unknown system based on input-output data from it. The methodology gradually builds the RBF network model, based on two separate levels of adaptation: On the first level, the structure of the hidden layer is modified by adding or deleting RBF centers, while on the second level, the synaptic weights are adjusted with the recursive least squares with exponential forgetting algorithm. The proposed approach is tested on two different systems, namely a simulated nonlinear DC Motor and a real industrial reactor. The results show that the produced soft-sensors can be successfully applied to model the two nonlinear systems. A comparison with two different adaptive modeling techniques, namely a dynamic evolving neural-fuzzy inference system (DENFIS) and neural networks trained with online backpropagation, highlights the advantages of the proposed methodology.

A Scheme for Obtaining Secure S-Boxes Based on Chaotic Baker's Map

NASA Astrophysics Data System (ADS)

Gondal, Muhammad Asif; Abdul Raheem; Hussain, Iqtadar

2014-09-01

In this paper, a method for obtaining cryptographically strong 8 × 8 substitution boxes (S-boxes) is presented. The method is based on chaotic baker's map and a "mini version" of a new block cipher with block size 8 bits and can be easily and efficiently performed on a computer. The cryptographic strength of some 8 × 8 S-boxes randomly produced by the method is analyzed. The results show (1) all of them are bijective; (2) the nonlinearity of each output bit of them is usually about 100; (3) all of them approximately satisfy the strict avalanche criterion and output bits independence criterion; (4) they all have an almost equiprobable input/output XOR distribution.

Output synchronization of discrete-time dynamical networks based on geometrically incremental dissipativity.

PubMed

Li, Chensong; Zhao, Jun

2017-01-01

In this work, we investigate the output synchronization problem for discrete-time dynamical networks with identical nodes. Firstly, if each node of a network is geometrically incrementally dissipative, the entire network can be viewed as a geometrically dissipative nonlinear system by choosing a particular input-output pair. Then, based on the geometrical dissipativity property, we consider two cases: output synchronization under arbitrary topology and switching topology, respectively. For the first case, we establish several criteria of output synchronization under arbitrary switching between a set of connection topologies by employing a common Lyapunov function. For the other case, we give the design method of a switching signal to achieve output synchronization even if all subnetworks are not synchronous. Finally, an example is provided to illustrate the effectiveness of the main results. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Closed-loop suppression of chaos in nonlinear driven oscillators

NASA Astrophysics Data System (ADS)

Aguirre, L. A.; Billings, S. A.

1995-05-01

This paper discusses the suppression of chaos in nonlinear driven oscillators via the addition of a periodic perturbation. Given a system originally undergoing chaotic motions, it is desired that such a system be driven to some periodic orbit. This can be achieved by the addition of a weak periodic signal to the oscillator input. This is usually accomplished in open loop, but this procedure presents some difficulties which are discussed in the paper. To ensure that this is attained despite uncertainties and possible disturbances on the system, a procedure is suggested to perform control in closed loop. In addition, it is illustrated how a model, estimated from input/output data, can be used in the design. Numerical examples which use the Duffing-Ueda and modified van der Pol oscillators are included to illustrate some of the properties of the new approach.

Multidimensional density shaping by sigmoids.

PubMed

Roth, Z; Baram, Y

1996-01-01

An estimate of the probability density function of a random vector is obtained by maximizing the output entropy of a feedforward network of sigmoidal units with respect to the input weights. Classification problems can be solved by selecting the class associated with the maximal estimated density. Newton's optimization method, applied to the estimated density, yields a recursive estimator for a random variable or a random sequence. A constrained connectivity structure yields a linear estimator, which is particularly suitable for "real time" prediction. A Gaussian nonlinearity yields a closed-form solution for the network's parameters, which may also be used for initializing the optimization algorithm when other nonlinearities are employed. A triangular connectivity between the neurons and the input, which is naturally suggested by the statistical setting, reduces the number of parameters. Applications to classification and forecasting problems are demonstrated.

Nonlinear response of silicon photonic crystal microresonators excited via an integrated waveguide and fiber taper.

PubMed

Barclay, Paul; Srinivasan, Kartik; Painter, Oskar

2005-02-07

A technique is demonstrated which efficiently transfers light between a tapered standard single-mode optical fiber and a high-Q, ultra-small mode volume, silicon photonic crystal resonant cavity. Cavity mode quality factors of 4.7x10(4) are measured, and a total fiber-to-cavity coupling efficiency of 44% is demonstrated. Using this efficient cavity input and output channel, the steady-state nonlinear absorption and dispersion of the photonic crystal cavity is studied. Optical bistability is observed for fiber input powers as low as 250 microW, corresponding to a dropped power of 100 microW and 3 fJ of stored cavity energy. A high-density effective free-carrier lifetime for these silicon photonic crystal resonators of ~ 0.5 ns is also estimated from power dependent loss and dispersion measurements.

Dendritic Na+ spikes enable cortical input to drive action potential output from hippocampal CA2 pyramidal neurons

PubMed Central

Sun, Qian; Srinivas, Kalyan V; Sotayo, Alaba; Siegelbaum, Steven A

2014-01-01

Synaptic inputs from different brain areas are often targeted to distinct regions of neuronal dendritic arbors. Inputs to proximal dendrites usually produce large somatic EPSPs that efficiently trigger action potential (AP) output, whereas inputs to distal dendrites are greatly attenuated and may largely modulate AP output. In contrast to most other cortical and hippocampal neurons, hippocampal CA2 pyramidal neurons show unusually strong excitation by their distal dendritic inputs from entorhinal cortex (EC). In this study, we demonstrate that the ability of these EC inputs to drive CA2 AP output requires the firing of local dendritic Na+ spikes. Furthermore, we find that CA2 dendritic geometry contributes to the efficient coupling of dendritic Na+ spikes to AP output. These results provide a striking example of how dendritic spikes enable direct cortical inputs to overcome unfavorable distal synaptic locale to trigger axonal AP output and thereby enable efficient cortico-hippocampal information flow. DOI: http://dx.doi.org/10.7554/eLife.04551.001 PMID:25390033

Cross-entropy embedding of high-dimensional data using the neural gas model.

PubMed

Estévez, Pablo A; Figueroa, Cristián J; Saito, Kazumi

2005-01-01

A cross-entropy approach to mapping high-dimensional data into a low-dimensional space embedding is presented. The method allows to project simultaneously the input data and the codebook vectors, obtained with the Neural Gas (NG) quantizer algorithm, into a low-dimensional output space. The aim of this approach is to preserve the relationship defined by the NG neighborhood function for each pair of input and codebook vectors. A cost function based on the cross-entropy between input and output probabilities is minimized by using a Newton-Raphson method. The new approach is compared with Sammon's non-linear mapping (NLM) and the hierarchical approach of combining a vector quantizer such as the self-organizing feature map (SOM) or NG with the NLM recall algorithm. In comparison with these techniques, our method delivers a clear visualization of both data points and codebooks, and it achieves a better mapping quality in terms of the topology preservation measure q(m).

Model-Free control performance improvement using virtual reference feedback tuning and reinforcement Q-learning

NASA Astrophysics Data System (ADS)

Radac, Mircea-Bogdan; Precup, Radu-Emil; Roman, Raul-Cristian

2017-04-01

This paper proposes the combination of two model-free controller tuning techniques, namely linear virtual reference feedback tuning (VRFT) and nonlinear state-feedback Q-learning, referred to as a new mixed VRFT-Q learning approach. VRFT is first used to find stabilising feedback controller using input-output experimental data from the process in a model reference tracking setting. Reinforcement Q-learning is next applied in the same setting using input-state experimental data collected under perturbed VRFT to ensure good exploration. The Q-learning controller learned with a batch fitted Q iteration algorithm uses two neural networks, one for the Q-function estimator and one for the controller, respectively. The VRFT-Q learning approach is validated on position control of a two-degrees-of-motion open-loop stable multi input-multi output (MIMO) aerodynamic system (AS). Extensive simulations for the two independent control channels of the MIMO AS show that the Q-learning controllers clearly improve performance over the VRFT controllers.

Controller design for a class of nonlinear MIMO coupled system using multiple models and second level adaptation.

PubMed

Pandey, Vinay Kumar; Kar, Indrani; Mahanta, Chitralekha

2017-07-01

In this paper, an adaptive control method using multiple models with second level adaptation is proposed for a class of nonlinear multi-input multi-output (MIMO) coupled systems. Multiple estimation models are used to tune the unknown parameters at the first level. The second level adaptation provides a single parameter vector for the controller. A feedback linearization technique is used to design a state feedback control. The efficacy of the designed controller is validated by conducting real time experiment on a laboratory setup of twin rotor MIMO system (TRMS). The TRMS setup is discussed in detail and the experiments were performed for regulation and tracking problem for pitch and yaw control using different reference signals. An Extended Kalman Filter (EKF) has been used to observe the unavailable states of the TRMS. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Theoretical and experimental studies of error in square-law detector circuits

NASA Technical Reports Server (NTRS)

Stanley, W. D.; Hearn, C. P.; Williams, J. B.

1984-01-01

Square law detector circuits to determine errors from the ideal input/output characteristic function were investigated. The nonlinear circuit response is analyzed by a power series expansion containing terms through the fourth degree, from which the significant deviation from square law can be predicted. Both fixed bias current and flexible bias current configurations are considered. The latter case corresponds with the situation where the mean current can change with the application of a signal. Experimental investigations of the circuit arrangements are described. Agreement between the analytical models and the experimental results are established. Factors which contribute to differences under certain conditions are outlined.

Boiler-turbine control system design using a genetic algorithm

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dimeo, R.; Lee, K.Y.

1995-12-01

This paper discusses the application of a genetic algorithm to control system design for a boiler-turbine plant. In particular the authors study the ability of the genetic algorithm to develop a proportional-integral (PI) controller and a state feedback controller for a non-linear multi-input/multi-output (MIMO) plant model. The plant model is presented along with a discussion of the inherent difficulties in such controller development. A sketch of the genetic algorithm (GA) is presented and its strategy as a method of control system design is discussed. Results are presented for two different control systems that have been designed with the genetic algorithm.

Evaluation of input output efficiency of oil field considering undesirable output —A case study of sandstone reservoir in Xinjiang oilfield

NASA Astrophysics Data System (ADS)

Zhang, Shuying; Wu, Xuquan; Li, Deshan; Xu, Yadong; Song, Shulin

2017-06-01

Based on the input and output data of sandstone reservoir in Xinjiang oilfield, the SBM-Undesirable model is used to study the technical efficiency of each block. Results show that: the model of SBM-undesirable to evaluate its efficiency and to avoid defects caused by traditional DEA model radial angle, improve the accuracy of the efficiency evaluation. by analyzing the projection of the oil blocks, we find that each block is in the negative external effects of input redundancy and output deficiency benefit and undesirable output, and there are greater differences in the production efficiency of each block; the way to improve the input-output efficiency of oilfield is to optimize the allocation of resources, reduce the undesirable output and increase the expected output.

Responses of magnocellular neurons to osmotic stimulation involves coactivation of excitatory and inhibitory input: an experimental and theoretical analysis.

PubMed

Leng, G; Brown, C H; Bull, P M; Brown, D; Scullion, S; Currie, J; Blackburn-Munro, R E; Feng, J; Onaka, T; Verbalis, J G; Russell, J A; Ludwig, M

2001-09-01

How does a neuron, challenged by an increase in synaptic input, display a response that is independent of the initial level of activity? Here we show that both oxytocin and vasopressin cells in the supraoptic nucleus of normal rats respond to intravenous infusions of hypertonic saline with gradual, linear increases in discharge rate. In hyponatremic rats, oxytocin and vasopressin cells also responded linearly to intravenous infusions of hypertonic saline but with much lower slopes. The linearity of response was surprising, given both the expected nonlinearity of neuronal behavior and the nonlinearity of the oxytocin secretory response to such infusions. We show that a simple computational model can reproduce these responses well, but only if it is assumed that hypertonic infusions coactivate excitatory and inhibitory synaptic inputs. This hypothesis was tested first by applying the GABA(A) antagonist bicuculline to the dendritic zone of the supraoptic nucleus by microdialysis. During local blockade of GABA inputs, the response of oxytocin cells to hypertonic infusion was greatly enhanced. We then went on to directly measure GABA release in the supraoptic nucleus during hypertonic infusion, confirming the predicted rise. Together, the results suggest that hypertonic infusions lead to coactivation of excitatory and inhibitory inputs and that this coactivation may confer appropriate characteristics on the output behavior of oxytocin cells. The nonlinearity of oxytocin secretion that accompanies the linear increase in oxytocin cell firing rate reflects frequency-facilitation of stimulus-secretion coupling at the neurohypophysis.

Effect of nonideal square-law detection on static calibration in noise-injection radiometers

NASA Technical Reports Server (NTRS)

Hearn, C. P.

1984-01-01

The effect of nonideal square-law detection on the static calibration for a class of Dicke radiometers is examined. It is shown that fourth-order curvature in the detection characteristic adds a nonlinear term to the linear calibration relationship normally ascribed to noise-injection, balanced Dicke radiometers. The minimum error, based on an optimum straight-line fit to the calibration curve, is derived in terms of the power series coefficients describing the input-output characteristics of the detector. These coefficients can be determined by simple measurements, and detection nonlinearity is, therefore, quantitatively related to radiometric measurement error.

Metal matrix composite analyzer (METCAN) user's manual, version 4.0

NASA Technical Reports Server (NTRS)

Lee, H.-J.; Gotsis, P. K.; Murthy, P. L. N.; Hopkins, D. A.

1992-01-01

The Metal Matrix Composite Analyzer (METCAN) is a computer code developed at Lewis Research Center to simulate the high temperature nonlinear behavior of metal matrix composites. An updated version of the METCAN User's Manual is presented. The manual provides the user with a step by step outline of the procedure necessary to run METCAN. The preparation of the input file is demonstrated, and the output files are explained. The sample problems are presented to highlight various features of METCAN. An overview of the geometric conventions, micromechanical unit cell, and the nonlinear constitutive relationships is also provided.

Enhancing PTFs with remotely sensed data for multi-scale soil water retention estimation

NASA Astrophysics Data System (ADS)

Jana, Raghavendra B.; Mohanty, Binayak P.

2011-03-01

SummaryUse of remotely sensed data products in the earth science and water resources fields is growing due to increasingly easy availability of the data. Traditionally, pedotransfer functions (PTFs) employed for soil hydraulic parameter estimation from other easily available data have used basic soil texture and structure information as inputs. Inclusion of surrogate/supplementary data such as topography and vegetation information has shown some improvement in the PTF's ability to estimate more accurate soil hydraulic parameters. Artificial neural networks (ANNs) are a popular tool for PTF development, and are usually applied across matching spatial scales of inputs and outputs. However, different hydrologic, hydro-climatic, and contaminant transport models require input data at different scales, all of which may not be easily available from existing databases. In such a scenario, it becomes necessary to scale the soil hydraulic parameter values estimated by PTFs to suit the model requirements. Also, uncertainties in the predictions need to be quantified to enable users to gauge the suitability of a particular dataset in their applications. Bayesian Neural Networks (BNNs) inherently provide uncertainty estimates for their outputs due to their utilization of Markov Chain Monte Carlo (MCMC) techniques. In this paper, we present a PTF methodology to estimate soil water retention characteristics built on a Bayesian framework for training of neural networks and utilizing several in situ and remotely sensed datasets jointly. The BNN is also applied across spatial scales to provide fine scale outputs when trained with coarse scale data. Our training data inputs include ground/remotely sensed soil texture, bulk density, elevation, and Leaf Area Index (LAI) at 1 km resolutions, while similar properties measured at a point scale are used as fine scale inputs. The methodology was tested at two different hydro-climatic regions. We also tested the effect of varying the support scale of the training data for the BNNs by sequentially aggregating finer resolution training data to coarser resolutions, and the applicability of the technique to upscaling problems. The BNN outputs are corrected for bias using a non-linear CDF-matching technique. Final results show good promise of the suitability of this Bayesian Neural Network approach for soil hydraulic parameter estimation across spatial scales using ground-, air-, or space-based remotely sensed geophysical parameters. Inclusion of remotely sensed data such as elevation and LAI in addition to in situ soil physical properties improved the estimation capabilities of the BNN-based PTF in certain conditions.

Integrated all-optical programmable logic array based on semiconductor optical amplifiers.

PubMed

Dong, Wenchan; Huang, Zhuyang; Hou, Jie; Santos, Rui; Zhang, Xinliang

2018-05-01

The all-optical programmable logic array (PLA) is one of the most important optical complex logic devices that can implement combinational logic functions. In this Letter, we propose and experimentally demonstrate an integrated all-optical PLA at the operation speed of 40 Gb/s. The PLA mainly consists of a delay interferometer (DI) and semiconductor optical amplifiers (SOAs) of different lengths. The DI is used to pre-code the input signals and improve the reconfigurability of the scheme. The longer SOAs are nonlinear media for generating canonical logic units (CLUs) using four-wave mixing. The shorter SOAs are used to select the appropriate CLUs by changing the working states; then reconfigurable logic functions can be output directly. The results show that all the CLUs are realized successfully, and the optical signal-to-noise ratios are above 22 dB. The exclusive NOR gate and exclusive OR gate are experimentally demonstrated based on output CLUs.

Nonlinear engine model for idle speed control

DOE Office of Scientific and Technical Information (OSTI.GOV)

Livshiz, M.; Sanvido, D.J.; Stiles, S.D.

1994-12-31

This paper describes a nonlinear model of an engine used for the design of idle speed control and prediction in a broad range of idle speeds and operational conditions. Idle speed control systems make use of both spark advance and the idle air actuator to control engine speed for improved response relative to variations in the target idle speed due to load disturbances. The control system at idle can be presented by a multiple input multiple output (MIMO) nonlinear model. Information of nonlinearities helps to improve performance of the system over the whole range of engine speeds. A proposed simplemore » nonlinear model of the engine at idle was applied for design of optimal controllers and predictors for improved steady state, load rejection and transition from and to idle. This paper describes vehicle results of engine speed prediction based on the described model.« less

Coordinated three-dimensional motion of the head and torso by dynamic neural networks.

PubMed

Kim, J; Hemami, H

1998-01-01

The problem of trajectory tracking control of a three dimensional (3D) model of the human upper torso and head is considered. The torso and the head are modeled as two rigid bodies connected at one point, and the Newton-Euler method is used to derive the nonlinear differential equations that govern the motion of the system. The two-link system is driven by six pairs of muscle like actuators that possess physiologically inspired alpha like and gamma like inputs, and spindle like and Golgi tendon organ like outputs. These outputs are utilized as reflex feedback for stability and stiffness control, in a long loop feedback for the purpose of estimating the state of the system (somesthesis), and as part of the input to the controller. Ideal delays of different duration are included in the feedforward and feedback paths of the system to emulate such delays encountered in physiological systems. Dynamical neural networks are trained to learn effective control of the desired maneuvers of the system. The feasibility of the controller is demonstrated by computer simulation of the successful execution of the desired maneuvers. This work demonstrates the capabilities of neural circuits in controlling highly nonlinear systems with multidelays in their feedforward and feedback paths. The ultimate long range goal of this research is toward understanding the working of the central nervous system in controlling movement. It is an interdisciplinary effort relying on mechanics, biomechanics, neuroscience, system theory, physiology and anatomy, and its short range relevance to rehabilitation must be noted.

Maximum likelihood identification and optimal input design for identifying aircraft stability and control derivatives

NASA Technical Reports Server (NTRS)

Stepner, D. E.; Mehra, R. K.

1973-01-01

A new method of extracting aircraft stability and control derivatives from flight test data is developed based on the maximum likelihood cirterion. It is shown that this new method is capable of processing data from both linear and nonlinear models, both with and without process noise and includes output error and equation error methods as special cases. The first application of this method to flight test data is reported for lateral maneuvers of the HL-10 and M2/F3 lifting bodies, including the extraction of stability and control derivatives in the presence of wind gusts. All the problems encountered in this identification study are discussed. Several different methods (including a priori weighting, parameter fixing and constrained parameter values) for dealing with identifiability and uniqueness problems are introduced and the results given. The method for the design of optimal inputs for identifying the parameters of linear dynamic systems is also given. The criterion used for the optimization is the sensitivity of the system output to the unknown parameters. Several simple examples are first given and then the results of an extensive stability and control dervative identification simulation for a C-8 aircraft are detailed.

The iso-response method: measuring neuronal stimulus integration with closed-loop experiments

PubMed Central

Gollisch, Tim; Herz, Andreas V. M.

2012-01-01

Throughout the nervous system, neurons integrate high-dimensional input streams and transform them into an output of their own. This integration of incoming signals involves filtering processes and complex non-linear operations. The shapes of these filters and non-linearities determine the computational features of single neurons and their functional roles within larger networks. A detailed characterization of signal integration is thus a central ingredient to understanding information processing in neural circuits. Conventional methods for measuring single-neuron response properties, such as reverse correlation, however, are often limited by the implicit assumption that stimulus integration occurs in a linear fashion. Here, we review a conceptual and experimental alternative that is based on exploring the space of those sensory stimuli that result in the same neural output. As demonstrated by recent results in the auditory and visual system, such iso-response stimuli can be used to identify the non-linearities relevant for stimulus integration, disentangle consecutive neural processing steps, and determine their characteristics with unprecedented precision. Automated closed-loop experiments are crucial for this advance, allowing rapid search strategies for identifying iso-response stimuli during experiments. Prime targets for the method are feed-forward neural signaling chains in sensory systems, but the method has also been successfully applied to feedback systems. Depending on the specific question, “iso-response” may refer to a predefined firing rate, single-spike probability, first-spike latency, or other output measures. Examples from different studies show that substantial progress in understanding neural dynamics and coding can be achieved once rapid online data analysis and stimulus generation, adaptive sampling, and computational modeling are tightly integrated into experiments. PMID:23267315

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chinthavali, Madhu Sudhan; Campbell, Steven L

This paper presents an analytical model for wireless power transfer system used in electric vehicle application. The equivalent circuit model for each major component of the system is described, including the input voltage source, resonant network, transformer, nonlinear diode rectifier load, etc. Based on the circuit model, the primary side compensation capacitance, equivalent input impedance, active / reactive power are calculated, which provides a guideline for parameter selection. Moreover, the voltage gain curve from dc output to dc input is derived as well. A hardware prototype with series-parallel resonant stage is built to verify the developed model. The experimental resultsmore » from the hardware are compared with the model predicted results to show the validity of the model.« less

Versatile current-mode universal biquadratic filter using DO-CCIIs

NASA Astrophysics Data System (ADS)

Chen, Hua-Pin

2013-07-01

In this article, a new three-input and three-output versatile current-mode universal biquadratic filter is proposed. The circuit employs three dual-output current conveyors (DO-CCIIs) as active elements together with three grounded resistors and two grounded capacitors. The proposed configuration exhibits low-input impedance and high-output impedance which is important for easy cascading in the current-mode operations. It can be used as either a single-input and three-output or three-input and two-output circuit. In the operation of single-input and three-output circuit, the lowpass, bandpass and bandreject can be realised simultaneously, while the highpass filtering response can be easily obtained by connecting appropriated output current directly without using addition stages. In the operation of three-input and two-output circuit, all five generic filtering functions can be easily realised by selecting different three input current signals. The filter permits orthogonal controllability of the quality factor and resonance angular frequency, and no component matching conditions or inverting-type input current signals are imposed. All the passive and active sensitivities are low. HSPICE simulation results based on using TSMC 0.18 µm 1P6M CMOS process technology and supply voltages ±0.9 V to verify the theoretical analysis.

Ultra-wideband short-pulse radar with range accuracy for short range detection

DOEpatents

Rodenbeck, Christopher T; Pankonin, Jeffrey; Heintzleman, Richard E; Kinzie, Nicola Jean; Popovic, Zorana P

2014-10-07

An ultra-wideband (UWB) radar transmitter apparatus comprises a pulse generator configured to produce from a sinusoidal input signal a pulsed output signal having a series of baseband pulses with a first pulse repetition frequency (PRF). The pulse generator includes a plurality of components that each have a nonlinear electrical reactance. A signal converter is coupled to the pulse generator and configured to convert the pulsed output signal into a pulsed radar transmit signal having a series of radar transmit pulses with a second PRF that is less than the first PRF.

Non-linear blend coding in the moth antennal lobe emerges from random glomerular networks

PubMed Central

Capurro, Alberto; Baroni, Fabiano; Olsson, Shannon B.; Kuebler, Linda S.; Karout, Salah; Hansson, Bill S.; Pearce, Timothy C.

2012-01-01

Neural responses to odor blends often exhibit non-linear interactions to blend components. The first olfactory processing center in insects, the antennal lobe (AL), exhibits a complex network connectivity. We attempt to determine if non-linear blend interactions can arise purely as a function of the AL network connectivity itself, without necessitating additional factors such as competitive ligand binding at the periphery or intrinsic cellular properties. To assess this, we compared blend interactions among responses from single neurons recorded intracellularly in the AL of the moth Manduca sexta with those generated using a population-based computational model constructed from the morphologically based connectivity pattern of projection neurons (PNs) and local interneurons (LNs) with randomized connection probabilities from which we excluded detailed intrinsic neuronal properties. The model accurately predicted most of the proportions of blend interaction types observed in the physiological data. Our simulations also indicate that input from LNs is important in establishing both the type of blend interaction and the nature of the neuronal response (excitation or inhibition) exhibited by AL neurons. For LNs, the only input that significantly impacted the blend interaction type was received from other LNs, while for PNs the input from olfactory sensory neurons and other PNs contributed agonistically with the LN input to shape the AL output. Our results demonstrate that non-linear blend interactions can be a natural consequence of AL connectivity, and highlight the importance of lateral inhibition as a key feature of blend coding to be addressed in future experimental and computational studies. PMID:22529799

Nonlinear Modeling of Causal Interrelationships in Neuronal Ensembles

PubMed Central

Zanos, Theodoros P.; Courellis, Spiros H.; Berger, Theodore W.; Hampson, Robert E.; Deadwyler, Sam A.; Marmarelis, Vasilis Z.

2009-01-01

The increasing availability of multiunit recordings gives new urgency to the need for effective analysis of “multidimensional” time-series data that are derived from the recorded activity of neuronal ensembles in the form of multiple sequences of action potentials—treated mathematically as point-processes and computationally as spike-trains. Whether in conditions of spontaneous activity or under conditions of external stimulation, the objective is the identification and quantification of possible causal links among the neurons generating the observed binary signals. A multiple-input/multiple-output (MIMO) modeling methodology is presented that can be used to quantify the neuronal dynamics of causal interrelationships in neuronal ensembles using spike-train data recorded from individual neurons. These causal interrelationships are modeled as transformations of spike-trains recorded from a set of neurons designated as the “inputs” into spike-trains recorded from another set of neurons designated as the “outputs.” The MIMO model is composed of a set of multiinput/single-output (MISO) modules, one for each output. Each module is the cascade of a MISO Volterra model and a threshold operator generating the output spikes. The Laguerre expansion approach is used to estimate the Volterra kernels of each MISO module from the respective input–output data using the least-squares method. The predictive performance of the model is evaluated with the use of the receiver operating characteristic (ROC) curve, from which the optimum threshold is also selected. The Mann–Whitney statistic is used to select the significant inputs for each output by examining the statistical significance of improvements in the predictive accuracy of the model when the respective inputs is included. Illustrative examples are presented for a simulated system and for an actual application using multiunit data recordings from the hippocampus of a behaving rat. PMID:18701382

670 GHz Schottky Diode Based Subharmonic Mixer with CPW Circuits and 70 GHz IF

NASA Technical Reports Server (NTRS)

Chattopadhyay, Goutam (Inventor); Schlecht, Erich T. (Inventor); Lee, Choonsup (Inventor); Lin, Robert H. (Inventor); Gill, John J. (Inventor); Sin, Seth (Inventor); Mehdi, Imran (Inventor)

2014-01-01

A coplanar waveguide (CPW) based subharmonic mixer working at 670 GHz using GaAs Schottky diodes. One example of the mixer has a LO input, an RF input and an IF output. Another possible mixer has a LO input, and IF input and an RF output. Each input or output is connected to a coplanar waveguide with a matching network. A pair of antiparallel diodes provides a signal at twice the LO frequency, which is then mixed with a second signal to provide signals having sum and difference frequencies. The output signal of interest is received after passing through a bandpass filter tuned to the frequency range of interest.

The spatial structure of a nonlinear receptive field.

PubMed

Schwartz, Gregory W; Okawa, Haruhisa; Dunn, Felice A; Morgan, Josh L; Kerschensteiner, Daniel; Wong, Rachel O; Rieke, Fred

2012-11-01

Understanding a sensory system implies the ability to predict responses to a variety of inputs from a common model. In the retina, this includes predicting how the integration of signals across visual space shapes the outputs of retinal ganglion cells. Existing models of this process generalize poorly to predict responses to new stimuli. This failure arises in part from properties of the ganglion cell response that are not well captured by standard receptive-field mapping techniques: nonlinear spatial integration and fine-scale heterogeneities in spatial sampling. Here we characterize a ganglion cell's spatial receptive field using a mechanistic model based on measurements of the physiological properties and connectivity of only the primary excitatory circuitry of the retina. The resulting simplified circuit model successfully predicts ganglion-cell responses to a variety of spatial patterns and thus provides a direct correspondence between circuit connectivity and retinal output.

PROGRAM VSAERO: A computer program for calculating the non-linear aerodynamic characteristics of arbitrary configurations: User's manual

NASA Technical Reports Server (NTRS)

Maskew, B.

1982-01-01

VSAERO is a computer program used to predict the nonlinear aerodynamic characteristics of arbitrary three-dimensional configurations in subsonic flow. Nonlinear effects of vortex separation and vortex surface interaction are treated in an iterative wake-shape calculation procedure, while the effects of viscosity are treated in an iterative loop coupling potential-flow and integral boundary-layer calculations. The program employs a surface singularity panel method using quadrilateral panels on which doublet and source singularities are distributed in a piecewise constant form. This user's manual provides a brief overview of the mathematical model, instructions for configuration modeling and a description of the input and output data. A listing of a sample case is included.

Expanding the occupational health methodology: A concatenated artificial neural network approach to model the burnout process in Chinese nurses.

PubMed

Ladstätter, Felix; Garrosa, Eva; Moreno-Jiménez, Bernardo; Ponsoda, Vicente; Reales Aviles, José Manuel; Dai, Junming

2016-01-01

Artificial neural networks are sophisticated modelling and prediction tools capable of extracting complex, non-linear relationships between predictor (input) and predicted (output) variables. This study explores this capacity by modelling non-linearities in the hardiness-modulated burnout process with a neural network. Specifically, two multi-layer feed-forward artificial neural networks are concatenated in an attempt to model the composite non-linear burnout process. Sensitivity analysis, a Monte Carlo-based global simulation technique, is then utilised to examine the first-order effects of the predictor variables on the burnout sub-dimensions and consequences. Results show that (1) this concatenated artificial neural network approach is feasible to model the burnout process, (2) sensitivity analysis is a prolific method to study the relative importance of predictor variables and (3) the relationships among variables involved in the development of burnout and its consequences are to different degrees non-linear. Many relationships among variables (e.g., stressors and strains) are not linear, yet researchers use linear methods such as Pearson correlation or linear regression to analyse these relationships. Artificial neural network analysis is an innovative method to analyse non-linear relationships and in combination with sensitivity analysis superior to linear methods.

Application of symbolic/numeric matrix solution techniques to the NASTRAN program

NASA Technical Reports Server (NTRS)

Buturla, E. M.; Burroughs, S. H.

1977-01-01

The matrix solving algorithm of any finite element algorithm is extremely important since solution of the matrix equations requires a large amount of elapse time due to null calculations and excessive input/output operations. An alternate method of solving the matrix equations is presented. A symbolic processing step followed by numeric solution yields the solution very rapidly and is especially useful for nonlinear problems.

Tunable protease-activatable virus nanonodes.

PubMed

Judd, Justin; Ho, Michelle L; Tiwari, Abhinav; Gomez, Eric J; Dempsey, Christopher; Van Vliet, Kim; Igoshin, Oleg A; Silberg, Jonathan J; Agbandje-McKenna, Mavis; Suh, Junghae

2014-05-27

We explored the unique signal integration properties of the self-assembling 60-mer protein capsid of adeno-associated virus (AAV), a clinically proven human gene therapy vector, by engineering proteolytic regulation of virus-receptor interactions such that processing of the capsid by proteases is required for infection. We find the transfer function of our engineered protease-activatable viruses (PAVs), relating the degree of proteolysis (input) to PAV activity (output), is highly nonlinear, likely due to increased polyvalency. By exploiting this dynamic polyvalency, in combination with the self-assembly properties of the virus capsid, we show that mosaic PAVs can be constructed that operate under a digital AND gate regime, where two different protease inputs are required for virus activation. These results show viruses can be engineered as signal-integrating nanoscale nodes whose functional properties are regulated by multiple proteolytic signals with easily tunable and predictable response surfaces, a promising development toward advanced control of gene delivery.

Tunable Protease-Activatable Virus Nanonodes

PubMed Central

2015-01-01

We explored the unique signal integration properties of the self-assembling 60-mer protein capsid of adeno-associated virus (AAV), a clinically proven human gene therapy vector, by engineering proteolytic regulation of virus–receptor interactions such that processing of the capsid by proteases is required for infection. We find the transfer function of our engineered protease-activatable viruses (PAVs), relating the degree of proteolysis (input) to PAV activity (output), is highly nonlinear, likely due to increased polyvalency. By exploiting this dynamic polyvalency, in combination with the self-assembly properties of the virus capsid, we show that mosaic PAVs can be constructed that operate under a digital AND gate regime, where two different protease inputs are required for virus activation. These results show viruses can be engineered as signal-integrating nanoscale nodes whose functional properties are regulated by multiple proteolytic signals with easily tunable and predictable response surfaces, a promising development toward advanced control of gene delivery. PMID:24796495

Exact nonlinear model reduction for a von Kármán beam: Slow-fast decomposition and spectral submanifolds

NASA Astrophysics Data System (ADS)

Jain, Shobhit; Tiso, Paolo; Haller, George

2018-06-01

We apply two recently formulated mathematical techniques, Slow-Fast Decomposition (SFD) and Spectral Submanifold (SSM) reduction, to a von Kármán beam with geometric nonlinearities and viscoelastic damping. SFD identifies a global slow manifold in the full system which attracts solutions at rates faster than typical rates within the manifold. An SSM, the smoothest nonlinear continuation of a linear modal subspace, is then used to further reduce the beam equations within the slow manifold. This two-stage, mathematically exact procedure results in a drastic reduction of the finite-element beam model to a one-degree-of freedom nonlinear oscillator. We also introduce the technique of spectral quotient analysis, which gives the number of modes relevant for reduction as output rather than input to the reduction process.

Nonlinear system identification technique validation

NASA Astrophysics Data System (ADS)

Rudko, M.; Bussgang, J. J.

1982-01-01

This final technical report describes the results obtained by SIGNATRON, Inc. of Lexington MA on Air Force Contract F30602-80-C-0104 for Rome Air Development Center. The objective of this effort is to develop a technique for identifying system response of nonlinear circuits by measurements of output response to known inputs. The report describes results of a study into the system identification technique based on the pencil-of-function method previously explored by Jain (1974) and Ewen (1979). The procedure identified roles of the linear response and is intended as a first step in nonlinear response and is intended as a first step in nonlinear circuit identification. There are serious implementation problems associated with the original approach such as loss of accuracy due to repeated integrations, lack of good measures of accuracy and computational iteration to identify the number of poles.

Solitary waves of surface plasmon polariton via phase shifts under Doppler broadening and Kerr nonlinearity

NASA Astrophysics Data System (ADS)

Ahmad, S.; Ahmad, A.; Bacha, B. A.; Khan, A. A.; Abdul Jabar, M. S.

2017-12-01

Surface Plasmon Polaritons (SPPs) are theoretically investigated at the interface of a dielectric metal and gold. The output pulse from the dielectric is used as the input pulse for the generation of SPPs. The SPPs show soliton-like behavior at the interface. The solitary form of a SPP is maintained under the effects of Kerr nonlinearity, Doppler broadening and Fresnel dragging whereas its phase shift is significantly modified. A 0.3radian phase shift is calculated in the presence of both Kerr nonlinearity and Fresnel dragging in the absence of plasma motion. The phase shift is enhanced to 60radian due to the combined effect of Doppler broadening, Kerr nonlinearity and Fresnel dragging. The results may have significant applications in nano-photonics, optical tweezers, photovoltaic devices, plasmonster and sensing technology.

Apparatus and method for detecting and measuring changes in linear relationships between a number of high frequency signals

DOEpatents

Bittner, J.W.; Biscardi, R.W.

1991-03-19

An electronic measurement circuit is disclosed for high speed comparison of the relative amplitudes of a predetermined number of electrical input signals independent of variations in the magnitude of the sum of the signals. The circuit includes a high speed electronic switch that is operably connected to receive on its respective input terminals one of said electrical input signals and to have its common terminal serve as an input for a variable-gain amplifier-detector circuit that is operably connected to feed its output to a common terminal of a second high speed electronic switch. The respective terminals of the second high speed electronic switch are operably connected to a plurality of integrating sample and hold circuits, which in turn have their outputs connected to a summing logic circuit that is operable to develop first, second and third output voltages, the first output voltage being proportional to a predetermined ratio of sums and differences between the compared input signals, the second output voltage being proportional to a second summed ratio of predetermined sums and differences between said input signals, and the third output voltage being proportional to the sum of signals to the summing logic circuit. A servo system that is operably connected to receive said third output signal and compare it with a reference voltage to develop a slowly varying feedback voltage to control the variable-gain amplifier in said common amplifier-detector circuit in order to make said first and second output signals independent of variations in the magnitude of the sum of said input signals. 2 figures.

Apparatus and method for detecting and measuring changes in linear relationships between a number of high frequency signals

DOEpatents

Bittner, John W.; Biscardi, Richard W.

1991-01-01

An electronic measurement circuit for high speed comparison of the relative amplitudes of a predetermined number of electrical input signals independent of variations in the magnitude of the sum of the signals. The circuit includes a high speed electronic switch that is operably connected to receive on its respective input terminals one of said electrical input signals and to have its common terminal serve as an input for a variable-gain amplifier-detector circuit that is operably connected to feed its output to a common terminal of a second high speed electronic switch. The respective terminals of the second high speed electronic switch are operably connected to a plurality of integrating sample and hold circuits, which in turn have their outputs connected to a summing logic circuit that is operable to develop first, second and third output voltages, the first output voltage being proportional to a predetermined ratio of sums and differences between the compared input signals, the second output voltage being proportional to a second summed ratio of predetermined sums and differences between said input signals, and the third output voltage being proportional to the sum of signals to the summing logic circuit. A servo system that is operably connected to receive said third output signal and compare it with a reference voltage to develop a slowly varying feedback voltage to control the variable-gain amplifier in said common amplifier-detector circuit in order to make said first and second output signals independent of variations in the magnitude of the sum of said input signals.

Non-blocking crossbar permutation engine with constant routing latency

NASA Technical Reports Server (NTRS)

Monacos, Steve P. (Inventor)

1994-01-01

The invention is embodied in an N x N crossbar for routing packets from a set of N input ports to a set of N output ports, each packet having a header identifying one of the output ports as its destination, including a plurality of individual links which carry individual packets. Each link has a link input end and a link output end, a plurality of switches. Each of the switches has at least top and bottom switch inputs connected to a corresponding pair of the link output ends and top and bottom switch outputs connected to a corresponding pair of link input ends, whereby each switch is connected to four different links. Each of the switches has an exchange state which routes packets from the top and bottom switch inputs to the bottom and top switch outputs, respectively, and a bypass state which routes packets from the top and bottom switch inputs to the top and bottom switch outputs, respectively. A plurality of individual controller devices governing respective switches for sensing from a header of a packet at each switch input for the identity of the destination output port of the packet and selecting one of the exchange and bypass states in accordance with the identity of the destination output port and with the location of the corresponding switch relative to the destination output port.

MAGNA (Materially and Geometrically Nonlinear Analysis). Part I. Finite Element Analysis Manual.

DTIC Science & Technology

1982-12-01

provided for operating the program, modifying storage caoacity, preparing input data, estimating computer run times , and interpreting the output...7.1.3 Reserved File Names 7.1.16 7.1.4 Typical Execution Times on CDC Computers 7.1.18 7.2 CRAY PROGRAM VERSION 7.2.1 7.2.1 Job Control Language 7.2.1...7.2.2 Modification of Storage Capacity 7.2.8 7.2.3 Execution Times on the CRAY-I Computer 7.2.12 7.3 VAX PROGRAM VERSION 7.3.1 8 INPUT DATA 8.0.1 8.1

Splines and polynomial tools for flatness-based constrained motion planning

NASA Astrophysics Data System (ADS)

Suryawan, Fajar; De Doná, José; Seron, María

2012-08-01

This article addresses the problem of trajectory planning for flat systems with constraints. Flat systems have the useful property that the input and the state can be completely characterised by the so-called flat output. We propose a spline parametrisation for the flat output, the performance output, the states and the inputs. Using this parametrisation the problem of constrained trajectory planning can be cast into a simple quadratic programming problem. An important result is that the B-spline parametrisation used gives exact results for constrained linear continuous-time system. The result is exact in the sense that the constrained signal can be made arbitrarily close to the boundary without having intersampling issues (as one would have in sampled-data systems). Simulation examples are presented, involving the generation of rest-to-rest trajectories. In addition, an experimental result of the method is also presented, where two methods to generate trajectories for a magnetic-levitation (maglev) system in the presence of constraints are compared and each method's performance is discussed. The first method uses the nonlinear model of the plant, which turns out to belong to the class of flat systems. The second method uses a linearised version of the plant model around an operating point. In every case, a continuous-time description is used. The experimental results on a real maglev system reported here show that, in most scenarios, the nonlinear and linearised models produce almost similar, indistinguishable trajectories.

Versatile tunable current-mode universal biquadratic filter using MO-DVCCs and MOSFET-based electronic resistors.

PubMed

Chen, Hua-Pin

2014-01-01

This paper presents a versatile tunable current-mode universal biquadratic filter with four-input and three-output employing only two multioutput differential voltage current conveyors (MO-DVCCs), two grounded capacitors, and a well-known method for replacement of three grounded resistors by MOSFET-based electronic resistors. The proposed configuration exhibits high-output impedance which is important for easy cascading in the current-mode operations. The proposed circuit can be used as either a two-input three-output circuit or a three-input single-output circuit. In the operation of two-input three-output circuit, the bandpass, highpass, and bandreject filtering responses can be realized simultaneously while the allpass filtering response can be easily obtained by connecting appropriated output current directly without using additional stages. In the operation of three-input single-output circuit, all five generic filtering functions can be easily realized by selecting different three-input current signals. The filter permits orthogonal controllability of the quality factor and resonance angular frequency, and no inverting-type input current signals are imposed. All the passive and active sensitivities are low. Postlayout simulations were carried out to verify the functionality of the design.

Versatile Tunable Current-Mode Universal Biquadratic Filter Using MO-DVCCs and MOSFET-Based Electronic Resistors

PubMed Central

2014-01-01

This paper presents a versatile tunable current-mode universal biquadratic filter with four-input and three-output employing only two multioutput differential voltage current conveyors (MO-DVCCs), two grounded capacitors, and a well-known method for replacement of three grounded resistors by MOSFET-based electronic resistors. The proposed configuration exhibits high-output impedance which is important for easy cascading in the current-mode operations. The proposed circuit can be used as either a two-input three-output circuit or a three-input single-output circuit. In the operation of two-input three-output circuit, the bandpass, highpass, and bandreject filtering responses can be realized simultaneously while the allpass filtering response can be easily obtained by connecting appropriated output current directly without using additional stages. In the operation of three-input single-output circuit, all five generic filtering functions can be easily realized by selecting different three-input current signals. The filter permits orthogonal controllability of the quality factor and resonance angular frequency, and no inverting-type input current signals are imposed. All the passive and active sensitivities are low. Postlayout simulations were carried out to verify the functionality of the design. PMID:24982963

Robust input design for nonlinear dynamic modeling of AUV.

PubMed

Nouri, Nowrouz Mohammad; Valadi, Mehrdad

2017-09-01

Input design has a dominant role in developing the dynamic model of autonomous underwater vehicles (AUVs) through system identification. Optimal input design is the process of generating informative inputs that can be used to generate the good quality dynamic model of AUVs. In a problem with optimal input design, the desired input signal depends on the unknown system which is intended to be identified. In this paper, the input design approach which is robust to uncertainties in model parameters is used. The Bayesian robust design strategy is applied to design input signals for dynamic modeling of AUVs. The employed approach can design multiple inputs and apply constraints on an AUV system's inputs and outputs. Particle swarm optimization (PSO) is employed to solve the constraint robust optimization problem. The presented algorithm is used for designing the input signals for an AUV, and the estimate obtained by robust input design is compared with that of the optimal input design. According to the results, proposed input design can satisfy both robustness of constraints and optimality. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Study of cumulative fatigue damage detection for used parts with nonlinear output frequency response functions based on NARMAX modelling

NASA Astrophysics Data System (ADS)

Huang, Honglan; Mao, Hanying; Mao, Hanling; Zheng, Weixue; Huang, Zhenfeng; Li, Xinxin; Wang, Xianghong

2017-12-01

Cumulative fatigue damage detection for used parts plays a key role in the process of remanufacturing engineering and is related to the service safety of the remanufactured parts. In light of the nonlinear properties of used parts caused by cumulative fatigue damage, the based nonlinear output frequency response functions detection approach offers a breakthrough to solve this key problem. First, a modified PSO-adaptive lasso algorithm is introduced to improve the accuracy of the NARMAX model under impulse hammer excitation, and then, an effective new algorithm is derived to estimate the nonlinear output frequency response functions under rectangular pulse excitation, and a based nonlinear output frequency response functions index is introduced to detect the cumulative fatigue damage in used parts. Then, a novel damage detection approach that integrates the NARMAX model and the rectangular pulse is proposed for nonlinear output frequency response functions identification and cumulative fatigue damage detection of used parts. Finally, experimental studies of fatigued plate specimens and used connecting rod parts are conducted to verify the validity of the novel approach. The obtained results reveal that the new approach can detect cumulative fatigue damages of used parts effectively and efficiently and that the various values of the based nonlinear output frequency response functions index can be used to detect the different fatigue damages or working time. Since the proposed new approach can extract nonlinear properties of systems by only a single excitation of the inspected system, it shows great promise for use in remanufacturing engineering applications.

Generation of 1.5-octave intense infrared pulses by nonlinear interactions in DAST crystal

NASA Astrophysics Data System (ADS)

Vicario, C.; Monoszlai, B.; Arisholm, G.; Hauri, C. P.

2015-09-01

Infrared pulses with large spectral width extending from 1.2 to 3.4 μm are generated in the organic crystal DAST (4-N, N-dimethylamino-4‧-N‧-methylstilbazolium tosylate). The input pulse has a central wavelength of 1.5 μm and 65 fs duration. With 2.8 mJ input energy we obtained up to 700 μJ in the broadened spectrum. The output can be easily scaled up in energy by increasing the crystal size together with the energy and the beam size of the pump. The ultra-broad spectrum is ascribed to cascaded second order processes mediated by the exceptionally large effective χ 2 nonlinearity of DAST, but the shape of the spectrum indicates that a delayed χ 3 process may also be involved. Numerical simulations reproduce the experimental results qualitatively and provide an insight in the mechanisms underlying the asymmetric spectral broadening.

Nonlinear model predictive control for chemical looping process

DOE Office of Scientific and Technical Information (OSTI.GOV)

Joshi, Abhinaya; Lei, Hao; Lou, Xinsheng

A control system for optimizing a chemical looping ("CL") plant includes a reduced order mathematical model ("ROM") that is designed by eliminating mathematical terms that have minimal effect on the outcome. A non-linear optimizer provides various inputs to the ROM and monitors the outputs to determine the optimum inputs that are then provided to the CL plant. An estimator estimates the values of various internal state variables of the CL plant. The system has one structure adapted to control a CL plant that only provides pressure measurements in the CL loops A and B, a second structure adapted to amore » CL plant that provides pressure measurements and solid levels in both loops A, and B, and a third structure adapted to control a CL plant that provides full information on internal state variables. A final structure provides a neural network NMPC controller to control operation of loops A and B.« less

A design multifunctional plasmonic optical device by micro ring system

NASA Astrophysics Data System (ADS)

Pornsuwancharoen, N.; Youplao, P.; Amiri, I. S.; Ali, J.; Yupapin, P.

2018-03-01

A multi-function electronic device based on the plasmonic circuit is designed and simulated by using the micro-ring system. From which a nonlinear micro-ring resonator is employed and the selected electronic devices such as rectifier, amplifier, regulator and filter are investigated. A system consists of a nonlinear micro-ring resonator, which is known as a modified add-drop filter and made of an InGaAsP/InP material. The stacked waveguide of an InGaAsP/InP - graphene -gold/silver is formed as a part of the device, the required output signals are formed by the specific control of input signals via the input and add ports. The material and device aspects are reviewed. The simulation results are obtained using the Opti-wave and MATLAB software programs, all device parameters are based on the fabrication technology capability.

Sensitivity analysis of a short distance atmospheric dispersion model applied to the Fukushima disaster

NASA Astrophysics Data System (ADS)

Périllat, Raphaël; Girard, Sylvain; Korsakissok, Irène; Mallet, Vinien

2015-04-01

In a previous study, the sensitivity of a long distance model was analyzed on the Fukushima Daiichi disaster case with the Morris screening method. It showed that a few variables, such as horizontal diffusion coefficient or clouds thickness, have a weak influence on most of the chosen outputs. The purpose of the present study is to apply a similar methodology on the IRSN's operational short distance atmospheric dispersion model, called pX. Atmospheric dispersion models are very useful in case of accidental releases of pollutant to minimize the population exposure during the accident and to obtain an accurate assessment of short and long term environmental and sanitary impact. Long range models are mostly used for consequences assessment while short range models are more adapted to the early phases of the crisis and are used to make prognosis. The Morris screening method was used to estimate the sensitivity of a set of outputs and to rank the inputs by their influences. The input ranking is highly dependent on the considered output, but a few variables seem to have a weak influence on most of them. This first step revealed that interactions and non-linearity are much more pronounced with the short range model than with the long range one. Afterward, the Sobol screening method was used to obtain more quantitative results on the same set of outputs. Using this method was possible for the short range model because it is far less computationally demanding than the long range model. The study also confronts two parameterizations, Doury's and Pasquill's models, to contrast their behavior. The Doury's model seems to excessively inflate the influence of some inputs compared to the Pasquill's model, such as the altitude of emission and the air stability which do not have the same role in the two models. The outputs of the long range model were dominated by only a few inputs. On the contrary, in this study the influence is shared more evenly between the inputs.

Short-Term Memory in Mathematics-Proficient and Mathematics-Disabled Students as a Function of Input-Modality/Output-Modality Pairings.

ERIC Educational Resources Information Center

Webster, Raymond E.

1980-01-01

A significant two-way input modality by output modality interaction suggested that short term memory capacity among the groups differed as a function of the modality used to present the items in combination with the output response required. (Author/CL)

A comparison of two multi-variable integrator windup protection schemes

NASA Technical Reports Server (NTRS)

Mattern, Duane

1993-01-01

Two methods are examined for limit and integrator wind-up protection for multi-input, multi-output linear controllers subject to actuator constraints. The methods begin with an existing linear controller that satisfies the specifications for the nominal, small perturbation, linear model of the plant. The controllers are formulated to include an additional contribution to the state derivative calculations. The first method to be examined is the multi-variable version of the single-input, single-output, high gain, Conventional Anti-Windup (CAW) scheme. Except for the actuator limits, the CAW scheme is linear. The second scheme to be examined, denoted the Modified Anti-Windup (MAW) scheme, uses a scalar to modify the magnitude of the controller output vector while maintaining the vector direction. The calculation of the scalar modifier is a nonlinear function of the controller outputs and the actuator limits. In both cases the constrained actuator is tracked. These two integrator windup protection methods are demonstrated on a turbofan engine control system with five measurements, four control variables, and four actuators. The closed-loop responses of the two schemes are compared and contrasted during limit operation. The issue of maintaining the direction of the controller output vector using the Modified Anti-Windup scheme is discussed and the advantages and disadvantages of both of the IWP methods are presented.

The impact of visual sequencing of graphic symbols on the sentence construction output of children who have acquired language.

PubMed

Alant, Erna; du Plooy, Amelia; Dada, Shakila

2007-01-01

Although the sequence of graphic or pictorial symbols displayed on a communication board can have an impact on the language output of children, very little research has been conducted to describe this. Research in this area is particularly relevant for prioritising the importance of specific visual and graphic features in providing more effective and user-friendly access to communication boards. This study is concerned with understanding the impact ofspecific sequences of graphic symbol input on the graphic and spoken output of children who have acquired language. Forty participants were divided into two comparable groups. Each group was exposed to graphic symbol input with a certain word order sequence. The structure of input was either in typical English word order sequence Subject- Verb-Object (SVO) or in the word order sequence of Subject-Object-Verb (SOV). Both input groups had to answer six questions by using graphic output as well as speech. The findings indicated that there are significant differences in the PCS graphic output patterns of children who are exposed to graphic input in the SOV and SVO sequences. Furthermore, the output produced in the graphic mode differed considerably to the output produced in the spoken mode. Clinical implications of these findings are discussed

Microwave-to-Optical Conversion in WGM Resonators

NASA Technical Reports Server (NTRS)

Savchenkov, Anatoliy; Strekalov, Dmitry; Yu, Nan; Matsko, Andrey; Maleki, Lute

2008-01-01

Microwave-to-optical frequency converters based on whispering-gallery-mode (WGM) resonators have been proposed as mixers for the input ends of microwave receivers in which, downstream of the input ends, signals would be processed photonically. A frequency converter as proposed (see figure) would exploit the nonlinearity of the electromagnetic response of a WGM resonator made of LiNbO3 or another suitable ferroelectric material. Up-conversion would take place by three-wave mixing in the resonator. The WGM resonator would be de - signed and fabricated to obtain (1) resonance at both the microwave and the optical operating frequencies and (2) phase matching among the input and output microwave and optical signals as described in the immediately preceding article. Because the resonator would be all dielectric there would be no metal electrodes signal losses would be very low and, consequently, the resonance quality factors (Q values) of the microwave and optical fields would be very large. The long lifetimes associated with the large Q values would enable attainment of high efficiency of nonlinear interaction with low saturation power. It is anticipated that efficiency would be especially well enhanced by the combination of optical and microwave resonances in operation at input signal frequencies between 90 and 300 GHz.

Dynamic control and information processing in chemical reaction systems by tuning self-organization behavior

NASA Astrophysics Data System (ADS)

Lebiedz, Dirk; Brandt-Pollmann, Ulrich

2004-09-01

Specific external control of chemical reaction systems and both dynamic control and signal processing as central functions in biochemical reaction systems are important issues of modern nonlinear science. For example nonlinear input-output behavior and its regulation are crucial for the maintainance of the life process that requires extensive communication between cells and their environment. An important question is how the dynamical behavior of biochemical systems is controlled and how they process information transmitted by incoming signals. But also from a general point of view external forcing of complex chemical reaction processes is important in many application areas ranging from chemical engineering to biomedicine. In order to study such control issues numerically, here, we choose a well characterized chemical system, the CO oxidation on Pt(110), which is interesting per se as an externally forced chemical oscillator model. We show numerically that tuning of temporal self-organization by input signals in this simple nonlinear chemical reaction exhibiting oscillatory behavior can in principle be exploited for both specific external control of dynamical system behavior and processing of complex information.

Low-complexity piecewise-affine virtual sensors: theory and design

NASA Astrophysics Data System (ADS)

Rubagotti, Matteo; Poggi, Tomaso; Oliveri, Alberto; Pascucci, Carlo Alberto; Bemporad, Alberto; Storace, Marco

2014-03-01

This paper is focused on the theoretical development and the hardware implementation of low-complexity piecewise-affine direct virtual sensors for the estimation of unmeasured variables of interest of nonlinear systems. The direct virtual sensor is designed directly from measured inputs and outputs of the system and does not require a dynamical model. The proposed approach allows one to design estimators which mitigate the effect of the so-called 'curse of dimensionality' of simplicial piecewise-affine functions, and can be therefore applied to relatively high-order systems, enjoying convergence and optimality properties. An automatic toolchain is also presented to generate the VHDL code describing the digital circuit implementing the virtual sensor, starting from the set of measured input and output data. The proposed methodology is applied to generate an FPGA implementation of the virtual sensor for the estimation of vehicle lateral velocity, using a hardware-in-the-loop setting.

A General Simulator Using State Estimation for a Space Tug Navigation System. [computerized simulation, orbital position estimation and flight mechanics

NASA Technical Reports Server (NTRS)

Boland, J. S., III

1975-01-01

A general simulation program is presented (GSP) involving nonlinear state estimation for space vehicle flight navigation systems. A complete explanation of the iterative guidance mode guidance law, derivation of the dynamics, coordinate frames, and state estimation routines are given so as to fully clarify the assumptions and approximations involved so that simulation results can be placed in their proper perspective. A complete set of computer acronyms and their definitions as well as explanations of the subroutines used in the GSP simulator are included. To facilitate input/output, a complete set of compatable numbers, with units, are included to aid in data development. Format specifications, output data phrase meanings and purposes, and computer card data input are clearly spelled out. A large number of simulation and analytical studies were used to determine the validity of the simulator itself as well as various data runs.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Alvarez-Ramirez, J.; Aguilar, R.; Lopez-Isunza, F.

FCC processes involve complex interactive dynamics which are difficult to operate and control as well as poorly known reaction kinetics. This work concerns the synthesis of temperature controllers for FCC units. The problem is addressed first for the case where perfect knowledge of the reaction kinetics is assumed, leading to an input-output linearizing state feedback. However, in most industrial FCC units, perfect knowledge of reaction kinetics and composition measurements is not available. To address the problem of robustness against uncertainties in the reaction kinetics, an adaptive model-based nonlinear controller with simplified reaction models is presented. The adaptive strategy makes usemore » of estimates of uncertainties derived from calorimetric (energy) balances. The resulting controller is similar in form to standard input-output linearizing controllers and can be tuned analogously. Alternatively, the controller can be tuned using a single gain parameter and is computationally efficient. The performance of the closed-loop system and the controller design procedure are shown with simulations.« less

Control of mechanical systems with rolling constraints: Application to dynamic control of mobile robots

NASA Technical Reports Server (NTRS)

Sarkar, Nilanjan; Yun, Xiaoping; Kumar, Vijay

1994-01-01

There are many examples of mechanical systems that require rolling contacts between two or more rigid bodies. Rolling contacts engender nonholonomic constraints in an otherwise holonomic system. In this article, we develop a unified approach to the control of mechanical systems subject to both holonomic and nonholonomic constraints. We first present a state space realization of a constrained system. We then discuss the input-output linearization and zero dynamics of the system. This approach is applied to the dynamic control of mobile robots. Two types of control algorithms for mobile robots are investigated: trajectory tracking and path following. In each case, a smooth nonlinear feedback is obtained to achieve asymptotic input-output stability and Lagrange stability of the overall system. Simulation results are presented to demonstrate the effectiveness of the control algorithms and to compare the performane of trajectory-tracking and path-following algorithms.

Spectral Mixing in Nervous Systems: Experimental Evidenceand Biologically Plausible Circuits

NASA Astrophysics Data System (ADS)

Kleinfeld, D.; Mehta, S. B.

The ability to compute the difference frequency for two periodic signals depends on a nonlinear operation that mixes those signals. Behavioral and psychophysical evidence suggest that such mixing is likely to occur in the vertebrate nervous system as a means to compare rhythmic sensory signals, such as occurs in human audition, and as a means to lock an intrinsic rhythm to a sensory input. Electrophysiological data from electroreceptors in the immobilized electric fish and somatosensory cortex in the anesthetized rat yield direct evidence for such mixing, providing a neurological substrate for the modulation and demodulation of rhythmic neuronal signals. We consider an analytical model of spectral mixing that makes use of the threshold characteristics of neuronal firing and which has features consistent with the experimental observations. This model serves as a guide for constructing circuits that isolate given mixture components. In particular, such circuits can generate nearly pure difference tones from sinusoidal inputs without the use of band-pass filters, in analogy to an image-reject mixer in communications engineering. We speculate that such computations may play a role in coding of sensory input and feedback stabilization of motor output in nervous systems.

Method and apparatus for predicting the direction of movement in machine vision

NASA Technical Reports Server (NTRS)

Lawton, Teri B. (Inventor)

1992-01-01

A computer-simulated cortical network is presented. The network is capable of computing the visibility of shifts in the direction of movement. Additionally, the network can compute the following: (1) the magnitude of the position difference between the test and background patterns; (2) localized contrast differences at different spatial scales analyzed by computing temporal gradients of the difference and sum of the outputs of paired even- and odd-symmetric bandpass filters convolved with the input pattern; and (3) the direction of a test pattern moved relative to a textured background. The direction of movement of an object in the field of view of a robotic vision system is detected in accordance with nonlinear Gabor function algorithms. The movement of objects relative to their background is used to infer the 3-dimensional structure and motion of object surfaces.

A Race Against Time: Time Lags in Terrestrial-Aquatic Linkages

NASA Astrophysics Data System (ADS)

Basu, N. B.

2017-12-01

Unprecedented decreases in atmospheric nitrogen (N) deposition together with increases in agricultural N-use efficiency have led to decreases in net anthropogenic N inputs in many eastern U.S. and Canadian watersheds as well as in Europe. Despite such decreases, N concentrations in streams and rivers continue to increase, and problems of coastal eutrophication remain acute. Such a mismatch between N inputs and outputs can arise due to legacy N accumulation and subsequent lag times between implementation of conservation measures and improvements in water quality. In the present study, we quantified such lag times by pairing long-term N input trajectories with stream N concentration data in multiple watersheds in North America. Results show significant nonlinearity between N inputs and outputs, with a strong hysteresis effect indicative of decadal-scale lag times. Lag times were found to be negatively correlated with both tile drainage and watershed slope, with tile drainage being a dominant control in fall and watershed slope being significant during the spring snowmelt period. Quantification of such lags will be crucial to policy-makers as they struggle to set appropriate goals for water quality improvement in human-impacted watersheds.

Emulation of simulations of atmospheric dispersion at Fukushima for Sobol' sensitivity analysis

NASA Astrophysics Data System (ADS)

Girard, Sylvain; Korsakissok, Irène; Mallet, Vivien

2015-04-01

Polyphemus/Polair3D, from which derives IRSN's operational model ldX, was used to simulate the atmospheric dispersion at the Japan scale of radionuclides after the Fukushima disaster. A previous study with the screening method of Morris had shown that - The sensitivities depend a lot on the considered output; - Only a few of the inputs are non-influential on all considered outputs; - Most influential inputs have either non-linear effects or are interacting. These preliminary results called for a more detailed sensitivity analysis, especially regarding the characterization of interactions. The method of Sobol' allows for a precise evaluation of interactions but requires large simulation samples. Gaussian process emulators for each considered outputs were built in order to relieve this computational burden. Globally aggregated outputs proved to be easy to emulate with high accuracy, and associated Sobol' indices are in broad agreement with previous results obtained with the Morris method. More localized outputs, such as temporal averages of gamma dose rates at measurement stations, resulted in lesser emulator performances: tests simulations could not satisfactorily be reproduced by some emulators. These outputs are of special interest because they can be compared to available observations, for instance for calibration purpose. A thorough inspection of prediction residuals hinted that the model response to wind perturbations often behaved in very distinct regimes relatively to some thresholds. Complementing the initial sample with wind perturbations set to the extreme values allowed for sensible improvement of some of the emulators while other remained too unreliable to be used in a sensitivity analysis. Adaptive sampling or regime-wise emulation could be tried to circumvent this issue. Sobol' indices for local outputs revealed interesting patterns, mostly dominated by the winds, with very high interactions. The emulators will be useful for subsequent studies. Indeed, our goal is to characterize the model output uncertainty but too little information is available about input uncertainties. Hence, calibration of the input distributions with observation and a Bayesian approach seem necessary. This would probably involve methods such as MCMC which would be intractable without emulators.

A metrics for soil hydrological processes and their intrinsic dimensionality in heterogeneous systems

NASA Astrophysics Data System (ADS)

Lischeid, G.; Hohenbrink, T.; Schindler, U.

2012-04-01

Hydrology is based on the observation that catchments process input signals, e.g., precipitation, in a highly deterministic way. Thus, the Darcy or the Richards equation can be applied to model water fluxes in the saturated or vadose zone, respectively. Soils and aquifers usually exhibit substantial spatial heterogeneities at different scales that can, in principle, be represented by corresponding parameterisations of the models. In practice, however, data are hardly available at the required spatial resolution, and accounting for observed heterogeneities of soil and aquifer structure renders models very time and CPU consuming. We hypothesize that the intrinsic dimensionality of soil hydrological processes, which is induced by spatial heterogeneities, actually is very low and that soil hydrological processes in heterogeneous soils follow approximately the same trajectory. That means, the way how the soil transforms any hydrological input signals is the same for different soil textures and structures. Different soils differ only with respect to the extent of transformation of input signals. In a first step, we analysed the output of a soil hydrological model, based on the Richards equation, for homogeneous soils down to 5 m depth for different soil textures. A matrix of time series of soil matrix potential and soil water content at 10 cm depth intervals was set up. The intrinsic dimensionality of that matrix was assessed using the Correlation Dimension and a non-linear principal component approach. The latter provided a metrics for the extent of transformation ("damping") of the input signal. In a second step, model outputs for heterogeneous soils were analysed. In a last step, the same approaches were applied to 55 time series of observed soil water content from 15 sites and different depths. In all cases, the intrinsic dimensionality in fact was very close to unity, confirming our hypothesis. The metrics provided a very efficient tool to quantify the observed behaviour, depending on depth and soil heterogeneity: Different soils differed primarily with respect to the extent of damping per depth interval rather than to the kind of damping. We will show how that metrics can be used in a very efficient way for representing soil heterogeneities in simulation models.

Optimizing Force Deployment and Force Structure for the Rapid Deployment Force

DTIC Science & Technology

1984-03-01

Analysis . . . . .. .. ... ... 97 Experimental Design . . . . . .. .. .. ... 99 IX. Use of a Flexible Response Surface ........ 10.2 Selection of a...setS . ere designe . arun, programming methodology , where the require: s.stem re..r is input and the model optimizes the num=er. :::pe, cargo. an...to obtain new computer outputs" (Ref 38:23). The methodology can be used with any decision model, linear or nonlinear. Experimental Desion Since the

Research in Applied Mathematics Related to Mathematical System Theory.

DTIC Science & Technology

1977-06-01

This report deals with research results obtained in the field of mathematical system theory . Special emphasis was given to the following areas: (1...Linear system theory over a field: parametrization of multi-input, multi-output systems and the geometric structure of classes of systems of...constant dimension. (2) Linear systems over a ring: development of the theory for very general classes of rings. (3) Nonlinear system theory : basic

Modulation linearization of a frequency-modulated voltage controlled oscillator, part 3

NASA Technical Reports Server (NTRS)

Honnell, M. A.

1975-01-01

An analysis is presented for the voltage versus frequency characteristics of a varactor modulated VHF voltage controlled oscillator in which the frequency deviation is linearized by using the nonlinear characteristics of a field effect transistor as a signal amplifier. The equations developed are used to calculate the oscillator output frequency in terms of pertinent circuit parameters. It is shown that the nonlinearity exponent of the FET has a pronounced influence on frequency deviation linearity, whereas the junction exponent of the varactor controls total frequency deviation for a given input signal. A design example for a 250 MHz frequency modulated oscillator is presented.

Theoretical and experimental study on PMD-supported transmission using polarization diversity in coherent optical OFDM systems.

PubMed

Shieh, W; Yi, X; Ma, Y; Tang, Y

2007-08-06

In this paper, we conduct theoretical and experimental study on the PMD-supported transmission with coherent optical orthogonal frequency-division multiplexing (CO-OFDM). We first present the model for the optical fiber communication channel in the presence of the polarization effects. It shows that the optical fiber channel model can be treated as a special kind of multiple-input multiple-output (MIMO) model, namely, a two-input two-output (TITO) model which is intrinsically represented by a two-element Jones vector familiar to the optical communications community. The detailed discussions on various coherent optical MIMO-OFDM (CO-MIMO-OFDM) models are presented. Furthermore, we show the first experiment of polarization-diversity detection in CO-OFDM systems. In particular, a CO-OFDM signal at 10.7 Gb/s is successfully recovered after 900 ps differential-group-delay (DGD) and 1000-km transmission through SSMF fiber without optical dispersion compensation. The transmission experiment with higher-order PMD further confirms the immunity of the CO-OFDM signal to PMD in the transmission fiber. The nonlinearity performance of PMD-supported transmission is also reported. For the first time, nonlinear phase noise mitigation based on receiver digital signal processing is experimentally demonstrated for CO-OFDM transmission.

Using nonlinear forecasting to learn the magnitude and phasing of time-varying sediment suspension in the surf zone

USGS Publications Warehouse

Jaffe, B.E.; Rubin, D.M.

1996-01-01

The time-dependent response of sediment suspension to flow velocity was explored by modeling field measurements collected in the surf zone during a large storm. Linear and nonlinear models were created and tested using flow velocity as input and suspended-sediment concentration as output. A sequence of past velocities (velocity history), as well as velocity from the same instant as the suspended-sediment concentration, was used as input; this velocity history length was allowed to vary. The models also allowed for a lag between input (instantaneous velocity or end of velocity sequence) and output (suspended-sediment concentration). Predictions of concentration from instantaneous velocity or instantaneous velocity raised to a power (up to 8) using linear models were poor (correlation coefficients between predicted and observed concentrations were less than 0.10). Allowing a lag between velocity and concentration improved linear models (correlation coefficient of 0.30), with optimum lag time increasing with elevation above the seabed (from 1.5 s at 13 cm to 8.5 s at 60 cm). These lags are largely due to the time for an observed flow event to effect the bed and mix sediment upward. Using a velocity history further improved linear models (correlation coefficient of 0.43). The best linear model used 12.5 s of velocity history (approximately one wave period) to predict concentration. Nonlinear models gave better predictions than linear models, and, as with linear models, nonlinear models using a velocity history performed better than models using only instantaneous velocity as input. Including a lag time between the velocity and concentration also improved the predictions. The best model (correlation coefficient of 0.58) used 3 s (approximately a quarter wave period) of the cross-shore velocity squared, starting at 4.5 s before the observed concentration, to predict concentration. Using a velocity history increases the performance of the models by specifying a more complete description of the dynamical forcing of the flow (including accelerations and wave phase and shape) responsible for sediment suspension. Incorporating such a velocity history and a lag time into the formulation of the forcing for time-dependent models for sediment suspension in the surf zone will greatly increase our ability to predict suspended-sediment transport.

Geochemical modeling of magma mixing and magma reservoir volumes during early episodes of Kīlauea Volcano's Pu`u `Ō`ō eruption

NASA Astrophysics Data System (ADS)

Shamberger, Patrick J.; Garcia, Michael O.

2007-02-01

Geochemical modeling of magma mixing allows for evaluation of volumes of magma storage reservoirs and magma plumbing configurations. A new analytical expression is derived for a simple two-component box-mixing model describing the proportions of mixing components in erupted lavas as a function of time. Four versions of this model are applied to a mixing trend spanning episodes 3 31 of Kilauea Volcano’s Puu Oo eruption, each testing different constraints on magma reservoir input and output fluxes. Unknown parameters (e.g., magma reservoir influx rate, initial reservoir volume) are optimized for each model using a non-linear least squares technique to fit model trends to geochemical time-series data. The modeled mixing trend closely reproduces the observed compositional trend. The two models that match measured lava effusion rates have constant magma input and output fluxes and suggest a large pre-mixing magma reservoir (46±2 and 49±1 million m3), with little or no volume change over time. This volume is much larger than a previous estimate for the shallow, dike-shaped magma reservoir under the Puu Oo vent, which grew from ˜3 to ˜10 12 million m3. These volumetric differences are interpreted as indicating that mixing occurred first in a larger, deeper reservoir before the magma was injected into the overlying smaller reservoir.

Suspended silica beam splitters on silicon with large core-clad index deference

NASA Astrophysics Data System (ADS)

Zhang, Xiaomin; Armani, Andrea M.

2012-03-01

Optical beam splitters form a fundamental component in integrated optical systems, performing as modulators, interferometers and (de)multiplexers. While silica is a desirable material, because of its low non-linear susceptibility, it is extremely challenging to achieve the requisite core-clad refractive index contrast. In this work, silica splitters with an effective refractive index difference of 25% between the core and clad is demonstrated. The splitter can divide power evenly with low crosstalk from 1520 to 1630nm. In addition, the splitting ratio doesn't change and the output power increases linearly with the input power, which indicates a low susceptibility to thermal effects. The splitter's polarization independent behavior is also verified.

Spectrotemporal Processing in Spectral Tuning Modules of Cat Primary Auditory Cortex

PubMed Central

Atencio, Craig A.; Schreiner, Christoph E.

2012-01-01

Spectral integration properties show topographical order in cat primary auditory cortex (AI). Along the iso-frequency domain, regions with predominantly narrowly tuned (NT) neurons are segregated from regions with more broadly tuned (BT) neurons, forming distinct processing modules. Despite their prominent spatial segregation, spectrotemporal processing has not been compared for these regions. We identified these NT and BT regions with broad-band ripple stimuli and characterized processing differences between them using both spectrotemporal receptive fields (STRFs) and nonlinear stimulus/firing rate transformations. The durations of STRF excitatory and inhibitory subfields were shorter and the best temporal modulation frequencies were higher for BT neurons than for NT neurons. For NT neurons, the bandwidth of excitatory and inhibitory subfields was matched, whereas for BT neurons it was not. Phase locking and feature selectivity were higher for NT neurons. Properties of the nonlinearities showed only slight differences across the bandwidth modules. These results indicate fundamental differences in spectrotemporal preferences - and thus distinct physiological functions - for neurons in BT and NT spectral integration modules. However, some global processing aspects, such as spectrotemporal interactions and nonlinear input/output behavior, appear to be similar for both neuronal subgroups. The findings suggest that spectral integration modules in AI differ in what specific stimulus aspects are processed, but they are similar in the manner in which stimulus information is processed. PMID:22384036

Neural network based adaptive output feedback control: Applications and improvements

NASA Astrophysics Data System (ADS)

Kutay, Ali Turker

Application of recently developed neural network based adaptive output feedback controllers to a diverse range of problems both in simulations and experiments is investigated in this thesis. The purpose is to evaluate the theory behind the development of these controllers numerically and experimentally, identify the needs for further development in practical applications, and to conduct further research in directions that are identified to ultimately enhance applicability of adaptive controllers to real world problems. We mainly focus our attention on adaptive controllers that augment existing fixed gain controllers. A recently developed approach holds great potential for successful implementations on real world applications due to its applicability to systems with minimal information concerning the plant model and the existing controller. In this thesis the formulation is extended to the multi-input multi-output case for distributed control of interconnected systems and successfully tested on a formation flight wind tunnel experiment. The command hedging method is formulated for the approach to further broaden the class of systems it can address by including systems with input nonlinearities. Also a formulation is adopted that allows the approach to be applied to non-minimum phase systems for which non-minimum phase characteristics are modeled with sufficient accuracy and treated properly in the design of the existing controller. It is shown that the approach can also be applied to augment nonlinear controllers under certain conditions and an example is presented where the nonlinear guidance law of a spinning projectile is augmented. Simulation results on a high fidelity 6 degrees-of-freedom nonlinear simulation code are presented. The thesis also presents a preliminary adaptive controller design for closed loop flight control with active flow actuators. Behavior of such actuators in dynamic flight conditions is not known. To test the adaptive controller design in simulation, a fictitious actuator model is developed that fits experimentally observed characteristics of flow control actuators in static flight conditions as well as possible coupling effects between actuation, the dynamics of flow field, and the rigid body dynamics of the vehicle.

Implementing Nonlinear Feedback Controllers Using DNA Strand Displacement Reactions.

PubMed

Sawlekar, Rucha; Montefusco, Francesco; Kulkarni, Vishwesh V; Bates, Declan G

2016-07-01

We show how an important class of nonlinear feedback controllers can be designed using idealized abstract chemical reactions and implemented via DNA strand displacement (DSD) reactions. Exploiting chemical reaction networks (CRNs) as a programming language for the design of complex circuits and networks, we show how a set of unimolecular and bimolecular reactions can be used to realize input-output dynamics that produce a nonlinear quasi sliding mode (QSM) feedback controller. The kinetics of the required chemical reactions can then be implemented as enzyme-free, enthalpy/entropy driven DNA reactions using a toehold mediated strand displacement mechanism via Watson-Crick base pairing and branch migration. We demonstrate that the closed loop response of the nonlinear QSM controller outperforms a traditional linear controller by facilitating much faster tracking response dynamics without introducing overshoots in the transient response. The resulting controller is highly modular and is less affected by retroactivity effects than standard linear designs.

Large predispersion for reduction of intrachannel nonlinear impairments in strongly dispersion-managed transmissions

NASA Astrophysics Data System (ADS)

Cao, Wenhua

2016-05-01

Predispersion for reduction of intrachannel nonlinear impairments in quasi-linear strongly dispersion-managed transmission system is analyzed in detail by numerical simulations. We show that for moderate amount of predispersion there is an optimal value at which reduction of the nonlinear impairments can be obtained, which is consistent with previous well-known predictions. However, we found that much better transmission performance than that of the previous predictions can be obtained if predispersion is increased to some extent. For large predispersion, the nonlinear impairments reduce monotonically with increasing predispersion and then they tend to be stabilized when predispersion is further increased. Thus, transmission performance can be efficiently improved by inserting a high-dispersive element, such as a chirped fiber bragg grating (CFBG), at the input end of the transmission link to broaden the signal pulses while, at the output end, using another CFBG with the opposite dispersion to recompress the signal.

Neural network L1 adaptive control of MIMO systems with nonlinear uncertainty.

PubMed

Zhen, Hong-tao; Qi, Xiao-hui; Li, Jie; Tian, Qing-min

2014-01-01

An indirect adaptive controller is developed for a class of multiple-input multiple-output (MIMO) nonlinear systems with unknown uncertainties. This control system is comprised of an L 1 adaptive controller and an auxiliary neural network (NN) compensation controller. The L 1 adaptive controller has guaranteed transient response in addition to stable tracking. In this architecture, a low-pass filter is adopted to guarantee fast adaptive rate without generating high-frequency oscillations in control signals. The auxiliary compensation controller is designed to approximate the unknown nonlinear functions by MIMO RBF neural networks to suppress the influence of uncertainties. NN weights are tuned on-line with no prior training and the project operator ensures the weights bounded. The global stability of the closed-system is derived based on the Lyapunov function. Numerical simulations of an MIMO system coupled with nonlinear uncertainties are used to illustrate the practical potential of our theoretical results.

Linear stable unity-feedback system - Necessary and sufficient conditions for stability under nonlinear plant perturbations

NASA Technical Reports Server (NTRS)

Desoer, C. A.; Kabuli, M. G.

1989-01-01

The authors consider a linear (not necessarily time-invariant) stable unity-feedback system, where the plant and the compensator have normalized right-coprime factorizations. They study two cases of nonlinear plant perturbations (additive and feedback), with four subcases resulting from: (1) allowing exogenous input to Delta P or not; 2) allowing the observation of the output of Delta P or not. The plant perturbation Delta P is not required to be stable. Using the factorization approach, the authors obtain necessary and sufficient conditions for all cases in terms of two pairs of nonlinear pseudostate maps. Simple physical considerations explain the form of these necessary and sufficient conditions. Finally, the authors obtain the characterization of all perturbations Delta P for which the perturbed system remains stable.

Nonlinear forcing in the resolvent analysis of wall-turbulence

NASA Astrophysics Data System (ADS)

Rosenberg, Kevin; Lozano Duran, Adrian; Towne, Aaron; McKeon, Beverley

2016-11-01

The resolvent analysis of McKeon and Sharma formulates the Navier-Stokes equations as an input/output system in which the nonlinearity is treated as a forcing that acts upon the linear dynamics to yield a velocity response across wavenumber/frequency space. DNS data for a low Reynolds number turbulent channel (Reτ = 180) is used to investigate the structure of the nonlinear forcing directly. Specifically, we explore the spatio-temporal scales where the forcing is active and analyze its interplay with the linear amplification mechanisms present in the resolvent operator. This work could provide insight into self-sustaining processes in wall-turbulence and inform the modeling of scale interactions in large eddy simulations. We gratefully acknowledge Stanford's Center for Turbulence Research for support of this work.

Tuning the group delay of optical wave packets in liquid-crystal light valves

NASA Astrophysics Data System (ADS)

Bortolozzo, U.; Residori, S.; Huignard, J. P.

2009-05-01

By performing two-wave mixing experiments in a liquid-crystal light valve, optical pulses are slowed down to group velocities as slow as a few tenths of mm/s, corresponding to a very large group index. We present experiments and model of the slow-light process occurring in the liquid-crystal light valve, showing that this is characterized by multiple-beam diffraction in the Raman-Nath regime. Depending on the initial frequency detuning between pump and signal, the different output order beams are distinguished by different group delays. The group delay can be tuned by changing the main parameters of the experiment: the detuning between the pump and the input wave packet, the strength of the nonlinearity, and the intensity of the pump beam.

A new approach to modeling temperature-related mortality: Non-linear autoregressive models with exogenous input.

PubMed

Lee, Cameron C; Sheridan, Scott C

2018-07-01

Temperature-mortality relationships are nonlinear, time-lagged, and can vary depending on the time of year and geographic location, all of which limits the applicability of simple regression models in describing these associations. This research demonstrates the utility of an alternative method for modeling such complex relationships that has gained recent traction in other environmental fields: nonlinear autoregressive models with exogenous input (NARX models). All-cause mortality data and multiple temperature-based data sets were gathered from 41 different US cities, for the period 1975-2010, and subjected to ensemble NARX modeling. Models generally performed better in larger cities and during the winter season. Across the US, median absolute percentage errors were 10% (ranging from 4% to 15% in various cities), the average improvement in the r-squared over that of a simple persistence model was 17% (6-24%), and the hit rate for modeling spike days in mortality (>80th percentile) was 54% (34-71%). Mortality responded acutely to hot summer days, peaking at 0-2 days of lag before dropping precipitously, and there was an extended mortality response to cold winter days, peaking at 2-4 days of lag and dropping slowly and continuing for multiple weeks. Spring and autumn showed both of the aforementioned temperature-mortality relationships, but generally to a lesser magnitude than what was seen in summer or winter. When compared to distributed lag nonlinear models, NARX model output was nearly identical. These results highlight the applicability of NARX models for use in modeling complex and time-dependent relationships for various applications in epidemiology and environmental sciences. Copyright © 2018 Elsevier Inc. All rights reserved.

Latent effects decision analysis

DOEpatents

Cooper, J Arlin [Albuquerque, NM; Werner, Paul W [Albuquerque, NM

2004-08-24

Latent effects on a system are broken down into components ranging from those far removed in time from the system under study (latent) to those which closely effect changes in the system. Each component is provided with weighted inputs either by a user or from outputs of other components. A non-linear mathematical process known as `soft aggregation` is performed on the inputs to each component to provide information relating to the component. This information is combined in decreasing order of latency to the system to provide a quantifiable measure of an attribute of a system (e.g., safety) or to test hypotheses (e.g., for forensic deduction or decisions about various system design options).

Ultralong relaxation times in bistable hybrid quantum systems.

PubMed

Angerer, Andreas; Putz, Stefan; Krimer, Dmitry O; Astner, Thomas; Zens, Matthias; Glattauer, Ralph; Streltsov, Kirill; Munro, William J; Nemoto, Kae; Rotter, Stefan; Schmiedmayer, Jörg; Majer, Johannes

2017-12-01

Nonlinear systems, whose outputs are not directly proportional to their inputs, are well known to exhibit many interesting and important phenomena that have profoundly changed our technological landscape over the last 50 years. Recently, the ability to engineer quantum metamaterials through hybridization has allowed us to explore these nonlinear effects in systems with no natural analog. We investigate amplitude bistability, which is one of the most fundamental nonlinear phenomena, in a hybrid system composed of a superconducting resonator inductively coupled to an ensemble of nitrogen-vacancy centers. One of the exciting properties of this spin system is its long spin lifetime, which is many orders of magnitude longer than other relevant time scales of the hybrid system. This allows us to dynamically explore this nonlinear regime of cavity quantum electrodynamics and demonstrate a critical slowing down of the cavity population on the order of several tens of thousands of seconds-a time scale much longer than observed so far for this effect. Our results provide a foundation for future quantum technologies based on nonlinear phenomena.

A 155-dB Dynamic Range Current Measurement Front End for Electrochemical Biosensing.

PubMed

Dai, Shanshan; Perera, Rukshan T; Yang, Zi; Rosenstein, Jacob K

2016-10-01

An integrated current measurement system with ultra wide dynamic range is presented and fabricated in a 180-nm CMOS technology. Its dual-mode design provides concurrent voltage and frequency outputs, without requiring an external clock source. An integrator-differentiator core provides a voltage output with a noise floor of 11.6 fA/ [Formula: see text] and a -3 dB cutoff frequency of 1.4 MHz. It is merged with an asynchronous current-to-frequency converter, which generates an output frequency linearly proportional to the input current. Together, the voltage and frequency outputs yield a current measurement range of 155 dB, spanning from 204 fA (100 Hz) or 1.25 pA (10 kHz) to 11.6 μA. The proposed architecture's low noise, wide bandwidth, and wide dynamic range make it ideal for measurements of highly nonlinear electrochemical and electrophysiological systems.

Viral-genetic tracing of the input-output organization of a central noradrenaline circuit.

PubMed

Schwarz, Lindsay A; Miyamichi, Kazunari; Gao, Xiaojing J; Beier, Kevin T; Weissbourd, Brandon; DeLoach, Katherine E; Ren, Jing; Ibanes, Sandy; Malenka, Robert C; Kremer, Eric J; Luo, Liqun

2015-08-06

Deciphering how neural circuits are anatomically organized with regard to input and output is instrumental in understanding how the brain processes information. For example, locus coeruleus noradrenaline (also known as norepinephrine) (LC-NE) neurons receive input from and send output to broad regions of the brain and spinal cord, and regulate diverse functions including arousal, attention, mood and sensory gating. However, it is unclear how LC-NE neurons divide up their brain-wide projection patterns and whether different LC-NE neurons receive differential input. Here we developed a set of viral-genetic tools to quantitatively analyse the input-output relationship of neural circuits, and applied these tools to dissect the LC-NE circuit in mice. Rabies-virus-based input mapping indicated that LC-NE neurons receive convergent synaptic input from many regions previously identified as sending axons to the locus coeruleus, as well as from newly identified presynaptic partners, including cerebellar Purkinje cells. The 'tracing the relationship between input and output' method (or TRIO method) enables trans-synaptic input tracing from specific subsets of neurons based on their projection and cell type. We found that LC-NE neurons projecting to diverse output regions receive mostly similar input. Projection-based viral labelling revealed that LC-NE neurons projecting to one output region also project to all brain regions we examined. Thus, the LC-NE circuit overall integrates information from, and broadcasts to, many brain regions, consistent with its primary role in regulating brain states. At the same time, we uncovered several levels of specificity in certain LC-NE sub-circuits. These tools for mapping output architecture and input-output relationship are applicable to other neuronal circuits and organisms. More broadly, our viral-genetic approaches provide an efficient intersectional means to target neuronal populations based on cell type and projection pattern.

Trajectory following and stabilization control of fully actuated AUV using inverse kinematics and self-tuning fuzzy PID.

PubMed

Hammad, Mohanad M; Elshenawy, Ahmed K; El Singaby, M I

2017-01-01

In this work a design for self-tuning non-linear Fuzzy Proportional Integral Derivative (FPID) controller is presented to control position and speed of Multiple Input Multiple Output (MIMO) fully-actuated Autonomous Underwater Vehicles (AUV) to follow desired trajectories. Non-linearity that results from the hydrodynamics and the coupled AUV dynamics makes the design of a stable controller a very difficult task. In this study, the control scheme in a simulation environment is validated using dynamic and kinematic equations for the AUV model and hydrodynamic damping equations. An AUV configuration with eight thrusters and an inverse kinematic model from a previous work is utilized in the simulation. In the proposed controller, Mamdani fuzzy rules are used to tune the parameters of the PID. Nonlinear fuzzy Gaussian membership functions are selected to give better performance and response in the non-linear system. A control architecture with two feedback loops is designed such that the inner loop is for velocity control and outer loop is for position control. Several test scenarios are executed to validate the controller performance including different complex trajectories with and without injection of ocean current disturbances. A comparison between the proposed FPID controller and the conventional PID controller is studied and shows that the FPID controller has a faster response to the reference signal and more stable behavior in a disturbed non-linear environment.

Trajectory following and stabilization control of fully actuated AUV using inverse kinematics and self-tuning fuzzy PID

PubMed Central

Elshenawy, Ahmed K.; El Singaby, M.I.

2017-01-01

In this work a design for self-tuning non-linear Fuzzy Proportional Integral Derivative (FPID) controller is presented to control position and speed of Multiple Input Multiple Output (MIMO) fully-actuated Autonomous Underwater Vehicles (AUV) to follow desired trajectories. Non-linearity that results from the hydrodynamics and the coupled AUV dynamics makes the design of a stable controller a very difficult task. In this study, the control scheme in a simulation environment is validated using dynamic and kinematic equations for the AUV model and hydrodynamic damping equations. An AUV configuration with eight thrusters and an inverse kinematic model from a previous work is utilized in the simulation. In the proposed controller, Mamdani fuzzy rules are used to tune the parameters of the PID. Nonlinear fuzzy Gaussian membership functions are selected to give better performance and response in the non-linear system. A control architecture with two feedback loops is designed such that the inner loop is for velocity control and outer loop is for position control. Several test scenarios are executed to validate the controller performance including different complex trajectories with and without injection of ocean current disturbances. A comparison between the proposed FPID controller and the conventional PID controller is studied and shows that the FPID controller has a faster response to the reference signal and more stable behavior in a disturbed non-linear environment. PMID:28683071

Thermally induced all-optical inverter and dynamic hysteresis loops in graphene oxide dispersions.

PubMed

Melle, Sonia; Calderón, Oscar G; Egatz-Gómez, Ana; Cabrera-Granado, E; Carreño, F; Antón, M A

2015-11-01

We experimentally study the temporal dynamics of amplitude-modulated laser beams propagating through a water dispersion of graphene oxide sheets in a fiber-to-fiber U-bench. Nonlinear refraction induced in the sample by thermal effects leads to both phase reversing of the transmitted signals and dynamic hysteresis in the input-output power curves. A theoretical model including beam propagation and thermal lensing dynamics reproduces the experimental findings.

Systematic Biological Filter Design with a Desired I/O Filtering Response Based on Promoter-RBS Libraries.

PubMed

Hsu, Chih-Yuan; Pan, Zhen-Ming; Hu, Rei-Hsing; Chang, Chih-Chun; Cheng, Hsiao-Chun; Lin, Che; Chen, Bor-Sen

2015-01-01

In this study, robust biological filters with an external control to match a desired input/output (I/O) filtering response are engineered based on the well-characterized promoter-RBS libraries and a cascade gene circuit topology. In the field of synthetic biology, the biological filter system serves as a powerful detector or sensor to sense different molecular signals and produces a specific output response only if the concentration of the input molecular signal is higher or lower than a specified threshold. The proposed systematic design method of robust biological filters is summarized into three steps. Firstly, several well-characterized promoter-RBS libraries are established for biological filter design by identifying and collecting the quantitative and qualitative characteristics of their promoter-RBS components via nonlinear parameter estimation method. Then, the topology of synthetic biological filter is decomposed into three cascade gene regulatory modules, and an appropriate promoter-RBS library is selected for each module to achieve the desired I/O specification of a biological filter. Finally, based on the proposed systematic method, a robust externally tunable biological filter is engineered by searching the promoter-RBS component libraries and a control inducer concentration library to achieve the optimal reference match for the specified I/O filtering response.

An investigation of adaptive controllers for helicopter vibration and the development of a new dual controller

NASA Technical Reports Server (NTRS)

Mookerjee, P.; Molusis, J. A.; Bar-Shalom, Y.

1985-01-01

An investigation of the properties important for the design of stochastic adaptive controllers for the higher harmonic control of helicopter vibration is presented. Three different model types are considered for the transfer relationship between the helicopter higher harmonic control input and the vibration output: (1) nonlinear; (2) linear with slow time varying coefficients; and (3) linear with constant coefficients. The stochastic controller formulations and solutions are presented for a dual, cautious, and deterministic controller for both linear and nonlinear transfer models. Extensive simulations are performed with the various models and controllers. It is shown that the cautious adaptive controller can sometimes result in unacceptable vibration control. A new second order dual controller is developed which is shown to modify the cautious adaptive controller by adding numerator and denominator correction terms to the cautious control algorithm. The new dual controller is simulated on a simple single-control vibration example and is found to achieve excellent vibration reduction and significantly improves upon the cautious controller.

A comparison of methods of fitting several models to nutritional response data.

PubMed

Vedenov, D; Pesti, G M

2008-02-01

A variety of models have been proposed to fit nutritional input-output response data. The models are typically nonlinear; therefore, fitting the models usually requires sophisticated statistical software and training to use it. An alternative tool for fitting nutritional response models was developed by using widely available and easier-to-use Microsoft Excel software. The tool, implemented as an Excel workbook (NRM.xls), allows simultaneous fitting and side-by-side comparisons of several popular models. This study compared the results produced by the tool we developed and PROC NLIN of SAS. The models compared were the broken line (ascending linear and quadratic segments), saturation kinetics, 4-parameter logistics, sigmoidal, and exponential models. The NRM.xls workbook provided results nearly identical to those of PROC NLIN. Furthermore, the workbook successfully fit several models that failed to converge in PROC NLIN. Two data sets were used as examples to compare fits by the different models. The results suggest that no particular nonlinear model is necessarily best for all nutritional response data.

Tracking Control of a Magnetic Shape Memory Actuator Using an Inverse Preisach Model with Modified Fuzzy Sliding Mode Control.

PubMed

Lin, Jhih-Hong; Chiang, Mao-Hsiung

2016-08-25

Magnetic shape memory (MSM) alloys are a new class of smart materials with extraordinary strains up to 12% and frequencies in the range of 1 to 2 kHz. The MSM actuator is a potential device which can achieve high performance electromagnetic actuation by using the properties of MSM alloys. However, significant non-linear hysteresis behavior is a significant barrier to control the MSM actuator. In this paper, the Preisach model was used, by capturing experiments from different input signals and output responses, to model the hysteresis of MSM actuator, and the inverse Preisach model, as a feedforward control, provided compensational signals to the MSM actuator to linearize the hysteresis non-linearity. The control strategy for path tracking combined the hysteresis compensator and the modified fuzzy sliding mode control (MFSMC) which served as a path controller. Based on the experimental results, it was verified that a tracking error in the order of micrometers was achieved.

Tracking Control of a Magnetic Shape Memory Actuator Using an Inverse Preisach Model with Modified Fuzzy Sliding Mode Control

PubMed Central

Lin, Jhih-Hong; Chiang, Mao-Hsiung

2016-01-01

Magnetic shape memory (MSM) alloys are a new class of smart materials with extraordinary strains up to 12% and frequencies in the range of 1 to 2 kHz. The MSM actuator is a potential device which can achieve high performance electromagnetic actuation by using the properties of MSM alloys. However, significant non-linear hysteresis behavior is a significant barrier to control the MSM actuator. In this paper, the Preisach model was used, by capturing experiments from different input signals and output responses, to model the hysteresis of MSM actuator, and the inverse Preisach model, as a feedforward control, provided compensational signals to the MSM actuator to linearize the hysteresis non-linearity. The control strategy for path tracking combined the hysteresis compensator and the modified fuzzy sliding mode control (MFSMC) which served as a path controller. Based on the experimental results, it was verified that a tracking error in the order of micrometers was achieved. PMID:27571081

High-contrast controllable switching based on polystyrene nonlinear cavities in 2D hole-type photonic crystals

NASA Astrophysics Data System (ADS)

Paghousi, Roohollah; Fasihi, Kiazand

2018-05-01

We present a new high-contrast controllable switch, which is based on a polystyrene nonlinear cavity, and is implemented in a two dimensional (2D) hole-type photonic crystal (PC). We show that by applying a control signal, the input power can be transmitted to the output waveguide with a high contrast ratio. The operation of the proposed device is investigated through the use of coupled-mode theory (CMT) and finite-difference time-domain (FDTD) method. The contrast ratio of the proposed device varies between 18 and 23, which is higher than the corresponding value in the previous investigations. Based on the simulation results, with increasing the control power the range of operating power will be increased, while the contrast ratio will be decreased. It has been shown that in a modified structure, at the expense of the range of operating power and the contrast ratio, the control power can be decreased, considerably.

Core reactivity estimation in space reactors using recurrent dynamic networks

NASA Technical Reports Server (NTRS)

Parlos, Alexander G.; Tsai, Wei K.

1991-01-01

A recurrent multilayer perceptron network topology is used in the identification of nonlinear dynamic systems from only the input/output measurements. The identification is performed in the discrete time domain, with the learning algorithm being a modified form of the back propagation (BP) rule. The recurrent dynamic network (RDN) developed is applied for the total core reactivity prediction of a spacecraft reactor from only neutronic power level measurements. Results indicate that the RDN can reproduce the nonlinear response of the reactor while keeping the number of nodes roughly equal to the relative order of the system. As accuracy requirements are increased, the number of required nodes also increases, however, the order of the RDN necessary to obtain such results is still in the same order of magnitude as the order of the mathematical model of the system. It is believed that use of the recurrent MLP structure with a variety of different learning algorithms may prove useful in utilizing artificial neural networks for recognition, classification, and prediction of dynamic systems.

Adaptive Fuzzy Bounded Control for Consensus of Multiple Strict-Feedback Nonlinear Systems.

PubMed

Wang, Wei; Tong, Shaocheng

2018-02-01

This paper studies the adaptive fuzzy bounded control problem for leader-follower multiagent systems, where each follower is modeled by the uncertain nonlinear strict-feedback system. Combining the fuzzy approximation with the dynamic surface control, an adaptive fuzzy control scheme is developed to guarantee the output consensus of all agents under directed communication topologies. Different from the existing results, the bounds of the control inputs are known as a priori, and they can be determined by the feedback control gains. To realize smooth and fast learning, a predictor is introduced to estimate each error surface, and the corresponding predictor error is employed to learn the optimal fuzzy parameter vector. It is proved that the developed adaptive fuzzy control scheme guarantees the uniformly ultimate boundedness of the closed-loop systems, and the tracking error converges to a small neighborhood of the origin. The simulation results and comparisons are provided to show the validity of the control strategy presented in this paper.

Logic gate system with three outputs and three inputs based on switchable electrocatalysis of glucose by glucose oxidase entrapped in chitosan films.

PubMed

Liu, Shuang; Wang, Lei; Lian, Wenjing; Liu, Hongyun; Li, Chen-Zhong

2015-01-01

A logic-gate system with three outputs and three inputs was developed based on the bioelectrocatalysis of glucose by glucose oxidase (GOx) entrapped in chitosan films on the electrode surface by means of ferrocenedicarboxylic acid (Fc(COOH)2 ). Cyclic voltammetric (CV) signals of Fc(COOH)2 exhibited pH-triggered on/off behavior owing to electrostatic interactions between the film and the probe at different pH levels. The addition of glucose greatly increased the oxidation peak current (Ipa ) through the electrocatalytic reaction. pH and glucose were selected as two inputs. As a reversible inhibitor of GOx, Cu(2+) was chosen as the third input. The combination of three inputs led to Ipa with different values according to different mechanisms, which were defined as three outputs with two thresholds. The logic gate with three outputs by using one type of enzyme provided a novel model to build logic circuits based on biomacromolecules, which might be applied to the intelligent medical diagnostics as smart biosensors in the future. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

ULTRA-STABILIZED D. C. AMPLIFIER

DOEpatents

Hartwig, E.C.; Kuenning, R.W.; Acker, R.C.

1959-02-17

An improved circuit is described for stabilizing the drift and minimizing the noise and hum level of d-c amplifiers so that the output voltage will be zero when the input is zero. In its detailed aspects, the disclosed circuit incorporates a d-c amplifier having a signal input, a second input, and an output circuit coupled back to the first input of the amplifier through inverse feedback means. An electronically driven chopper having a pair of fixed contacts and a moveable contact alternately connects the two inputs of a difference amplifier to the signal input. The A. E. error signal produced in the difference amplifier is amplified, rectified, and applied to the second input of the amplifier as the d-c stabilizing voltage.

Differences in the Transmission of Sensory Input into Motor Output between Introverts and Extraverts: Behavioral and Psychophysiological Analyses

ERIC Educational Resources Information Center

Stahl, J.; Rammsayer, T.

2004-01-01

The present study was designed to investigate extraversion-related individual differences in the speed of transmission of sensory input into motor output. In a sample of 16 introverted and 16 extraverted female volunteers, event-related potentials, lateralized readiness potentials (LRPs), and electromyogram (EMG) were recorded as participants…

Input output scaling relations in Italian manufacturing firms

NASA Astrophysics Data System (ADS)

Bottazzi, Giulio; Grazzi, Marco; Secchi, Angelo

2005-09-01

Recent analyses on different database have proposed some regularities with respect to size and growth rates distribution of firms. In this work we explore some basic properties of the dynamics of productivity in Italian manufacturing firms. We investigate relations between different inputs and output examining the impact of productivity in shaping the pattern of corporates evolution.

Hydrologic response to multimodel climate output using a physically based model of groundwater/surface water interactions

NASA Astrophysics Data System (ADS)

Sulis, M.; Paniconi, C.; Marrocu, M.; Huard, D.; Chaumont, D.

2012-12-01

General circulation models (GCMs) are the primary instruments for obtaining projections of future global climate change. Outputs from GCMs, aided by dynamical and/or statistical downscaling techniques, have long been used to simulate changes in regional climate systems over wide spatiotemporal scales. Numerous studies have acknowledged the disagreements between the various GCMs and between the different downscaling methods designed to compensate for the mismatch between climate model output and the spatial scale at which hydrological models are applied. Very little is known, however, about the importance of these differences once they have been input or assimilated by a nonlinear hydrological model. This issue is investigated here at the catchment scale using a process-based model of integrated surface and subsurface hydrologic response driven by outputs from 12 members of a multimodel climate ensemble. The data set consists of daily values of precipitation and min/max temperatures obtained by combining four regional climate models and five GCMs. The regional scenarios were downscaled using a quantile scaling bias-correction technique. The hydrologic response was simulated for the 690 km2des Anglais catchment in southwestern Quebec, Canada. The results show that different hydrological components (river discharge, aquifer recharge, and soil moisture storage) respond differently to precipitation and temperature anomalies in the multimodel climate output, with greater variability for annual discharge compared to recharge and soil moisture storage. We also find that runoff generation and extreme event-driven peak hydrograph flows are highly sensitive to any uncertainty in climate data. Finally, the results show the significant impact of changing sequences of rainy days on groundwater recharge fluxes and the influence of longer dry spells in modifying soil moisture spatial variability.

Neural node network and model, and method of teaching same

DOEpatents

Parlos, A.G.; Atiya, A.F.; Fernandez, B.; Tsai, W.K.; Chong, K.T.

1995-12-26

The present invention is a fully connected feed forward network that includes at least one hidden layer. The hidden layer includes nodes in which the output of the node is fed back to that node as an input with a unit delay produced by a delay device occurring in the feedback path (local feedback). Each node within each layer also receives a delayed output (crosstalk) produced by a delay unit from all the other nodes within the same layer. The node performs a transfer function operation based on the inputs from the previous layer and the delayed outputs. The network can be implemented as analog or digital or within a general purpose processor. Two teaching methods can be used: (1) back propagation of weight calculation that includes the local feedback and the crosstalk or (2) more preferably a feed forward gradient decent which immediately follows the output computations and which also includes the local feedback and the crosstalk. Subsequent to the gradient propagation, the weights can be normalized, thereby preventing convergence to a local optimum. Education of the network can be incremental both on and off-line. An educated network is suitable for modeling and controlling dynamic nonlinear systems and time series systems and predicting the outputs as well as hidden states and parameters. The educated network can also be further educated during on-line processing. 21 figs.

Neural node network and model, and method of teaching same

DOEpatents

Parlos, Alexander G.; Atiya, Amir F.; Fernandez, Benito; Tsai, Wei K.; Chong, Kil T.

1995-01-01

The present invention is a fully connected feed forward network that includes at least one hidden layer 16. The hidden layer 16 includes nodes 20 in which the output of the node is fed back to that node as an input with a unit delay produced by a delay device 24 occurring in the feedback path 22 (local feedback). Each node within each layer also receives a delayed output (crosstalk) produced by a delay unit 36 from all the other nodes within the same layer 16. The node performs a transfer function operation based on the inputs from the previous layer and the delayed outputs. The network can be implemented as analog or digital or within a general purpose processor. Two teaching methods can be used: (1) back propagation of weight calculation that includes the local feedback and the crosstalk or (2) more preferably a feed forward gradient decent which immediately follows the output computations and which also includes the local feedback and the crosstalk. Subsequent to the gradient propagation, the weights can be normalized, thereby preventing convergence to a local optimum. Education of the network can be incremental both on and off-line. An educated network is suitable for modeling and controlling dynamic nonlinear systems and time series systems and predicting the outputs as well as hidden states and parameters. The educated network can also be further educated during on-line processing.

Arrangement for multiplexing and intensity splitting light beams for interface into fiber optic cables

DOEpatents

Johnson, Steve A.

1990-01-01

An arrangement especially suitable for use in a laser apparatus for converting a plurality of different input light beams, for example copper vapor laser beams, into a plurality of substantially identical light beams is disclosed herein. This arrangement utilizes an optical mixing bar which is preferably integrally formed as a single unit and which includes a main body for mixing light therein, a flat input surface on one end of the main body, and a multi-faceted output face on the opposite end of the main body. This arrangement also includes means for directing the plurality of different input light beams onto the input face of the mixing base, whereby to cause the different beams to mix within the main body of the mixing bar and exit the latter from its multi-faceted output face as the desired plurality of substantially identical output beams.

Energy harvesting with stacked dielectric elastomer transducers: Nonlinear theory, optimization, and linearized scaling law

NASA Astrophysics Data System (ADS)

Tutcuoglu, A.; Majidi, C.

2014-12-01

Using principles of damped harmonic oscillation with continuous media, we examine electrostatic energy harvesting with a "soft-matter" array of dielectric elastomer (DE) transducers. The array is composed of infinitely thin and deformable electrodes separated by layers of insulating elastomer. During vibration, it deforms longitudinally, resulting in a change in the capacitance and electrical enthalpy of the charged electrodes. Depending on the phase of electrostatic loading, the DE array can function as either an actuator that amplifies small vibrations or a generator that converts these external excitations into electrical power. Both cases are addressed with a comprehensive theory that accounts for the influence of viscoelasticity, dielectric breakdown, and electromechanical coupling induced by Maxwell stress. In the case of a linearized Kelvin-Voigt model of the dielectric, we obtain a closed-form estimate for the electrical power output and a scaling law for DE generator design. For the complete nonlinear model, we obtain the optimal electrostatic voltage input for maximum electrical power output.

Adaptive Neural Tracking Control for Switched High-Order Stochastic Nonlinear Systems.

PubMed

Zhao, Xudong; Wang, Xinyong; Zong, Guangdeng; Zheng, Xiaolong

2017-10-01

This paper deals with adaptive neural tracking control design for a class of switched high-order stochastic nonlinear systems with unknown uncertainties and arbitrary deterministic switching. The considered issues are: 1) completely unknown uncertainties; 2) stochastic disturbances; and 3) high-order nonstrict-feedback system structure. The considered mathematical models can represent many practical systems in the actual engineering. By adopting the approximation ability of neural networks, common stochastic Lyapunov function method together with adding an improved power integrator technique, an adaptive state feedback controller with multiple adaptive laws is systematically designed for the systems. Subsequently, a controller with only two adaptive laws is proposed to solve the problem of over parameterization. Under the designed controllers, all the signals in the closed-loop system are bounded-input bounded-output stable in probability, and the system output can almost surely track the target trajectory within a specified bounded error. Finally, simulation results are presented to show the effectiveness of the proposed approaches.

Dynamic adjustment in agricultural practices to economic incentives aiming to decrease fertilizer application.

PubMed

Sun, Shanxia; Delgado, Michael S; Sesmero, Juan P

2016-07-15

Input- and output-based economic policies designed to reduce water pollution from fertilizer runoff by adjusting management practices are theoretically justified and well-understood. Yet, in practice, adjustment in fertilizer application or land allocation may be sluggish. We provide practical guidance for policymakers regarding the relative magnitude and speed of adjustment of input- and output-based policies. Through a dynamic dual model of corn production that takes fertilizer as one of several production inputs, we measure the short- and long-term effects of policies that affect the relative prices of inputs and outputs through the short- and long-term price elasticities of fertilizer application, and also the total time required for different policies to affect fertilizer application through the adjustment rates of capital and land. These estimates allow us to compare input- and output-based policies based on their relative cost-effectiveness. Using data from Indiana and Illinois, we find that input-based policies are more cost-effective than their output-based counterparts in achieving a target reduction in fertilizer application. We show that input- and output-based policies yield adjustment in fertilizer application at the same speed, and that most of the adjustment takes place in the short-term. Copyright © 2016 Elsevier Ltd. All rights reserved.

Simplified Interval Observer Scheme: A New Approach for Fault Diagnosis in Instruments

PubMed Central

Martínez-Sibaja, Albino; Astorga-Zaragoza, Carlos M.; Alvarado-Lassman, Alejandro; Posada-Gómez, Rubén; Aguila-Rodríguez, Gerardo; Rodríguez-Jarquin, José P.; Adam-Medina, Manuel

2011-01-01

There are different schemes based on observers to detect and isolate faults in dynamic processes. In the case of fault diagnosis in instruments (FDI) there are different diagnosis schemes based on the number of observers: the Simplified Observer Scheme (SOS) only requires one observer, uses all the inputs and only one output, detecting faults in one detector; the Dedicated Observer Scheme (DOS), which again uses all the inputs and just one output, but this time there is a bank of observers capable of locating multiple faults in sensors, and the Generalized Observer Scheme (GOS) which involves a reduced bank of observers, where each observer uses all the inputs and m-1 outputs, and allows the localization of unique faults. This work proposes a new scheme named Simplified Interval Observer SIOS-FDI, which does not requires the measurement of any input and just with just one output allows the detection of unique faults in sensors and because it does not require any input, it simplifies in an important way the diagnosis of faults in processes in which it is difficult to measure all the inputs, as in the case of biologic reactors. PMID:22346593

Irrationality and Quasiperiodicity in Driven Nonlinear Systems

NASA Astrophysics Data System (ADS)

Cubero, David; Casado-Pascual, Jesús; Renzoni, Ferruccio

2014-05-01

We analyze the relationship between irrationality and quasiperiodicity in nonlinear driven systems. To that purpose, we consider a nonlinear system whose steady-state response is very sensitive to the periodic or quasiperiodic character of the input signal. In the infinite time limit, an input signal consisting of two incommensurate frequencies will be recognized by the system as quasiperiodic. We show that this is, in general, not true in the case of finite interaction times. An irrational ratio of the driving frequencies of the input signal is not sufficient for it to be recognized by the nonlinear system as quasiperiodic, resulting in observations which may differ by several orders of magnitude from the expected quasiperiodic behavior. Thus, the system response depends on the nature of the irrational ratio, as well as the observation time. We derive a condition for the input signal to be identified by the system as quasiperiodic. Such a condition also takes into account the sub-Fourier response of the nonlinear system.

Bayesian sensitivity analysis of bifurcating nonlinear models

NASA Astrophysics Data System (ADS)

Becker, W.; Worden, K.; Rowson, J.

2013-01-01

Sensitivity analysis allows one to investigate how changes in input parameters to a system affect the output. When computational expense is a concern, metamodels such as Gaussian processes can offer considerable computational savings over Monte Carlo methods, albeit at the expense of introducing a data modelling problem. In particular, Gaussian processes assume a smooth, non-bifurcating response surface. This work highlights a recent extension to Gaussian processes which uses a decision tree to partition the input space into homogeneous regions, and then fits separate Gaussian processes to each region. In this way, bifurcations can be modelled at region boundaries and different regions can have different covariance properties. To test this method, both the treed and standard methods were applied to the bifurcating response of a Duffing oscillator and a bifurcating FE model of a heart valve. It was found that the treed Gaussian process provides a practical way of performing uncertainty and sensitivity analysis on large, potentially-bifurcating models, which cannot be dealt with by using a single GP, although an open problem remains how to manage bifurcation boundaries that are not parallel to coordinate axes.

The Boeing plastic analysis capability for engines

NASA Technical Reports Server (NTRS)

Vos, R. G.

1976-01-01

The current BOPACE program is described as a nonlinear stress analysis program, which is based on a family of isoparametric finite elements. The theoretical, user, programmer, preprocessing aspects are discussed, and example problems are included. New features in the current program version include substructuring, an out-of-core Gauss wavefront equation solver, multipoint constraints, combined material and geometric nonlinearities, automatic calculation of inertia effects, provision for distributed as well as concentrated mechanical loads, follower forces, singular crack-tip elements, the SAIL automatic generation capability, and expanded user control over input quantity definition, output selection, and program execution. BOPACE is written in FORTRAN 4 and is currently available for both the IBM 360/370 and the UNIVAC 1108 machines.

Neural networks for tracking of unknown SISO discrete-time nonlinear dynamic systems.

PubMed

Aftab, Muhammad Saleheen; Shafiq, Muhammad

2015-11-01

This article presents a Lyapunov function based neural network tracking (LNT) strategy for single-input, single-output (SISO) discrete-time nonlinear dynamic systems. The proposed LNT architecture is composed of two feedforward neural networks operating as controller and estimator. A Lyapunov function based back propagation learning algorithm is used for online adjustment of the controller and estimator parameters. The controller and estimator error convergence and closed-loop system stability analysis is performed by Lyapunov stability theory. Moreover, two simulation examples and one real-time experiment are investigated as case studies. The achieved results successfully validate the controller performance. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Modified-hybrid optical neural network filter for multiple object recognition within cluttered scenes

NASA Astrophysics Data System (ADS)

Kypraios, Ioannis; Young, Rupert C. D.; Chatwin, Chris R.

2009-08-01

Motivated by the non-linear interpolation and generalization abilities of the hybrid optical neural network filter between the reference and non-reference images of the true-class object we designed the modifiedhybrid optical neural network filter. We applied an optical mask to the hybrid optical neural network's filter input. The mask was built with the constant weight connections of a randomly chosen image included in the training set. The resulted design of the modified-hybrid optical neural network filter is optimized for performing best in cluttered scenes of the true-class object. Due to the shift invariance properties inherited by its correlator unit the filter can accommodate multiple objects of the same class to be detected within an input cluttered image. Additionally, the architecture of the neural network unit of the general hybrid optical neural network filter allows the recognition of multiple objects of different classes within the input cluttered image by modifying the output layer of the unit. We test the modified-hybrid optical neural network filter for multiple objects of the same and of different classes' recognition within cluttered input images and video sequences of cluttered scenes. The filter is shown to exhibit with a single pass over the input data simultaneously out-of-plane rotation, shift invariance and good clutter tolerance. It is able to successfully detect and classify correctly the true-class objects within background clutter for which there has been no previous training.

Adaptive sensor-fault tolerant control for a class of multivariable uncertain nonlinear systems.

PubMed

Khebbache, Hicham; Tadjine, Mohamed; Labiod, Salim; Boulkroune, Abdesselem

2015-03-01

This paper deals with the active fault tolerant control (AFTC) problem for a class of multiple-input multiple-output (MIMO) uncertain nonlinear systems subject to sensor faults and external disturbances. The proposed AFTC method can tolerate three additive (bias, drift and loss of accuracy) and one multiplicative (loss of effectiveness) sensor faults. By employing backstepping technique, a novel adaptive backstepping-based AFTC scheme is developed using the fact that sensor faults and system uncertainties (including external disturbances and unexpected nonlinear functions caused by sensor faults) can be on-line estimated and compensated via robust adaptive schemes. The stability analysis of the closed-loop system is rigorously proven using a Lyapunov approach. The effectiveness of the proposed controller is illustrated by two simulation examples. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Neuromodulation impact on nonlinear firing behavior of a reduced model motoneuron with the active dendrite

PubMed Central

Kim, Hojeong; Heckman, C. J.

2014-01-01

Neuromodulatory inputs from brainstem systems modulate the normal function of spinal motoneurons by altering the activation properties of persistent inward currents (PICs) in their dendrites. However, the effect of the PIC on firing outputs also depends on its location in the dendritic tree. To investigate the interaction between PIC neuromodulation and PIC location dependence, we used a two-compartment model that was biologically realistic in that it retains directional and frequency-dependent electrical coupling between the soma and the dendrites, as seen in multi-compartment models based on full anatomical reconstructions of motoneurons. Our two-compartment approach allowed us to systematically vary the coupling parameters between the soma and the dendrite to accurately reproduce the effect of location of the dendritic PIC on the generation of nonlinear (hysteretic) motoneuron firing patterns. Our results show that as a single parameter value for PIC activation was either increased or decreased by 20% from its default value, the solution space of the coupling parameter values for nonlinear firing outputs was drastically reduced by approximately 80%. As a result, the model tended to fire only in a linear mode at the majority of dendritic PIC sites. The same results were obtained when all parameters for the PIC activation simultaneously changed only by approximately ±10%. Our results suggest the democratization effect of neuromodulation: the neuromodulation by the brainstem systems may play a role in switching the motoneurons with PICs at different dendritic locations to a similar mode of firing by reducing the effect of the dendritic location of PICs on the firing behavior. PMID:25309410

Removing Visual Bias in Filament Identification: A New Goodness-of-fit Measure

NASA Astrophysics Data System (ADS)

Green, C.-E.; Cunningham, M. R.; Dawson, J. R.; Jones, P. A.; Novak, G.; Fissel, L. M.

2017-05-01

Different combinations of input parameters to filament identification algorithms, such as disperse and filfinder, produce numerous different output skeletons. The skeletons are a one-pixel-wide representation of the filamentary structure in the original input image. However, these output skeletons may not necessarily be a good representation of that structure. Furthermore, a given skeleton may not be as good of a representation as another. Previously, there has been no mathematical “goodness-of-fit” measure to compare output skeletons to the input image. Thus far this has been assessed visually, introducing visual bias. We propose the application of the mean structural similarity index (MSSIM) as a mathematical goodness-of-fit measure. We describe the use of the MSSIM to find the output skeletons that are the most mathematically similar to the original input image (the optimum, or “best,” skeletons) for a given algorithm, and independently of the algorithm. This measure makes possible systematic parameter studies, aimed at finding the subset of input parameter values returning optimum skeletons. It can also be applied to the output of non-skeleton-based filament identification algorithms, such as the Hessian matrix method. The MSSIM removes the need to visually examine thousands of output skeletons, and eliminates the visual bias, subjectivity, and limited reproducibility inherent in that process, representing a major improvement upon existing techniques. Importantly, it also allows further automation in the post-processing of output skeletons, which is crucial in this era of “big data.”

State estimation of spatio-temporal phenomena

NASA Astrophysics Data System (ADS)

Yu, Dan

This dissertation addresses the state estimation problem of spatio-temporal phenomena which can be modeled by partial differential equations (PDEs), such as pollutant dispersion in the atmosphere. After discretizing the PDE, the dynamical system has a large number of degrees of freedom (DOF). State estimation using Kalman Filter (KF) is computationally intractable, and hence, a reduced order model (ROM) needs to be constructed first. Moreover, the nonlinear terms, external disturbances or unknown boundary conditions can be modeled as unknown inputs, which leads to an unknown input filtering problem. Furthermore, the performance of KF could be improved by placing sensors at feasible locations. Therefore, the sensor scheduling problem to place multiple mobile sensors is of interest. The first part of the dissertation focuses on model reduction for large scale systems with a large number of inputs/outputs. A commonly used model reduction algorithm, the balanced proper orthogonal decomposition (BPOD) algorithm, is not computationally tractable for large systems with a large number of inputs/outputs. Inspired by the BPOD and randomized algorithms, we propose a randomized proper orthogonal decomposition (RPOD) algorithm and a computationally optimal RPOD (RPOD*) algorithm, which construct an ROM to capture the input-output behaviour of the full order model, while reducing the computational cost of BPOD by orders of magnitude. It is demonstrated that the proposed RPOD* algorithm could construct the ROM in real-time, and the performance of the proposed algorithms on different advection-diffusion equations. Next, we consider the state estimation problem of linear discrete-time systems with unknown inputs which can be treated as a wide-sense stationary process with rational power spectral density, while no other prior information needs to be known. We propose an autoregressive (AR) model based unknown input realization technique which allows us to recover the input statistics from the output data by solving an appropriate least squares problem, then fit an AR model to the recovered input statistics and construct an innovations model of the unknown inputs using the eigensystem realization algorithm. The proposed algorithm outperforms the augmented two-stage Kalman Filter (ASKF) and the unbiased minimum-variance (UMV) algorithm are shown in several examples. Finally, we propose a framework to place multiple mobile sensors to optimize the long-term performance of KF in the estimation of the state of a PDE. The major challenges are that placing multiple sensors is an NP-hard problem, and the optimization problem is non-convex in general. In this dissertation, first, we construct an ROM using RPOD* algorithm, and then reduce the feasible sensor locations into a subset using the ROM. The Information Space Receding Horizon Control (I-RHC) approach and a modified Monte Carlo Tree Search (MCTS) approach are applied to solve the sensor scheduling problem using the subset. Various applications have been provided to demonstrate the performance of the proposed approach.

Improved methods in neural network-based adaptive output feedback control, with applications to flight control

NASA Astrophysics Data System (ADS)

Kim, Nakwan

Utilizing the universal approximation property of neural networks, we develop several novel approaches to neural network-based adaptive output feedback control of nonlinear systems, and illustrate these approaches for several flight control applications. In particular, we address the problem of non-affine systems and eliminate the fixed point assumption present in earlier work. All of the stability proofs are carried out in a form that eliminates an algebraic loop in the neural network implementation. An approximate input/output feedback linearizing controller is augmented with a neural network using input/output sequences of the uncertain system. These approaches permit adaptation to both parametric uncertainty and unmodeled dynamics. All physical systems also have control position and rate limits, which may either deteriorate performance or cause instability for a sufficiently high control bandwidth. Here we apply a method for protecting an adaptive process from the effects of input saturation and time delays, known as "pseudo control hedging". This method was originally developed for the state feedback case, and we provide a stability analysis that extends its domain of applicability to the case of output feedback. The approach is illustrated by the design of a pitch-attitude flight control system for a linearized model of an R-50 experimental helicopter, and by the design of a pitch-rate control system for a 58-state model of a flexible aircraft consisting of rigid body dynamics coupled with actuator and flexible modes. A new approach to augmentation of an existing linear controller is introduced. It is especially useful when there is limited information concerning the plant model, and the existing controller. The approach is applied to the design of an adaptive autopilot for a guided munition. Design of a neural network adaptive control that ensures asymptotically stable tracking performance is also addressed.

Computer simulation and design of a three degree-of-freedom shoulder module

NASA Technical Reports Server (NTRS)

Marco, David; Torfason, L.; Tesar, Delbert

1989-01-01

An in-depth kinematic analysis of a three degree of freedom fully-parallel robotic shoulder module is presented. The major goal of the analysis is to determine appropriate link dimensions which will provide a maximized workspace along with desirable input to output velocity and torque amplification. First order kinematic influence coefficients which describe the output velocity properties in terms of actuator motions provide a means to determine suitable geometric dimensions for the device. Through the use of computer simulation, optimal or near optimal link dimensions based on predetermined design criteria are provided for two different structural designs of the mechanism. The first uses three rotational inputs to control the output motion. The second design involves the use of four inputs, actuating any three inputs for a given position of the output link. Alternative actuator placements are examined to determine the most effective approach to control the output motion.

Design, Fabrication, and Modeling of a Novel Dual-Axis Control Input PZT Gyroscope.

PubMed

Chang, Cheng-Yang; Chen, Tsung-Lin

2017-10-31

Conventional gyroscopes are equipped with a single-axis control input, limiting their performance. Although researchers have proposed control algorithms with dual-axis control inputs to improve gyroscope performance, most have verified the control algorithms through numerical simulations because they lacked practical devices with dual-axis control inputs. The aim of this study was to design a piezoelectric gyroscope equipped with a dual-axis control input so that researchers may experimentally verify those control algorithms in future. Designing a piezoelectric gyroscope with a dual-axis control input is more difficult than designing a conventional gyroscope because the control input must be effective over a broad frequency range to compensate for imperfections, and the multiple mode shapes in flexural deformations complicate the relation between flexural deformation and the proof mass position. This study solved these problems by using a lead zirconate titanate (PZT) material, introducing additional electrodes for shielding, developing an optimal electrode pattern, and performing calibrations of undesired couplings. The results indicated that the fabricated device could be operated at 5.5±1 kHz to perform dual-axis actuations and position measurements. The calibration of the fabricated device was completed by system identifications of a new dynamic model including gyroscopic motions, electromechanical coupling, mechanical coupling, electrostatic coupling, and capacitive output impedance. Finally, without the assistance of control algorithms, the "open loop sensitivity" of the fabricated gyroscope was 1.82 μV/deg/s with a nonlinearity of 9.5% full-scale output. This sensitivity is comparable with those of other PZT gyroscopes with single-axis control inputs.

Predictive and Neural Predictive Control of Uncertain Systems

NASA Technical Reports Server (NTRS)

Kelkar, Atul G.

2000-01-01

Accomplishments and future work are:(1) Stability analysis: the work completed includes characterization of stability of receding horizon-based MPC in the setting of LQ paradigm. The current work-in-progress includes analyzing local as well as global stability of the closed-loop system under various nonlinearities; for example, actuator nonlinearities; sensor nonlinearities, and other plant nonlinearities. Actuator nonlinearities include three major types of nonlineaxities: saturation, dead-zone, and (0, 00) sector. (2) Robustness analysis: It is shown that receding horizon parameters such as input and output horizon lengths have direct effect on the robustness of the system. (3) Code development: A matlab code has been developed which can simulate various MPC formulations. The current effort is to generalize the code to include ability to handle all plant types and all MPC types. (4) Improved predictor: It is shown that MPC design using better predictors that can minimize prediction errors. It is shown analytically and numerically that Smith predictor can provide closed-loop stability under GPC operation for plants with dead times where standard optimal predictor fails. (5) Neural network predictors: When neural network is used as predictor it can be shown that neural network predicts the plant output within some finite error bound under certain conditions. Our preliminary study shows that with proper choice of update laws and network architectures such bound can be obtained. However, much work needs to be done to obtain a similar result in general case.

Neural network based adaptive control for nonlinear dynamic regimes

NASA Astrophysics Data System (ADS)

Shin, Yoonghyun

Adaptive control designs using neural networks (NNs) based on dynamic inversion are investigated for aerospace vehicles which are operated at highly nonlinear dynamic regimes. NNs play a key role as the principal element of adaptation to approximately cancel the effect of inversion error, which subsequently improves robustness to parametric uncertainty and unmodeled dynamics in nonlinear regimes. An adaptive control scheme previously named 'composite model reference adaptive control' is further developed so that it can be applied to multi-input multi-output output feedback dynamic inversion. It can have adaptive elements in both the dynamic compensator (linear controller) part and/or in the conventional adaptive controller part, also utilizing state estimation information for NN adaptation. This methodology has more flexibility and thus hopefully greater potential than conventional adaptive designs for adaptive flight control in highly nonlinear flight regimes. The stability of the control system is proved through Lyapunov theorems, and validated with simulations. The control designs in this thesis also include the use of 'pseudo-control hedging' techniques which are introduced to prevent the NNs from attempting to adapt to various actuation nonlinearities such as actuator position and rate saturations. Control allocation is introduced for the case of redundant control effectors including thrust vectoring nozzles. A thorough comparison study of conventional and NN-based adaptive designs for a system under a limit cycle, wing-rock, is included in this research, and the NN-based adaptive control designs demonstrate their performances for two highly maneuverable aerial vehicles, NASA F-15 ACTIVE and FQM-117B unmanned aerial vehicle (UAV), operated under various nonlinearities and uncertainties.

Sources of Individual Differences in L2 Narrative Production: The Contribution of Input, Processing, and Output Anxiety

ERIC Educational Resources Information Center

Trebits, Anna

2016-01-01

The aim of this study was to investigate the effects of cognitive task complexity and individual differences in input, processing, and output anxiety (IPOA) on L2 narrative production. The participants were enrolled in a bilingual secondary educational program. They performed two narrative tasks in speech and writing. The participants' level of…

Optical bistability for optical signal processing and computing

NASA Astrophysics Data System (ADS)

Peyghambarian, N.; Gibbs, H. M.

1985-02-01

Optical bistability (OB) is a phenomenon in which a nonlinear medium responds to an optical input beam by changing its transmission abruptly from one value to another. A 'nonlinear medium' is a medium in which the index of refraction depends on the incident light intensity. A device is said to be optically bistable if two stable output states exist for the same value of the input. Optically bistable devices can perform a number of logic functions related to optical memory, optical transistor, optical discriminator, optical limiter, optical oscillator, and optical gate. They also have the potential for subpicosecond switching, greatly exceeding the capability of electronics. This potential is one of several advantages of optical data processing over electronic processing. Other advantages are greater immunity to electromagnetic interference and crosstalk, and highly parallel processing capability. The present investigation is mainly concerned with all-optical etalon devices. The considered materials, include GaAs, ZnS and ZnSe, CuCl, InSb, InAs, and CdS.

Slow feature analysis: unsupervised learning of invariances.

PubMed

Wiskott, Laurenz; Sejnowski, Terrence J

2002-04-01

Invariant features of temporally varying signals are useful for analysis and classification. Slow feature analysis (SFA) is a new method for learning invariant or slowly varying features from a vectorial input signal. It is based on a nonlinear expansion of the input signal and application of principal component analysis to this expanded signal and its time derivative. It is guaranteed to find the optimal solution within a family of functions directly and can learn to extract a large number of decorrelated features, which are ordered by their degree of invariance. SFA can be applied hierarchically to process high-dimensional input signals and extract complex features. SFA is applied first to complex cell tuning properties based on simple cell output, including disparity and motion. Then more complicated input-output functions are learned by repeated application of SFA. Finally, a hierarchical network of SFA modules is presented as a simple model of the visual system. The same unstructured network can learn translation, size, rotation, contrast, or, to a lesser degree, illumination invariance for one-dimensional objects, depending on only the training stimulus. Surprisingly, only a few training objects suffice to achieve good generalization to new objects. The generated representation is suitable for object recognition. Performance degrades if the network is trained to learn multiple invariances simultaneously.

A family of asymptotically stable control laws for flexible robots based on a passivity approach

NASA Technical Reports Server (NTRS)

Lanari, Leonardo; Wen, John T.

1991-01-01

A general family of asymptotically stabilizing control laws is introduced for a class of nonlinear Hamiltonian systems. The inherent passivity property of this class of systems and the Passivity Theorem are used to show the closed-loop input/output stability which is then related to the internal state space stability through the stabilizability and detectability condition. Applications of these results include fully actuated robots, flexible joint robots, and robots with link flexibility.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hamlet, Jason R.; Mayo, Jackson R.

Embodiments of the invention describe a Boolean circuit having a voter circuit and a plurality of approximate circuits each based, at least in part, on a reference circuit. The approximate circuits are each to generate one or more output signals based on values of received input signals. The voter circuit is to receive the one or more output signals generated by each of the approximate circuits, and is to output one or more signals corresponding to a majority value of the received signals. At least some of the approximate circuits are to generate an output value different than the referencemore » circuit for one or more input signal values; however, for each possible input signal value, the majority values of the one or more output signals generated by the approximate circuits and received by the voter circuit correspond to output signal result values of the reference circuit.« less

A new open-loop fiber optic gyro error compensation method based on angular velocity error modeling.

PubMed

Zhang, Yanshun; Guo, Yajing; Li, Chunyu; Wang, Yixin; Wang, Zhanqing

2015-02-27

With the open-loop fiber optic gyro (OFOG) model, output voltage and angular velocity can effectively compensate OFOG errors. However, the model cannot reflect the characteristics of OFOG errors well when it comes to pretty large dynamic angular velocities. This paper puts forward a modeling scheme with OFOG output voltage u and temperature T as the input variables and angular velocity error Δω as the output variable. Firstly, the angular velocity error Δω is extracted from OFOG output signals, and then the output voltage u, temperature T and angular velocity error Δω are used as the learning samples to train a Radial-Basis-Function (RBF) neural network model. Then the nonlinear mapping model over T, u and Δω is established and thus Δω can be calculated automatically to compensate OFOG errors according to T and u. The results of the experiments show that the established model can be used to compensate the nonlinear OFOG errors. The maximum, the minimum and the mean square error of OFOG angular velocity are decreased by 97.0%, 97.1% and 96.5% relative to their initial values, respectively. Compared with the direct modeling of gyro angular velocity, which we researched before, the experimental results of the compensating method proposed in this paper are further reduced by 1.6%, 1.4% and 1.42%, respectively, so the performance of this method is better than that of the direct modeling for gyro angular velocity.

A New Open-Loop Fiber Optic Gyro Error Compensation Method Based on Angular Velocity Error Modeling

PubMed Central

Zhang, Yanshun; Guo, Yajing; Li, Chunyu; Wang, Yixin; Wang, Zhanqing

2015-01-01

With the open-loop fiber optic gyro (OFOG) model, output voltage and angular velocity can effectively compensate OFOG errors. However, the model cannot reflect the characteristics of OFOG errors well when it comes to pretty large dynamic angular velocities. This paper puts forward a modeling scheme with OFOG output voltage u and temperature T as the input variables and angular velocity error Δω as the output variable. Firstly, the angular velocity error Δω is extracted from OFOG output signals, and then the output voltage u, temperature T and angular velocity error Δω are used as the learning samples to train a Radial-Basis-Function (RBF) neural network model. Then the nonlinear mapping model over T, u and Δω is established and thus Δω can be calculated automatically to compensate OFOG errors according to T and u. The results of the experiments show that the established model can be used to compensate the nonlinear OFOG errors. The maximum, the minimum and the mean square error of OFOG angular velocity are decreased by 97.0%, 97.1% and 96.5% relative to their initial values, respectively. Compared with the direct modeling of gyro angular velocity, which we researched before, the experimental results of the compensating method proposed in this paper are further reduced by 1.6%, 1.4% and 1.2%, respectively, so the performance of this method is better than that of the direct modeling for gyro angular velocity. PMID:25734642

The potential of different artificial neural network (ANN) techniques in daily global solar radiation modeling based on meteorological data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Behrang, M.A.; Assareh, E.; Ghanbarzadeh, A.

2010-08-15

The main objective of present study is to predict daily global solar radiation (GSR) on a horizontal surface, based on meteorological variables, using different artificial neural network (ANN) techniques. Daily mean air temperature, relative humidity, sunshine hours, evaporation, and wind speed values between 2002 and 2006 for Dezful city in Iran (32 16'N, 48 25'E), are used in this study. In order to consider the effect of each meteorological variable on daily GSR prediction, six following combinations of input variables are considered: (I)Day of the year, daily mean air temperature and relative humidity as inputs and daily GSR as output.more » (II)Day of the year, daily mean air temperature and sunshine hours as inputs and daily GSR as output. (III)Day of the year, daily mean air temperature, relative humidity and sunshine hours as inputs and daily GSR as output. (IV)Day of the year, daily mean air temperature, relative humidity, sunshine hours and evaporation as inputs and daily GSR as output. (V)Day of the year, daily mean air temperature, relative humidity, sunshine hours and wind speed as inputs and daily GSR as output. (VI)Day of the year, daily mean air temperature, relative humidity, sunshine hours, evaporation and wind speed as inputs and daily GSR as output. Multi-layer perceptron (MLP) and radial basis function (RBF) neural networks are applied for daily GSR modeling based on six proposed combinations. The measured data between 2002 and 2005 are used to train the neural networks while the data for 214 days from 2006 are used as testing data. The comparison of obtained results from ANNs and different conventional GSR prediction (CGSRP) models shows very good improvements (i.e. the predicted values of best ANN model (MLP-V) has a mean absolute percentage error (MAPE) about 5.21% versus 10.02% for best CGSRP model (CGSRP 5)). (author)« less

Optimal reorientation of asymmetric underactuated spacecraft using differential flatness and receding horizon control

NASA Astrophysics Data System (ADS)

Cai, Wei-wei; Yang, Le-ping; Zhu, Yan-wei

2015-01-01

This paper presents a novel method integrating nominal trajectory optimization and tracking for the reorientation control of an underactuated spacecraft with only two available control torque inputs. By employing a pseudo input along the uncontrolled axis, the flatness property of a general underactuated spacecraft is extended explicitly, by which the reorientation trajectory optimization problem is formulated into the flat output space with all the differential constraints eliminated. Ultimately, the flat output optimization problem is transformed into a nonlinear programming problem via the Chebyshev pseudospectral method, which is improved by the conformal map and barycentric rational interpolation techniques to overcome the side effects of the differential matrix's ill-conditions on numerical accuracy. Treating the trajectory tracking control as a state regulation problem, we develop a robust closed-loop tracking control law using the receding-horizon control method, and compute the feedback control at each control cycle rapidly via the differential transformation method. Numerical simulation results show that the proposed control scheme is feasible and effective for the reorientation maneuver.

Compound hydraulic shear-modulated vortex amplifiers

NASA Technical Reports Server (NTRS)

Goldschmied, F. R.

1977-01-01

A novel two-stage shear-modulated hydraulic vortex amplifier (U.S. patent 3,520,317) has been fabricated and put through preliminary steady-state testing at the 1000 psi supply pressure level with flows up to 15 gpm. The invention comprises a conventional fluidic vortex power stage and a shear-modulated pilot stage. In the absence of any mechanical moving parts, water may be used as the hydraulic medium thus opening the way to many underseas applications. At blocked load, a control input from 0 to 150 psi was required to achieve an output from 0 to 900 psi; at wide-open load, a control input of 0 to 120 psi was needed to achieve an output from 0 to 15 gpm. The power stage has been found unsuitable for the proportional control mode because of its nonlinear performance in the intermediate load range and because of strong pressure fluctuations (plus or minus 150 psi) in the intermediate control range. The addition of the shear-modulated pilot stage improves intermediate load linearity.

Failure Bounding And Sensitivity Analysis Applied To Monte Carlo Entry, Descent, And Landing Simulations

NASA Technical Reports Server (NTRS)

Gaebler, John A.; Tolson, Robert H.

2010-01-01

In the study of entry, descent, and landing, Monte Carlo sampling methods are often employed to study the uncertainty in the designed trajectory. The large number of uncertain inputs and outputs, coupled with complicated non-linear models, can make interpretation of the results difficult. Three methods that provide statistical insights are applied to an entry, descent, and landing simulation. The advantages and disadvantages of each method are discussed in terms of the insights gained versus the computational cost. The first method investigated was failure domain bounding which aims to reduce the computational cost of assessing the failure probability. Next a variance-based sensitivity analysis was studied for the ability to identify which input variable uncertainty has the greatest impact on the uncertainty of an output. Finally, probabilistic sensitivity analysis is used to calculate certain sensitivities at a reduced computational cost. These methods produce valuable information that identifies critical mission parameters and needs for new technology, but generally at a significant computational cost.

Modeling and Simulation of Linear and Nonlinear MEMS Scale Electromagnetic Energy Harvesters for Random Vibration Environments

PubMed Central

Sassani, Farrokh

2014-01-01

The simulation results for electromagnetic energy harvesters (EMEHs) under broad band stationary Gaussian random excitations indicate the importance of both a high transformation factor and a high mechanical quality factor to achieve favourable mean power, mean square load voltage, and output spectral density. The optimum load is different for random vibrations and for sinusoidal vibration. Reducing the total damping ratio under band-limited random excitation yields a higher mean square load voltage. Reduced bandwidth resulting from decreased mechanical damping can be compensated by increasing the electrical damping (transformation factor) leading to a higher mean square load voltage and power. Nonlinear EMEHs with a Duffing spring and with linear plus cubic damping are modeled using the method of statistical linearization. These nonlinear EMEHs exhibit approximately linear behaviour under low levels of broadband stationary Gaussian random vibration; however, at higher levels of such excitation the central (resonant) frequency of the spectral density of the output voltage shifts due to the increased nonlinear stiffness and the bandwidth broadens slightly. Nonlinear EMEHs exhibit lower maximum output voltage and central frequency of the spectral density with nonlinear damping compared to linear damping. Stronger nonlinear damping yields broader bandwidths at stable resonant frequency. PMID:24605063

A Novel Estimator for the Rate of Information Transfer by Continuous Signals

PubMed Central

Takalo, Jouni; Ignatova, Irina; Weckström, Matti; Vähäsöyrinki, Mikko

2011-01-01

The information transfer rate provides an objective and rigorous way to quantify how much information is being transmitted through a communications channel whose input and output consist of time-varying signals. However, current estimators of information content in continuous signals are typically based on assumptions about the system's linearity and signal statistics, or they require prohibitive amounts of data. Here we present a novel information rate estimator without these limitations that is also optimized for computational efficiency. We validate the method with a simulated Gaussian information channel and demonstrate its performance with two example applications. Information transfer between the input and output signals of a nonlinear system is analyzed using a sensory receptor neuron as the model system. Then, a climate data set is analyzed to demonstrate that the method can be applied to a system based on two outputs generated by interrelated random processes. These analyses also demonstrate that the new method offers consistent performance in situations where classical methods fail. In addition to these examples, the method is applicable to a wide range of continuous time series commonly observed in the natural sciences, economics and engineering. PMID:21494562

Evolution of optimal Hill coefficients in nonlinear public goods games.

PubMed

Archetti, Marco; Scheuring, István

2016-10-07

In evolutionary game theory, the effect of public goods like diffusible molecules has been modelled using linear, concave, sigmoid and step functions. The observation that biological systems are often sigmoid input-output functions, as described by the Hill equation, suggests that a sigmoid function is more realistic. The Michaelis-Menten model of enzyme kinetics, however, predicts a concave function, and while mechanistic explanations of sigmoid kinetics exist, we lack an adaptive explanation: what is the evolutionary advantage of a sigmoid benefit function? We analyse public goods games in which the shape of the benefit function can evolve, in order to determine the optimal and evolutionarily stable Hill coefficients. We find that, while the dynamics depends on whether output is controlled at the level of the individual or the population, intermediate or high Hill coefficients often evolve, leading to sigmoid input-output functions that for some parameters are so steep to resemble a step function (an on-off switch). Our results suggest that, even when the shape of the benefit function is unknown, biological public goods should be modelled using a sigmoid or step function rather than a linear or concave function. Copyright © 2016 Elsevier Ltd. All rights reserved.

Noise Suppression and Surplus Synchrony by Coincidence Detection

PubMed Central

Schultze-Kraft, Matthias; Diesmann, Markus; Grün, Sonja; Helias, Moritz

2013-01-01

The functional significance of correlations between action potentials of neurons is still a matter of vivid debate. In particular, it is presently unclear how much synchrony is caused by afferent synchronized events and how much is intrinsic due to the connectivity structure of cortex. The available analytical approaches based on the diffusion approximation do not allow to model spike synchrony, preventing a thorough analysis. Here we theoretically investigate to what extent common synaptic afferents and synchronized inputs each contribute to correlated spiking on a fine temporal scale between pairs of neurons. We employ direct simulation and extend earlier analytical methods based on the diffusion approximation to pulse-coupling, allowing us to introduce precisely timed correlations in the spiking activity of the synaptic afferents. We investigate the transmission of correlated synaptic input currents by pairs of integrate-and-fire model neurons, so that the same input covariance can be realized by common inputs or by spiking synchrony. We identify two distinct regimes: In the limit of low correlation linear perturbation theory accurately determines the correlation transmission coefficient, which is typically smaller than unity, but increases sensitively even for weakly synchronous inputs. In the limit of high input correlation, in the presence of synchrony, a qualitatively new picture arises. As the non-linear neuronal response becomes dominant, the output correlation becomes higher than the total correlation in the input. This transmission coefficient larger unity is a direct consequence of non-linear neural processing in the presence of noise, elucidating how synchrony-coded signals benefit from these generic properties present in cortical networks. PMID:23592953

Aerodynamic preliminary analysis system 2. Part 1: Theory

NASA Technical Reports Server (NTRS)

Bonner, E.; Clever, W.; Dunn, K.

1981-01-01

A subsonic/supersonic/hypersonic aerodynamic analysis was developed by integrating the Aerodynamic Preliminary Analysis System (APAS), and the inviscid force calculation modules of the Hypersonic Arbitrary Body Program. APAS analysis was extended for nonlinear vortex forces using a generalization of the Polhamus analogy. The interactive system provides appropriate aerodynamic models for a single input geometry data base and has a run/output format similar to a wind tunnel test program. The user's manual was organized to cover the principle system activities of a typical application, geometric input/editing, aerodynamic evaluation, and post analysis review/display. Sample sessions are included to illustrate the specific task involved and are followed by a comprehensive command/subcommand dictionary used to operate the system.

String resistance detector

NASA Technical Reports Server (NTRS)

Hall, A. Daniel (Inventor); Davies, Francis J. (Inventor)

2007-01-01

Method and system are disclosed for determining individual string resistance in a network of strings when the current through a parallel connected string is unknown and when the voltage across a series connected string is unknown. The method/system of the invention involves connecting one or more frequency-varying impedance components with known electrical characteristics to each string and applying a frequency-varying input signal to the network of strings. The frequency-varying impedance components may be one or more capacitors, inductors, or both, and are selected so that each string is uniquely identifiable in the output signal resulting from the frequency-varying input signal. Numerical methods, such as non-linear regression, may then be used to resolve the resistance associated with each string.

An engineering optimization method with application to STOL-aircraft approach and landing trajectories

NASA Technical Reports Server (NTRS)

Jacob, H. G.

1972-01-01

An optimization method has been developed that computes the optimal open loop inputs for a dynamical system by observing only its output. The method reduces to static optimization by expressing the inputs as series of functions with parameters to be optimized. Since the method is not concerned with the details of the dynamical system to be optimized, it works for both linear and nonlinear systems. The method and the application to optimizing longitudinal landing paths for a STOL aircraft with an augmented wing are discussed. Noise, fuel, time, and path deviation minimizations are considered with and without angle of attack, acceleration excursion, flight path, endpoint, and other constraints.

Pandemic recovery analysis using the dynamic inoperability input-output model.

PubMed

Santos, Joost R; Orsi, Mark J; Bond, Erik J

2009-12-01

Economists have long conceptualized and modeled the inherent interdependent relationships among different sectors of the economy. This concept paved the way for input-output modeling, a methodology that accounts for sector interdependencies governing the magnitude and extent of ripple effects due to changes in the economic structure of a region or nation. Recent extensions to input-output modeling have enhanced the model's capabilities to account for the impact of an economic perturbation; two such examples are the inoperability input-output model((1,2)) and the dynamic inoperability input-output model (DIIM).((3)) These models introduced sector inoperability, or the inability to satisfy as-planned production levels, into input-output modeling. While these models provide insights for understanding the impacts of inoperability, there are several aspects of the current formulation that do not account for complexities associated with certain disasters, such as a pandemic. This article proposes further enhancements to the DIIM to account for economic productivity losses resulting primarily from workforce disruptions. A pandemic is a unique disaster because the majority of its direct impacts are workforce related. The article develops a modeling framework to account for workforce inoperability and recovery factors. The proposed workforce-explicit enhancements to the DIIM are demonstrated in a case study to simulate a pandemic scenario in the Commonwealth of Virginia.

All-optical universal logic gates on nonlinear multimode interference coupler using tunable input intensity

NASA Astrophysics Data System (ADS)

Tajaldini, Mehdi; Jafri, Mohd Zubir Mat

2015-04-01

The theory of Nonlinear Modal Propagation Analysis Method (NMPA) have shown significant features of nonlinear multimode interference (MMI) coupler with compact dimension and when launched near the threshold of nonlinearity. Moreover, NMPA have the potential to allow studying the nonlinear MMI based the modal interference to explorer the phenomenon that what happen due to the natural of multimode region. Proposal of all-optical switch based NMPA has approved its capability to achieving the all-optical gates. All-optical gates have attracted increasing attention due to their practical utility in all-optical signal processing networks and systems. Nonlinear multimode interference devices could apply as universal all-optical gates due to significant features that NMPA introduce them. In this Paper, we present a novel Ultra-compact MMI coupler based on NMPA method in low intensity compared to last reports either as a novel design method and potential application for optical NAND, NOR as universal gates on single structure for Boolean logic signal processing devices and optimize their application via studding the contrast ratio between ON and OFF as a function of output width. We have applied NMPA for several applications so that the miniaturization in low nonlinear intensities is their main purpose.

Dendritic nonlinearities are tuned for efficient spike-based computations in cortical circuits.

PubMed

Ujfalussy, Balázs B; Makara, Judit K; Branco, Tiago; Lengyel, Máté

2015-12-24

Cortical neurons integrate thousands of synaptic inputs in their dendrites in highly nonlinear ways. It is unknown how these dendritic nonlinearities in individual cells contribute to computations at the level of neural circuits. Here, we show that dendritic nonlinearities are critical for the efficient integration of synaptic inputs in circuits performing analog computations with spiking neurons. We developed a theory that formalizes how a neuron's dendritic nonlinearity that is optimal for integrating synaptic inputs depends on the statistics of its presynaptic activity patterns. Based on their in vivo preynaptic population statistics (firing rates, membrane potential fluctuations, and correlations due to ensemble dynamics), our theory accurately predicted the responses of two different types of cortical pyramidal cells to patterned stimulation by two-photon glutamate uncaging. These results reveal a new computational principle underlying dendritic integration in cortical neurons by suggesting a functional link between cellular and systems--level properties of cortical circuits.

Multiple-frequency acoustic wave devices for chemical sensing and materials characterization in both gas and liquid phase

DOEpatents

Martin, S.J.; Ricco, A.J.

1993-08-10

A chemical or intrinsic physical property sensor is described comprising: (a) a substrate; (b) an interaction region of said substrate where the presence of a chemical or physical stimulus causes a detectable change in the velocity and/or an attenuation of an acoustic wave traversing said region; and (c) a plurality of paired input and output interdigitated electrodes patterned on the surface of said substrate where each of said paired electrodes has a distinct periodicity, where each of said paired electrodes is comprised of an input and an output electrode; (d) an input signal generation means for transmitting an input signal having a distinct frequency to a specified input interdigitated electrode of said plurality so that each input electrode receives a unique input signal, whereby said electrode responds to said input signal by generating an acoustic wave of a specified frequency, thus, said plurality responds by generating a plurality of acoustic waves of different frequencies; (e) an output signal receiving means for determining an acoustic wave velocity and an amplitude of said acoustic waves at several frequencies after said waves transverses said interaction region and comparing these values to an input acoustic wave velocity and an input acoustic wave amplitude to produce values for perturbations in acoustic wave velocities and for acoustic wave attenuation as a function of frequency, where said output receiving means is individually coupled to each of said output interdigitated electrode; (f) a computer means for analyzing a data stream comprising information from said output receiving means and from said input signal generation means to differentiate a specified response due to a perturbation from a subsequent specified response due to a subsequent perturbation to determine the chemical or intrinsic physical properties desired.

Parameterized data-driven fuzzy model based optimal control of a semi-batch reactor.

PubMed

Kamesh, Reddi; Rani, K Yamuna

2016-09-01

A parameterized data-driven fuzzy (PDDF) model structure is proposed for semi-batch processes, and its application for optimal control is illustrated. The orthonormally parameterized input trajectories, initial states and process parameters are the inputs to the model, which predicts the output trajectories in terms of Fourier coefficients. Fuzzy rules are formulated based on the signs of a linear data-driven model, while the defuzzification step incorporates a linear regression model to shift the domain from input to output domain. The fuzzy model is employed to formulate an optimal control problem for single rate as well as multi-rate systems. Simulation study on a multivariable semi-batch reactor system reveals that the proposed PDDF modeling approach is capable of capturing the nonlinear and time-varying behavior inherent in the semi-batch system fairly accurately, and the results of operating trajectory optimization using the proposed model are found to be comparable to the results obtained using the exact first principles model, and are also found to be comparable to or better than parameterized data-driven artificial neural network model based optimization results. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Linear summation in the barn owl's brainstem underlies responses to interaural time differences.

PubMed

Kuokkanen, Paula T; Ashida, Go; Carr, Catherine E; Wagner, Hermann; Kempter, Richard

2013-07-01

The neurophonic potential is a synchronized frequency-following extracellular field potential that can be recorded in the nucleus laminaris (NL) in the brainstem of the barn owl. Putative generators of the neurophonic are the afferent axons from the nucleus magnocellularis, synapses onto NL neurons, and spikes of NL neurons. The outputs of NL, i.e., action potentials of NL neurons, are only weakly represented in the neurophonic. Instead, the inputs to NL, i.e., afferent axons and their synaptic potentials, are the predominant origin of the neurophonic (Kuokkanen PT, Wagner H, Ashida G, Carr CE, Kempter R. J Neurophysiol 104: 2274-2290, 2010). Thus in NL the monaural inputs from the two brain sides converge and create a binaural neurophonic. If these monaural inputs contribute independently to the extracellular field, the response to binaural stimulation can be predicted from the sum of the responses to ipsi- and contralateral stimulation. We found that a linear summation model explains the dependence of the responses on interaural time difference as measured experimentally with binaural stimulation. The fit between model predictions and data was excellent, even without taking into account the nonlinear responses of NL coincidence detector neurons, although their firing rate and synchrony strongly depend on the interaural time difference. These results are consistent with the view that the afferent axons and their synaptic potentials in NL are the primary origin of the neurophonic.

Effect of non-linearity in predicting doppler waveforms through a novel model

PubMed Central

Gayasen, Aman; Dua, Sunil Kumar; Sengupta, Amit; Nagchoudhuri, D

2003-01-01

Background In pregnancy, the uteroplacental vascular system develops de novo locally in utero and a systemic haemodynamic & bio-rheological alteration accompany it. Any abnormality in the non-linear vascular system is believed to trigger the onset of serious morbid conditions like pre-eclampsia and/or intrauterine growth restriction (IUGR). Exact Aetiopathogenesis is unknown. Advancement in the field of non-invasive doppler image analysis and simulation incorporating non-linearities may unfold the complexities associated with the inaccessible uteroplacental vessels. Earlier modeling approaches approximate it as a linear system. Method We proposed a novel electrical model for the uteroplacental system that uses MOSFETs as non-linear elements in place of traditional linear transmission line (TL) model. The model to simulate doppler FVW's was designed by including the inputs from our non-linear mathematical model. While using the MOSFETs as voltage-controlled switches, a fair degree of controlled-non-linearity has been introduced in the model. Comparative analysis was done between the simulated data and the actual doppler FVW's waveforms. Results & Discussion Normal pregnancy has been successfully modeled and the doppler output waveforms are simulated for different gestation time using the model. It is observed that the dicrotic notch disappears and the S/D ratio decreases as the pregnancy matures. Both these results are established clinical facts. Effects of blood density, viscosity and the arterial wall elasticity on the blood flow velocity profile were also studied. Spectral analysis on the output of the model (blood flow velocity) indicated that the Total Harmonic Distortion (THD) falls during the mid-gestation. Conclusion Total harmonic distortion (THD) is found to be informative in determining the Feto-maternal health. Effects of the blood density, the viscosity and the elasticity changes on the blood FVW are simulated. Future works are expected to concentrate mainly on improving the load with respect to varying non-linear parameters in the model. Heart rate variability, which accounts for the vascular tone, should also be included. We also expect the model to initiate extensive clinical or experimental studies in the near future. PMID:14561227

Input/output properties of the lateral vestibular nucleus

NASA Technical Reports Server (NTRS)

Boyle, R.; Bush, G.; Ehsanian, R.

2004-01-01

This article is a review of work in three species, squirrel monkey, cat, and rat studying the inputs and outputs from the lateral vestibular nucleus (LVN). Different electrophysiological shock paradigms were used to determine the synaptic inputs derived from thick to thin diameter vestibular nerve afferents. Angular and linear mechanical stimulations were used to activate and study the combined and individual contribution of inner ear organs and neck afferents. The spatio-temporal properties of LVN neurons in the decerebrated rat were studied in response to dynamic acceleration inputs using sinusoidal linear translation in the horizontal head plane. Outputs were evaluated using antidromic identification techniques and identified LVN neurons were intracellularly injected with biocytin and their morphology studied.

Linear and nonlinear ARMA model parameter estimation using an artificial neural network

NASA Technical Reports Server (NTRS)

Chon, K. H.; Cohen, R. J.

1997-01-01

This paper addresses parametric system identification of linear and nonlinear dynamic systems by analysis of the input and output signals. Specifically, we investigate the relationship between estimation of the system using a feedforward neural network model and estimation of the system by use of linear and nonlinear autoregressive moving-average (ARMA) models. By utilizing a neural network model incorporating a polynomial activation function, we show the equivalence of the artificial neural network to the linear and nonlinear ARMA models. We compare the parameterization of the estimated system using the neural network and ARMA approaches by utilizing data generated by means of computer simulations. Specifically, we show that the parameters of a simulated ARMA system can be obtained from the neural network analysis of the simulated data or by conventional least squares ARMA analysis. The feasibility of applying neural networks with polynomial activation functions to the analysis of experimental data is explored by application to measurements of heart rate (HR) and instantaneous lung volume (ILV) fluctuations.

Nonlinear channel equalization for QAM signal constellation using artificial neural networks.

PubMed

Patra, J C; Pal, R N; Baliarsingh, R; Panda, G

1999-01-01

Application of artificial neural networks (ANN's) to adaptive channel equalization in a digital communication system with 4-QAM signal constellation is reported in this paper. A novel computationally efficient single layer functional link ANN (FLANN) is proposed for this purpose. This network has a simple structure in which the nonlinearity is introduced by functional expansion of the input pattern by trigonometric polynomials. Because of input pattern enhancement, the FLANN is capable of forming arbitrarily nonlinear decision boundaries and can perform complex pattern classification tasks. Considering channel equalization as a nonlinear classification problem, the FLANN has been utilized for nonlinear channel equalization. The performance of the FLANN is compared with two other ANN structures [a multilayer perceptron (MLP) and a polynomial perceptron network (PPN)] along with a conventional linear LMS-based equalizer for different linear and nonlinear channel models. The effect of eigenvalue ratio (EVR) of input correlation matrix on the equalizer performance has been studied. The comparison of computational complexity involved for the three ANN structures is also provided.

Fold-change detection and scalar symmetry of sensory input fields.

PubMed

Shoval, Oren; Goentoro, Lea; Hart, Yuval; Mayo, Avi; Sontag, Eduardo; Alon, Uri

2010-09-07

Recent studies suggest that certain cellular sensory systems display fold-change detection (FCD): a response whose entire shape, including amplitude and duration, depends only on fold changes in input and not on absolute levels. Thus, a step change in input from, for example, level 1 to 2 gives precisely the same dynamical output as a step from level 2 to 4, because the steps have the same fold change. We ask what the benefit of FCD is and show that FCD is necessary and sufficient for sensory search to be independent of multiplying the input field by a scalar. Thus, the FCD search pattern depends only on the spatial profile of the input and not on its amplitude. Such scalar symmetry occurs in a wide range of sensory inputs, such as source strength multiplying diffusing/convecting chemical fields sensed in chemotaxis, ambient light multiplying the contrast field in vision, and protein concentrations multiplying the output in cellular signaling systems. Furthermore, we show that FCD entails two features found across sensory systems, exact adaptation and Weber's law, but that these two features are not sufficient for FCD. Finally, we present a wide class of mechanisms that have FCD, including certain nonlinear feedback and feed-forward loops. We find that bacterial chemotaxis displays feedback within the present class and hence, is expected to show FCD. This can explain experiments in which chemotaxis searches are insensitive to attractant source levels. This study, thus, suggests a connection between properties of biological sensory systems and scalar symmetry stemming from physical properties of their input fields.

Dual physiological rate measurement instrument

NASA Technical Reports Server (NTRS)

Cooper, Tommy G. (Inventor)

1990-01-01

The object of the invention is to provide an instrument for converting a physiological pulse rate into a corresponding linear output voltage. The instrument which accurately measures the rate of an unknown rectangular pulse wave over an extended range of values comprises a phase-locked loop including a phase comparator, a filtering network, and a voltage-controlled oscillator, arranged in cascade. The phase comparator has a first input responsive to the pulse wave and a second input responsive to the output signal of the voltage-controlled oscillator. The comparator provides a signal dependent on the difference in phase and frequency between the signals appearing on the first and second inputs. A high-input impedance amplifier accepts an output from the filtering network and provides an amplified output DC signal to a utilization device for providing a measurement of the rate of the pulse wave.

NONLINEAR OPTICAL EFFECTS AND FIBER OPTICS: Modulation of radiation in a fiber Sagnac interferometer induced by an external field

NASA Astrophysics Data System (ADS)

Zakhidov, É. A.; Kasymdzhanov, M. A.; Mirtadzhiev, F. M.; Tartakovskiĭ, G. Kh; Khabibullaev, P. K.

1988-12-01

A study was made of the influence of the Kerr nonlinearity of a fiber waveguide on fluctuations of the output signal from a fiber-optic interferometer. The intensity fluctuations were modeled using the radiation from a pulsed high-power laser with a controlled intensity and pulse profile. Interferograms of the output radiation were obtained for different interferometer configurations. A comparison of the experiment and theory made it possible to explain the observed changes in the signal and to estimate the phase noise due to the Kerr nonlinearity in the investigated fiber waveguide.

Adaptive non-predictor control of lower triangular uncertain nonlinear systems with an unknown time-varying delay in the input

NASA Astrophysics Data System (ADS)

Koo, Min-Sung; Choi, Ho-Lim

2018-01-01

In this paper, we consider a control problem for a class of uncertain nonlinear systems in which there exists an unknown time-varying delay in the input and lower triangular nonlinearities. Usually, in the existing results, input delays have been coupled with feedforward (or upper triangular) nonlinearities; in other words, the combination of lower triangular nonlinearities and input delay has been rare. Motivated by the existing controller for input-delayed chain of integrators with nonlinearity, we show that the control of input-delayed nonlinear systems with two particular types of lower triangular nonlinearities can be done. As a control solution, we propose a newly designed feedback controller whose main features are its dynamic gain and non-predictor approach. Three examples are given for illustration.

Foldover effect and energy output from a nonlinear pseudo-maglev harvester

NASA Astrophysics Data System (ADS)

Kecik, Krzysztof; Mitura, Andrzej; Warminski, Jerzy; Lenci, Stefano

2018-01-01

Dynamics analysis and energy harvesting of a nonlinear magnetic pseudo-levitation (pseudo-maglev) harvester under harmonic excitation is presented in this paper. The system, for selected parameters, has two stable possible solutions with different corresponding energy outputs. The main goal is to analyse the influence of resistance load on the multi-stability zones and energy recovery which can help to tune the system to improve the energy harvesting efficiency.

Global sensitivity and uncertainty analysis of an atmospheric chemistry transport model: the FRAME model (version 9.15.0) as a case study

NASA Astrophysics Data System (ADS)

Aleksankina, Ksenia; Heal, Mathew R.; Dore, Anthony J.; Van Oijen, Marcel; Reis, Stefan

2018-04-01

Atmospheric chemistry transport models (ACTMs) are widely used to underpin policy decisions associated with the impact of potential changes in emissions on future pollutant concentrations and deposition. It is therefore essential to have a quantitative understanding of the uncertainty in model output arising from uncertainties in the input pollutant emissions. ACTMs incorporate complex and non-linear descriptions of chemical and physical processes which means that interactions and non-linearities in input-output relationships may not be revealed through the local one-at-a-time sensitivity analysis typically used. The aim of this work is to demonstrate a global sensitivity and uncertainty analysis approach for an ACTM, using as an example the FRAME model, which is extensively employed in the UK to generate source-receptor matrices for the UK Integrated Assessment Model and to estimate critical load exceedances. An optimised Latin hypercube sampling design was used to construct model runs within ±40 % variation range for the UK emissions of SO2, NOx, and NH3, from which regression coefficients for each input-output combination and each model grid ( > 10 000 across the UK) were calculated. Surface concentrations of SO2, NOx, and NH3 (and of deposition of S and N) were found to be predominantly sensitive to the emissions of the respective pollutant, while sensitivities of secondary species such as HNO3 and particulate SO42-, NO3-, and NH4+ to pollutant emissions were more complex and geographically variable. The uncertainties in model output variables were propagated from the uncertainty ranges reported by the UK National Atmospheric Emissions Inventory for the emissions of SO2, NOx, and NH3 (±4, ±10, and ±20 % respectively). The uncertainties in the surface concentrations of NH3 and NOx and the depositions of NHx and NOy were dominated by the uncertainties in emissions of NH3, and NOx respectively, whilst concentrations of SO2 and deposition of SOy were affected by the uncertainties in both SO2 and NH3 emissions. Likewise, the relative uncertainties in the modelled surface concentrations of each of the secondary pollutant variables (NH4+, NO3-, SO42-, and HNO3) were due to uncertainties in at least two input variables. In all cases the spatial distribution of relative uncertainty was found to be geographically heterogeneous. The global methods used here can be applied to conduct sensitivity and uncertainty analyses of other ACTMs.

Cell type-specific long-range connections of basal forebrain circuit.

PubMed

Do, Johnny Phong; Xu, Min; Lee, Seung-Hee; Chang, Wei-Cheng; Zhang, Siyu; Chung, Shinjae; Yung, Tyler J; Fan, Jiang Lan; Miyamichi, Kazunari; Luo, Liqun; Dan, Yang

2016-09-19

The basal forebrain (BF) plays key roles in multiple brain functions, including sleep-wake regulation, attention, and learning/memory, but the long-range connections mediating these functions remain poorly characterized. Here we performed whole-brain mapping of both inputs and outputs of four BF cell types - cholinergic, glutamatergic, and parvalbumin-positive (PV+) and somatostatin-positive (SOM+) GABAergic neurons - in the mouse brain. Using rabies virus -mediated monosynaptic retrograde tracing to label the inputs and adeno-associated virus to trace axonal projections, we identified numerous brain areas connected to the BF. The inputs to different cell types were qualitatively similar, but the output projections showed marked differences. The connections to glutamatergic and SOM+ neurons were strongly reciprocal, while those to cholinergic and PV+ neurons were more unidirectional. These results reveal the long-range wiring diagram of the BF circuit with highly convergent inputs and divergent outputs and point to both functional commonality and specialization of different BF cell types.

Wrapping Python around MODFLOW/MT3DMS based groundwater models

NASA Astrophysics Data System (ADS)

Post, V.

2008-12-01

Numerical models that simulate groundwater flow and solute transport require a great amount of input data that is often organized into different files. A large proportion of the input data consists of spatially-distributed model parameters. The model output consists of a variety data such as heads, fluxes and concentrations. Typically all files have different formats. Consequently, preparing input and managing output is a complex and error-prone task. Proprietary software tools are available that facilitate the preparation of input files and analysis of model outcomes. The use of such software may be limited if it does not support all the features of the groundwater model or when the costs of such tools are prohibitive. Therefore a Python library was developed that contains routines to generate input files and process output files of MODFLOW/MT3DMS based models. The library is freely available and has an open structure so that the routines can be customized and linked into other scripts and libraries. The current set of functions supports the generation of input files for MODFLOW and MT3DMS, including the capability to read spatially-distributed input parameters (e.g. hydraulic conductivity) from PNG files. Both ASCII and binary output files can be read efficiently allowing for visualization of, for example, solute concentration patterns in contour plots with superimposed flow vectors using matplotlib. Series of contour plots are then easily saved as an animation. The subroutines can also be used within scripts to calculate derived quantities such as the mass of a solute within a particular region of the model domain. Using Python as a wrapper around groundwater models provides an efficient and flexible way of processing input and output data, which is not constrained by limitations of third-party products.

Adaptive and neuroadaptive control for nonnegative and compartmental dynamical systems

NASA Astrophysics Data System (ADS)

Volyanskyy, Kostyantyn Y.

Neural networks have been extensively used for adaptive system identification as well as adaptive and neuroadaptive control of highly uncertain systems. The goal of adaptive and neuroadaptive control is to achieve system performance without excessive reliance on system models. To improve robustness and the speed of adaptation of adaptive and neuroadaptive controllers several controller architectures have been proposed in the literature. In this dissertation, we develop a new neuroadaptive control architecture for nonlinear uncertain dynamical systems. The proposed framework involves a novel controller architecture with additional terms in the update laws that are constructed using a moving window of the integrated system uncertainty. These terms can be used to identify the ideal system weights of the neural network as well as effectively suppress system uncertainty. Linear and nonlinear parameterizations of the system uncertainty are considered and state and output feedback neuroadaptive controllers are developed. Furthermore, we extend the developed framework to discrete-time dynamical systems. To illustrate the efficacy of the proposed approach we apply our results to an aircraft model with wing rock dynamics, a spacecraft model with unknown moment of inertia, and an unmanned combat aerial vehicle undergoing actuator failures, and compare our results with standard neuroadaptive control methods. Nonnegative systems are essential in capturing the behavior of a wide range of dynamical systems involving dynamic states whose values are nonnegative. A sub-class of nonnegative dynamical systems are compartmental systems. These systems are derived from mass and energy balance considerations and are comprised of homogeneous interconnected microscopic subsystems or compartments which exchange variable quantities of material via intercompartmental flow laws. In this dissertation, we develop direct adaptive and neuroadaptive control framework for stabilization, disturbance rejection and noise suppression for nonnegative and compartmental dynamical systems with noise and exogenous system disturbances. We then use the developed framework to control the infusion of the anesthetic drug propofol for maintaining a desired constant level of depth of anesthesia for surgery in the face of continuing hemorrhage and hemodilution. Critical care patients, whether undergoing surgery or recovering in intensive care units, require drug administration to regulate physiological variables such as blood pressure, cardiac output, heart rate, and degree of consciousness. The rate of infusion of each administered drug is critical, requiring constant monitoring and frequent adjustments. In this dissertation, we develop a neuroadaptive output feedback control framework for nonlinear uncertain nonnegative and compartmental systems with nonnegative control inputs and noisy measurements. The proposed framework is Lyapunov-based and guarantees ultimate boundedness of the error signals. In addition, the neuroadaptive controller guarantees that the physical system states remain in the nonnegative orthant of the state space. Finally, the developed approach is used to control the infusion of the anesthetic drug propofol for maintaining a desired constant level of depth of anesthesia for surgery in the face of noisy electroencephalographic (EEG) measurements. Clinical trials demonstrate excellent regulation of unconsciousness allowing for a safe and effective administration of the anesthetic agent propofol. Furthermore, a neuroadaptive output feedback control architecture for nonlinear nonnegative dynamical systems with input amplitude and integral constraints is developed. Specifically, the neuroadaptive controller guarantees that the imposed amplitude and integral input constraints are satisfied and the physical system states remain in the nonnegative orthant of the state space. The proposed approach is used to control the infusion of the anesthetic drug propofol for maintaining a desired constant level of depth of anesthesia for noncardiac surgery in the face of infusion rate constraints and a drug dosing constraint over a specified period. In addition, the aforementioned control architecture is used to control lung volume and minute ventilation with input pressure constraints that also accounts for spontaneous breathing by the patient. Specifically, we develop a pressure- and work-limited neuroadaptive controller for mechanical ventilation based on a nonlinear multi-compartmental lung model. The control framework does not rely on any averaged data and is designed to automatically adjust the input pressure to the patient's physiological characteristics capturing lung resistance and compliance modeling uncertainty. Moreover, the controller accounts for input pressure constraints as well as work of breathing constraints. The effect of spontaneous breathing is incorporated within the lung model and the control framework. Finally, a neural network hybrid adaptive control framework for nonlinear uncertain hybrid dynamical systems is developed. The proposed hybrid adaptive control framework is Lyapunov-based and guarantees partial asymptotic stability of the closed-loop hybrid system; that is, asymptotic stability with respect to part of the closed-loop system states associated with the hybrid plant states. A numerical example is provided to demonstrate the efficacy of the proposed hybrid adaptive stabilization approach.

Enhancement of laser power-efficiency by control of spatial hole burning interactions

NASA Astrophysics Data System (ADS)

Ge, Li; Malik, Omer; Türeci, Hakan E.

2014-11-01

The laser is an out-of-equilibrium nonlinear wave system where the interplay of the cavity geometry and nonlinear wave interactions mediated by the gain medium determines the self-organized oscillation frequencies and the associated spatial field patterns. In the steady state, a constant energy flux flows through the laser from the pump to the far field, with the ratio of the total output power to the input power determining the power-efficiency. Although nonlinear wave interactions have been modelled and well understood since the early days of laser theory, their impact on the power-efficiency of a laser system is poorly understood. Here, we show that spatial hole burning interactions generally decrease the power-efficiency. We then demonstrate how spatial hole burning interactions can be controlled by a spatially tailored pump profile, thereby boosting the power-efficiency, in some cases by orders of magnitude.

Nonlinear frequency compression: Influence of start frequency and input bandwidth on consonant and vowel recognitiona)

PubMed Central

Alexander, Joshua M.

2016-01-01

By varying parameters that control nonlinear frequency compression (NFC), this study examined how different ways of compressing inaudible mid- and/or high-frequency information at lower frequencies influences perception of consonants and vowels. Twenty-eight listeners with mild to moderately severe hearing loss identified consonants and vowels from nonsense syllables in noise following amplification via a hearing aid simulator. Low-pass filtering and the selection of NFC parameters fixed the output bandwidth at a frequency representing a moderately severe (3.3 kHz, group MS) or a mild-to-moderate (5.0 kHz, group MM) high-frequency loss. For each group (n = 14), effects of six combinations of NFC start frequency (SF) and input bandwidth [by varying the compression ratio (CR)] were examined. For both groups, the 1.6 kHz SF significantly reduced vowel and consonant recognition, especially as CR increased; whereas, recognition was generally unaffected if SF increased at the expense of a higher CR. Vowel recognition detriments for group MS were moderately correlated with the size of the second formant frequency shift following NFC. For both groups, significant improvement (33%–50%) with NFC was confined to final /s/ and /z/ and to some VCV tokens, perhaps because of listeners' limited exposure to each setting. No set of parameters simultaneously maximized recognition across all tokens. PMID:26936574

Approximate circuits for increased reliability

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hamlet, Jason R.; Mayo, Jackson R.

2015-08-18

Embodiments of the invention describe a Boolean circuit having a voter circuit and a plurality of approximate circuits each based, at least in part, on a reference circuit. The approximate circuits are each to generate one or more output signals based on values of received input signals. The voter circuit is to receive the one or more output signals generated by each of the approximate circuits, and is to output one or more signals corresponding to a majority value of the received signals. At least some of the approximate circuits are to generate an output value different than the referencemore » circuit for one or more input signal values; however, for each possible input signal value, the majority values of the one or more output signals generated by the approximate circuits and received by the voter circuit correspond to output signal result values of the reference circuit.« less

Design of the small pixel pitch ROIC

NASA Astrophysics Data System (ADS)

Liang, Qinghua; Jiang, Dazhao; Chen, Honglei; Zhai, Yongcheng; Gao, Lei; Ding, Ruijun

2014-11-01

Since the technology trend of the third generation IRFPA towards resolution enhancing has steadily progressed,the pixel pitch of IRFPA has been greatly reduced.A 640×512 readout integrated circuit(ROIC) of IRFPA with 15μm pixel pitch is presented in this paper.The 15μm pixel pitch ROIC design will face many challenges.As we all known,the integrating capacitor is a key performance parameter when considering pixel area,charge capacity and dynamic range,so we adopt the effective method of 2 by 2 pixels sharing an integrating capacitor to solve this problem.The input unit cell architecture will contain two paralleled sample and hold parts,which not only allow the FPA to be operated in full frame snapshot mode but also save relatively unit circuit area.Different applications need more matching input unit circuits. Because the dimension of 2×2 pixels is 30μm×30μm, an input stage based on direct injection (DI) which has medium injection ratio and small layout area is proved to be suitable for middle wave (MW) while BDI with three-transistor cascode amplifier for long wave(LW). By adopting the 0.35μm 2P4M mixed signal process, the circuit architecture can make the effective charge capacity of 7.8Me- per pixel with 2.2V output range for MW and 7.3 Me- per pixel with 2.6V output range for LW. According to the simulation results, this circuit works well under 5V power supply and achieves less than 0.1% nonlinearity.

Ultralong relaxation times in bistable hybrid quantum systems

PubMed Central

Angerer, Andreas; Putz, Stefan; Krimer, Dmitry O.; Astner, Thomas; Zens, Matthias; Glattauer, Ralph; Streltsov, Kirill; Munro, William J.; Nemoto, Kae; Rotter, Stefan; Schmiedmayer, Jörg; Majer, Johannes

2017-01-01

Nonlinear systems, whose outputs are not directly proportional to their inputs, are well known to exhibit many interesting and important phenomena that have profoundly changed our technological landscape over the last 50 years. Recently, the ability to engineer quantum metamaterials through hybridization has allowed us to explore these nonlinear effects in systems with no natural analog. We investigate amplitude bistability, which is one of the most fundamental nonlinear phenomena, in a hybrid system composed of a superconducting resonator inductively coupled to an ensemble of nitrogen-vacancy centers. One of the exciting properties of this spin system is its long spin lifetime, which is many orders of magnitude longer than other relevant time scales of the hybrid system. This allows us to dynamically explore this nonlinear regime of cavity quantum electrodynamics and demonstrate a critical slowing down of the cavity population on the order of several tens of thousands of seconds—a time scale much longer than observed so far for this effect. Our results provide a foundation for future quantum technologies based on nonlinear phenomena. PMID:29230435

DAISY: a new software tool to test global identifiability of biological and physiological systems.

PubMed

Bellu, Giuseppina; Saccomani, Maria Pia; Audoly, Stefania; D'Angiò, Leontina

2007-10-01

A priori global identifiability is a structural property of biological and physiological models. It is considered a prerequisite for well-posed estimation, since it concerns the possibility of recovering uniquely the unknown model parameters from measured input-output data, under ideal conditions (noise-free observations and error-free model structure). Of course, determining if the parameters can be uniquely recovered from observed data is essential before investing resources, time and effort in performing actual biomedical experiments. Many interesting biological models are nonlinear but identifiability analysis for nonlinear system turns out to be a difficult mathematical problem. Different methods have been proposed in the literature to test identifiability of nonlinear models but, to the best of our knowledge, so far no software tools have been proposed for automatically checking identifiability of nonlinear models. In this paper, we describe a software tool implementing a differential algebra algorithm to perform parameter identifiability analysis for (linear and) nonlinear dynamic models described by polynomial or rational equations. Our goal is to provide the biological investigator a completely automatized software, requiring minimum prior knowledge of mathematical modelling and no in-depth understanding of the mathematical tools. The DAISY (Differential Algebra for Identifiability of SYstems) software will potentially be useful in biological modelling studies, especially in physiology and clinical medicine, where research experiments are particularly expensive and/or difficult to perform. Practical examples of use of the software tool DAISY are presented. DAISY is available at the web site http://www.dei.unipd.it/~pia/.

SCI Identification (SCIDNT) program user's guide. [maximum likelihood method for linear rotorcraft models

NASA Technical Reports Server (NTRS)

1979-01-01

The computer program Linear SCIDNT which evaluates rotorcraft stability and control coefficients from flight or wind tunnel test data is described. It implements the maximum likelihood method to maximize the likelihood function of the parameters based on measured input/output time histories. Linear SCIDNT may be applied to systems modeled by linear constant-coefficient differential equations. This restriction in scope allows the application of several analytical results which simplify the computation and improve its efficiency over the general nonlinear case.

Multi-Input Multi-Output Flight Control System Design for the YF-16 Using Nonlinear QFT and Pilot Compensation

DTIC Science & Technology

1990-12-01

methods are implemented in MATRIXx with the programs SISOTF and MIMOTF respectively. Following the mathe - matical development, the application of these...intent is not to teach any of the methods , it has been written in a manner to significantly assist an individual attempting follow on work. I would...equivalent plant models. A detailed mathematical development of the method used to develop these equivalent LTI plant models is provided. After this inner

Non Linear Dynamics and Chaos (La Dynamique Non-Lineaie et le Chaos)

DTIC Science & Technology

1993-06-01

mention some aspects of non-linear dynamics and/or disorder is independence. All other definitions are merely negative chaos which p,.Laps a,, margin- al ...collection of fixed coefficients and input-output corellation is U1, pp. 1037-1044, 1990, Roger Cerf et al . Among ,ie very slightly modified. If Rl is...des param~tres qui ou la deformation 61astique d’une partie du sol d~finissent le syst~me A contr~ler, et donne lieu, soudainement A un saut

Alternatives for jet engine control

NASA Technical Reports Server (NTRS)

Sain, M. K.

1979-01-01

The research is classified in two categories: (1) the use of modern multivariable frequency domain methods for control of engine models in the neighborhood of a set-point, and (2) the use of nonlinear modelling and optimization techniques for control of engine models over a more extensive part of the flight envelope. Progress in the first category included the extension of CARDIAD (Complex Acceptability Region for Diagonal Dominance) methods developed with the help of the grant to the case of engine models with four inputs and four outputs. A suitable bounding procedure for the dominance function was determined. Progress in the second category had its principal focus on automatic nonlinear model generation. Simulations of models produced satisfactory results where compared with the NASA DYNGEN digital engine deck.

Asymmetric temporal integration of layer 4 and layer 2/3 inputs in visual cortex.

PubMed

Hang, Giao B; Dan, Yang

2011-01-01

Neocortical neurons in vivo receive concurrent synaptic inputs from multiple sources, including feedforward, horizontal, and feedback pathways. Layer 2/3 of the visual cortex receives feedforward input from layer 4 and horizontal input from layer 2/3. Firing of the pyramidal neurons, which carries the output to higher cortical areas, depends critically on the interaction of these pathways. Here we examined synaptic integration of inputs from layer 4 and layer 2/3 in rat visual cortical slices. We found that the integration is sublinear and temporally asymmetric, with larger responses if layer 2/3 input preceded layer 4 input. The sublinearity depended on inhibition, and the asymmetry was largely attributable to the difference between the two inhibitory inputs. Interestingly, the asymmetric integration was specific to pyramidal neurons, and it strongly affected their spiking output. Thus via cortical inhibition, the temporal order of activation of layer 2/3 and layer 4 pathways can exert powerful control of cortical output during visual processing.

Probabilistic switching circuits in DNA

PubMed Central

Wilhelm, Daniel; Bruck, Jehoshua

2018-01-01

A natural feature of molecular systems is their inherent stochastic behavior. A fundamental challenge related to the programming of molecular information processing systems is to develop a circuit architecture that controls the stochastic states of individual molecular events. Here we present a systematic implementation of probabilistic switching circuits, using DNA strand displacement reactions. Exploiting the intrinsic stochasticity of molecular interactions, we developed a simple, unbiased DNA switch: An input signal strand binds to the switch and releases an output signal strand with probability one-half. Using this unbiased switch as a molecular building block, we designed DNA circuits that convert an input signal to an output signal with any desired probability. Further, this probability can be switched between 2n different values by simply varying the presence or absence of n distinct DNA molecules. We demonstrated several DNA circuits that have multiple layers and feedback, including a circuit that converts an input strand to an output strand with eight different probabilities, controlled by the combination of three DNA molecules. These circuits combine the advantages of digital and analog computation: They allow a small number of distinct input molecules to control a diverse signal range of output molecules, while keeping the inputs robust to noise and the outputs at precise values. Moreover, arbitrarily complex circuit behaviors can be implemented with just a single type of molecular building block. PMID:29339484

Characterization of real-world vibration sources with a view toward optimal energy harvesting architectures

NASA Astrophysics Data System (ADS)

Rantz, Robert; Roundy, Shad

2016-04-01

A tremendous amount of research has been performed on the design and analysis of vibration energy harvester architectures with the goal of optimizing power output; most studies assume idealized input vibrations without paying much attention to whether such idealizations are broadly representative of real sources. These "idealized input signals" are typically derived from the expected nature of the vibrations produced from a given source. Little work has been done on corroborating these expectations by virtue of compiling a comprehensive list of vibration signals organized by detailed classifications. Vibration data representing 333 signals were collected from the NiPS Laboratory "Real Vibration" database, processed, and categorized according to the source of the signal (e.g. animal, machine, etc.), the number of dominant frequencies, the nature of the dominant frequencies (e.g. stationary, band-limited noise, etc.), and other metrics. By categorizing signals in this way, the set of idealized vibration inputs commonly assumed for harvester input can be corroborated and refined, and heretofore overlooked vibration input types have motivation for investigation. An initial qualitative analysis of vibration signals has been undertaken with the goal of determining how often a standard linear oscillator based harvester is likely the optimal architecture, and how often a nonlinear harvester with a cubic stiffness function might provide improvement. Although preliminary, the analysis indicates that in at least 23% of cases, a linear harvester is likely optimal and in no more than 53% of cases would a nonlinear cubic stiffness based harvester provide improvement.

H∞ output tracking control of uncertain and disturbed nonlinear systems based on neural network model

NASA Astrophysics Data System (ADS)

Li, Chengcheng; Li, Yuefeng; Wang, Guanglin

2017-07-01

The work presented in this paper seeks to address the tracking problem for uncertain continuous nonlinear systems with external disturbances. The objective is to obtain a model that uses a reference-based output feedback tracking control law. The control scheme is based on neural networks and a linear difference inclusion (LDI) model, and a PDC structure and H∞ performance criterion are used to attenuate external disturbances. The stability of the whole closed-loop model is investigated using the well-known quadratic Lyapunov function. The key principles of the proposed approach are as follows: neural networks are first used to approximate nonlinearities, to enable a nonlinear system to then be represented as a linearised LDI model. An LMI (linear matrix inequality) formula is obtained for uncertain and disturbed linear systems. This formula enables a solution to be obtained through an interior point optimisation method for some nonlinear output tracking control problems. Finally, simulations and comparisons are provided on two practical examples to illustrate the validity and effectiveness of the proposed method.

A controls engineering approach for analyzing airplane input-output characteristics

NASA Technical Reports Server (NTRS)

Arbuckle, P. Douglas

1991-01-01

An engineering approach for analyzing airplane control and output characteristics is presented. State-space matrix equations describing the linear perturbation dynamics are transformed from physical coordinates into scaled coordinates. The scaling is accomplished by applying various transformations to the system to employ prior engineering knowledge of the airplane physics. Two different analysis techniques are then explained. Modal analysis techniques calculate the influence of each system input on each fundamental mode of motion and the distribution of each mode among the system outputs. The optimal steady state response technique computes the blending of steady state control inputs that optimize the steady state response of selected system outputs. Analysis of an example airplane model is presented to demonstrate the described engineering approach.

Neural network-based optimal adaptive output feedback control of a helicopter UAV.

PubMed

Nodland, David; Zargarzadeh, Hassan; Jagannathan, Sarangapani

2013-07-01

Helicopter unmanned aerial vehicles (UAVs) are widely used for both military and civilian operations. Because the helicopter UAVs are underactuated nonlinear mechanical systems, high-performance controller design for them presents a challenge. This paper introduces an optimal controller design via an output feedback for trajectory tracking of a helicopter UAV, using a neural network (NN). The output-feedback control system utilizes the backstepping methodology, employing kinematic and dynamic controllers and an NN observer. The online approximator-based dynamic controller learns the infinite-horizon Hamilton-Jacobi-Bellman equation in continuous time and calculates the corresponding optimal control input by minimizing a cost function, forward-in-time, without using the value and policy iterations. Optimal tracking is accomplished by using a single NN utilized for the cost function approximation. The overall closed-loop system stability is demonstrated using Lyapunov analysis. Finally, simulation results are provided to demonstrate the effectiveness of the proposed control design for trajectory tracking.

Short-term forecasting of electric loads using nonlinear autoregressive artificial neural networks with exogenous vector inputs

DOE PAGES

Buitrago, Jaime; Asfour, Shihab

2017-01-01

Short-term load forecasting is crucial for the operations planning of an electrical grid. Forecasting the next 24 h of electrical load in a grid allows operators to plan and optimize their resources. The purpose of this study is to develop a more accurate short-term load forecasting method utilizing non-linear autoregressive artificial neural networks (ANN) with exogenous multi-variable input (NARX). The proposed implementation of the network is new: the neural network is trained in open-loop using actual load and weather data, and then, the network is placed in closed-loop to generate a forecast using the predicted load as the feedback input.more » Unlike the existing short-term load forecasting methods using ANNs, the proposed method uses its own output as the input in order to improve the accuracy, thus effectively implementing a feedback loop for the load, making it less dependent on external data. Using the proposed framework, mean absolute percent errors in the forecast in the order of 1% have been achieved, which is a 30% improvement on the average error using feedforward ANNs, ARMAX and state space methods, which can result in large savings by avoiding commissioning of unnecessary power plants. Finally, the New England electrical load data are used to train and validate the forecast prediction.« less

Short-term forecasting of electric loads using nonlinear autoregressive artificial neural networks with exogenous vector inputs

DOE Office of Scientific and Technical Information (OSTI.GOV)

Buitrago, Jaime; Asfour, Shihab

Short-term load forecasting is crucial for the operations planning of an electrical grid. Forecasting the next 24 h of electrical load in a grid allows operators to plan and optimize their resources. The purpose of this study is to develop a more accurate short-term load forecasting method utilizing non-linear autoregressive artificial neural networks (ANN) with exogenous multi-variable input (NARX). The proposed implementation of the network is new: the neural network is trained in open-loop using actual load and weather data, and then, the network is placed in closed-loop to generate a forecast using the predicted load as the feedback input.more » Unlike the existing short-term load forecasting methods using ANNs, the proposed method uses its own output as the input in order to improve the accuracy, thus effectively implementing a feedback loop for the load, making it less dependent on external data. Using the proposed framework, mean absolute percent errors in the forecast in the order of 1% have been achieved, which is a 30% improvement on the average error using feedforward ANNs, ARMAX and state space methods, which can result in large savings by avoiding commissioning of unnecessary power plants. Finally, the New England electrical load data are used to train and validate the forecast prediction.« less

Modified nonlinear amplifying loop mirror for mode-locked fibre oscillators with record-high energy and high-average-power pulsed output

NASA Astrophysics Data System (ADS)

Kobtsev, Sergey; Ivanenko, Alexey; Smirnov, Sergey; Kokhanovsky, Alexey

2018-02-01

The present work proposes and studies approaches for development of new modified non-linear amplifying loop mirror (NALM) allowing flexible and dynamic control of their non-linear properties within a relatively broad range of radiation powers. Using two independently pumped active media in the loop reflector constitutes one of the most promising approaches to development of better NALM with nonlinear properties controllable independently of the intra-cavity radiation power. This work reports on experimental and theoretical studies of the proposed redesigned NALM allowing both a higher level of energy parameters of output generated by mode-locked fibre oscillators and new possibilities of switching among different mode-locked regimes.

Transport coefficient computation based on input/output reduced order models

NASA Astrophysics Data System (ADS)

Hurst, Joshua L.

The guiding purpose of this thesis is to address the optimal material design problem when the material description is a molecular dynamics model. The end goal is to obtain a simplified and fast model that captures the property of interest such that it can be used in controller design and optimization. The approach is to examine model reduction analysis and methods to capture a specific property of interest, in this case viscosity, or more generally complex modulus or complex viscosity. This property and other transport coefficients are defined by a input/output relationship and this motivates model reduction techniques that are tailored to preserve input/output behavior. In particular Singular Value Decomposition (SVD) based methods are investigated. First simulation methods are identified that are amenable to systems theory analysis. For viscosity, these models are of the Gosling and Lees-Edwards type. They are high order nonlinear Ordinary Differential Equations (ODEs) that employ Periodic Boundary Conditions. Properties can be calculated from the state trajectories of these ODEs. In this research local linear approximations are rigorously derived and special attention is given to potentials that are evaluated with Periodic Boundary Conditions (PBC). For the Gosling description LTI models are developed from state trajectories but are found to have limited success in capturing the system property, even though it is shown that full order LTI models can be well approximated by reduced order LTI models. For the Lees-Edwards SLLOD type model nonlinear ODEs will be approximated by a Linear Time Varying (LTV) model about some nominal trajectory and both balanced truncation and Proper Orthogonal Decomposition (POD) will be used to assess the plausibility of reduced order models to this system description. An immediate application of the derived LTV models is Quasilinearization or Waveform Relaxation. Quasilinearization is a Newton's method applied to the ODE operator equation. Its a recursive method that solves nonlinear ODE's by solving a LTV systems at each iteration to obtain a new closer solution. LTV models are derived for both Gosling and Lees-Edwards type models. Particular attention is given to SLLOD Lees-Edwards models because they are in a form most amenable to performing Taylor series expansion, and the most commonly used model to examine viscosity. With linear models developed a method is presented to calculate viscosity based on LTI Gosling models but is shown to have some limitations. To address these issues LTV SLLOD models are analyzed with both Balanced Truncation and POD and both show that significant order reduction is possible. By examining the singular values of both techniques it is shown that Balanced Truncation has a potential to offer greater reduction, which should be expected as it is based on the input/output mapping instead of just the state information as in POD. Obtaining reduced order systems that capture the property of interest is challenging. For Balanced Truncation reduced order models for 1-D LJ and FENE systems are obtained and are shown to capture the output of interest fairly well. However numerical challenges currently limit this analysis to small order systems. Suggestions are presented to extend this method to larger systems. In addition reduced 2nd order systems are obtained from POD. Here the challenge is extending the solution beyond the original period used for the projection, in particular identifying the manifold the solution travels along. The remaining challenges are presented and discussed.

Performance Analysis of Triple Asymmetrical Optical Micro Ring Resonator with 2 × 2 Input-Output Bus Waveguide

NASA Astrophysics Data System (ADS)

Ranjan, Suman; Mandal, Sanjoy

2017-12-01

Modeling of triple asymmetrical optical micro ring resonator (TAOMRR) in z-domain with 2 × 2 input-output system with detailed design of its waveguide configuration using finite-difference time-domain (FDTD) method is presented. Transfer function in z-domain using delay-line signal processing technique of the proposed TAOMRR is determined for different input and output ports. The frequency response analysis is carried out using MATLAB software. Group delay and dispersion characteristics are also determined in MATLAB. The electric field analysis is done using FDTD. The method proposes a new methodology to design and draw multiple configurations of coupled ring resonators having multiple in and out ports. Various important parameters such as coupling coefficients and FSR are also determined.

Performance Analysis of Triple Asymmetrical Optical Micro Ring Resonator with 2 × 2 Input-Output Bus Waveguide

NASA Astrophysics Data System (ADS)

Ranjan, Suman; Mandal, Sanjoy

2018-02-01

Modeling of triple asymmetrical optical micro ring resonator (TAOMRR) in z-domain with 2 × 2 input-output system with detailed design of its waveguide configuration using finite-difference time-domain (FDTD) method is presented. Transfer function in z-domain using delay-line signal processing technique of the proposed TAOMRR is determined for different input and output ports. The frequency response analysis is carried out using MATLAB software. Group delay and dispersion characteristics are also determined in MATLAB. The electric field analysis is done using FDTD. The method proposes a new methodology to design and draw multiple configurations of coupled ring resonators having multiple in and out ports. Various important parameters such as coupling coefficients and FSR are also determined.

Reservoir computing on the hypersphere

NASA Astrophysics Data System (ADS)

Andrecut, M.

Reservoir Computing (RC) refers to a Recurrent Neural Network (RNNs) framework, frequently used for sequence learning and time series prediction. The RC system consists of a random fixed-weight RNN (the input-hidden reservoir layer) and a classifier (the hidden-output readout layer). Here, we focus on the sequence learning problem, and we explore a different approach to RC. More specifically, we remove the nonlinear neural activation function, and we consider an orthogonal reservoir acting on normalized states on the unit hypersphere. Surprisingly, our numerical results show that the system’s memory capacity exceeds the dimensionality of the reservoir, which is the upper bound for the typical RC approach based on Echo State Networks (ESNs). We also show how the proposed system can be applied to symmetric cryptography problems, and we include a numerical implementation.

A Cellular Neural Networks Based DiffServ Switch for Satellite Communication Systems

NASA Astrophysics Data System (ADS)

Tarchi, Daniele; Fantacci, Romano; Gubellini, Roberto; Pecorella, Tommaso

2003-07-01

Recent developments of Internet services and advanced compression methods has revived interest on IP based multimedia satellite communication systems. However a main problem arising here is to guarantee specific Quality of Service (QoS) constraints in order to have good performance for each traffic class.Among various QoS approach used in Internet, recently the DiffServ technique has became the most promising so- lution, mainly for its simplicity with respect to different alternatives. Moreover, in satellite communication systems, DiffServ policy computational capabilities are placed at the edge points (end-to-end philosophy); this is very important for a network constituted by one satellite link because it allows to reduce the implementation complexity of the satellite on-board equipments.The satellite switch under consideration makes use of the Multiple Input Queuing approach. Packets arrived at a switch input are stored in a shared buffer but they are logically ordered in individual queues, one for each possible output link. According to the DiffServ policy, within a same logical queue, packets are reordered in individual sub-queues according to the priority. A suitable implementation of the DiffServ policy based on a Cellular Neural Network (CNN) is proposed in the paper in order to achieve QoS requirements.The CNNs are a set of linear and nonlinear circuits connected among them that allow parallel and asynchronous computation. CNNs are a class of neural networks similar to Hopfield Neural Networks (HNN), but more flexible and suitable for solving the output contention problem, inherent of switching systems, for VLSI implementation.In this paper a CNN has been designed in order to maximize a cost functional, related to the on-board switch through- put and QoS constraints. The initial state for each neural cell is obtained looking at the presence of at least one packet from a certain input logical queue to a specific output line. The input value for each neural cell is a function of priority and length of each input logical queue. The versatility of neural network make feasible to take the best decision for the packet to be delivered to each output satellite beam, in order to meet specific QoS constraints. Numerical results for CNN approach highlights that Neural network convergence within a time slot is guaranteed, and an optimal, or at least near-optimal, solution in terms of cost function is achieved.The proposed system is based on the IETF (Internet Engineering Task Force) recommendations; this means that traffic entering the switching fabric could be marked as Expedited Forward (EF) or Assured Forward (AF), otherwise handled as Best Effort (BE). Two Assured Forward classes with different emission priority have been implemented, taking into account time spent inside the logical queue and its length. Expedited Forward traffic is typical of services to be delivered with the maximum priority, as streaming or interactive services. The packets, belonging to services that need a certain level of priority with low packet loss, are marked as Assured Forward. Best Effort traffic is related to e-mail or file transfer, or other that have not particular QoS requirements. The CNN used to solve conflict situations act as an arbiter for all the output links. Differently from other Multiple Input Queuing approach, where one arbiter for each output line is present, in proposed approach there exist only one arbiter that make the best decision. The selected rule has been defined in order to give priority to packets, according to opportunely defined functionals characteristic of each traffic class, under the constraint that no more than one packet can be delivered to the same output line. The functionals depend on queue length and time spent inside the queue by front packet.The performance of the proposed DiffServ switch has been derived in terms of delay and jitter; buffer occupancy has been analyzed for different configuration, such as a unique common buffer, one buffer for each input line, one buffer for each input line and each priority class.The obtained results highlight an high flexibility of satellite switch with CNN, taking into account that functional used to calculate priority of each queue could be easily changed, without any complexity gain nor change in CNN structure, in order to consider different traffic characteristic. Numerical results show that proposed algorithm outperform the switches based on Multiple Input Queuing, that use strictly priority methods, in terms of delay and jitter. Different buffer size have been also considered in order to analyze packet loss for CNN switch algorithm, comparing different configuration described above.The good behavior of the proposed DiffServ switch has been verified in the case of traffic with pareto distribution for packet length and a geometrical distribution for packet interarrival time, highlighting good performance in terms of delay and jitter. Numerical results also demonstrate the stability of this method for heavy load traffic; in particular maximum permitted load is higher for higher priority classes.

Proceedings of the Conference on Moments and Signal

NASA Astrophysics Data System (ADS)

Purdue, P.; Solomon, H.

1992-09-01

The focus of this paper is (1) to describe systematic methodologies for selecting nonlinear transformations for blind equalization algorithms (and thus new types of cumulants), and (2) to give an overview of the existing blind equalization algorithms and point out their strengths as well as weaknesses. It is shown that all blind equalization algorithms belong in one of the following three categories, depending where the nonlinear transformation is being applied on the data: (1) the Bussgang algorithms, where the nonlinearity is in the output of the adaptive equalization filter; (2) the polyspectra (or Higher-Order Spectra) algorithms, where the nonlinearity is in the input of the adaptive equalization filter; and (3) the algorithms where the nonlinearity is inside the adaptive filter, i.e., the nonlinear filter or neural network. We describe methodologies for selecting nonlinear transformations based on various optimality criteria such as MSE or MAP. We illustrate that such existing algorithms as Sato, Benveniste-Goursat, Godard or CMA, Stop-and-Go, and Donoho are indeed special cases of the Bussgang family of techniques when the nonlinearity is memoryless. We present results that demonstrate the polyspectra-based algorithms exhibit faster convergence rate than Bussgang algorithms. However, this improved performance is at the expense of more computations per iteration. We also show that blind equalizers based on nonlinear filters or neural networks are more suited for channels that have nonlinear distortions.

Approximation-Based Adaptive Neural Tracking Control of Nonlinear MIMO Unknown Time-Varying Delay Systems With Full State Constraints.

PubMed

Li, Da-Peng; Li, Dong-Juan; Liu, Yan-Jun; Tong, Shaocheng; Chen, C L Philip

2017-10-01

This paper deals with the tracking control problem for a class of nonlinear multiple input multiple output unknown time-varying delay systems with full state constraints. To overcome the challenges which cause by the appearances of the unknown time-varying delays and full-state constraints simultaneously in the systems, an adaptive control method is presented for such systems for the first time. The appropriate Lyapunov-Krasovskii functions and a separation technique are employed to eliminate the effect of unknown time-varying delays. The barrier Lyapunov functions are employed to prevent the violation of the full state constraints. The singular problems are dealt with by introducing the signal function. Finally, it is proven that the proposed method can both guarantee the good tracking performance of the systems output, all states are remained in the constrained interval and all the closed-loop signals are bounded in the design process based on choosing appropriate design parameters. The practicability of the proposed control technique is demonstrated by a simulation study in this paper.

Finite-horizon control-constrained nonlinear optimal control using single network adaptive critics.

PubMed

Heydari, Ali; Balakrishnan, Sivasubramanya N

2013-01-01

To synthesize fixed-final-time control-constrained optimal controllers for discrete-time nonlinear control-affine systems, a single neural network (NN)-based controller called the Finite-horizon Single Network Adaptive Critic is developed in this paper. Inputs to the NN are the current system states and the time-to-go, and the network outputs are the costates that are used to compute optimal feedback control. Control constraints are handled through a nonquadratic cost function. Convergence proofs of: 1) the reinforcement learning-based training method to the optimal solution; 2) the training error; and 3) the network weights are provided. The resulting controller is shown to solve the associated time-varying Hamilton-Jacobi-Bellman equation and provide the fixed-final-time optimal solution. Performance of the new synthesis technique is demonstrated through different examples including an attitude control problem wherein a rigid spacecraft performs a finite-time attitude maneuver subject to control bounds. The new formulation has great potential for implementation since it consists of only one NN with single set of weights and it provides comprehensive feedback solutions online, though it is trained offline.