A new optimized GA-RBF neural network algorithm.
Jia, Weikuan; Zhao, Dean; Shen, Tian; Su, Chunyang; Hu, Chanli; Zhao, Yuyan
2014-01-01
When confronting the complex problems, radial basis function (RBF) neural network has the advantages of adaptive and self-learning ability, but it is difficult to determine the number of hidden layer neurons, and the weights learning ability from hidden layer to the output layer is low; these deficiencies easily lead to decreasing learning ability and recognition precision. Aiming at this problem, we propose a new optimized RBF neural network algorithm based on genetic algorithm (GA-RBF algorithm), which uses genetic algorithm to optimize the weights and structure of RBF neural network; it chooses new ways of hybrid encoding and optimizing simultaneously. Using the binary encoding encodes the number of the hidden layer's neurons and using real encoding encodes the connection weights. Hidden layer neurons number and connection weights are optimized simultaneously in the new algorithm. However, the connection weights optimization is not complete; we need to use least mean square (LMS) algorithm for further leaning, and finally get a new algorithm model. Using two UCI standard data sets to test the new algorithm, the results show that the new algorithm improves the operating efficiency in dealing with complex problems and also improves the recognition precision, which proves that the new algorithm is valid.
A New Optimized GA-RBF Neural Network Algorithm
Zhao, Dean; Su, Chunyang; Hu, Chanli; Zhao, Yuyan
2014-01-01
When confronting the complex problems, radial basis function (RBF) neural network has the advantages of adaptive and self-learning ability, but it is difficult to determine the number of hidden layer neurons, and the weights learning ability from hidden layer to the output layer is low; these deficiencies easily lead to decreasing learning ability and recognition precision. Aiming at this problem, we propose a new optimized RBF neural network algorithm based on genetic algorithm (GA-RBF algorithm), which uses genetic algorithm to optimize the weights and structure of RBF neural network; it chooses new ways of hybrid encoding and optimizing simultaneously. Using the binary encoding encodes the number of the hidden layer's neurons and using real encoding encodes the connection weights. Hidden layer neurons number and connection weights are optimized simultaneously in the new algorithm. However, the connection weights optimization is not complete; we need to use least mean square (LMS) algorithm for further leaning, and finally get a new algorithm model. Using two UCI standard data sets to test the new algorithm, the results show that the new algorithm improves the operating efficiency in dealing with complex problems and also improves the recognition precision, which proves that the new algorithm is valid. PMID:25371666
Analog neural nonderivative optimizers.
Teixeira, M M; Zak, S H
1998-01-01
Continuous-time neural networks for solving convex nonlinear unconstrained programming problems without using gradient information of the objective function are proposed and analyzed. Thus, the proposed networks are nonderivative optimizers. First, networks for optimizing objective functions of one variable are discussed. Then, an existing one-dimensional optimizer is analyzed, and a new line search optimizer is proposed. It is shown that the proposed optimizer network is robust in the sense that it has disturbance rejection property. The network can be implemented easily in hardware using standard circuit elements. The one-dimensional net is used as a building block in multidimensional networks for optimizing objective functions of several variables. The multidimensional nets implement a continuous version of the coordinate descent method.
Forecasting of Market Clearing Price by Using GA Based Neural Network
NASA Astrophysics Data System (ADS)
Yang, Bo; Chen, Yun-Ping; Zhao, Zun-Lian; Han, Qi-Ye
Forecasting of Market Clearing Price (MCP) is important to economic benefits of electricity market participants. To accurately forecast MCP, a novel two-stage GA-based neural network model (GA-NN) is proposed. In the first stage, GA chromosome is designed into two parts: boolean coding part for neural network topology and real coding part for connection weights. By hybrid genetic operation of selection, crossover and mutation under the criterion of error minimization between the actual output and the desired output, optimal architecture of neural network is obtained. In the second stage, gradient learning algorithm with momentum rate is imposed on neural network with optimal architecture. After learning process, optimal connection weights are obtained. The proposed model is tested on MCP forecasting in California electricity market. The test results show that GA-NN has self-adaptive ability in its topology and connection weights and can obtain more accurate MCP forecasting values than BP neural network.
Optimization for training neural nets.
Barnard, E
1992-01-01
Various techniques of optimizing criterion functions to train neural-net classifiers are investigated. These techniques include three standard deterministic techniques (variable metric, conjugate gradient, and steepest descent), and a new stochastic technique. It is found that the stochastic technique is preferable on problems with large training sets and that the convergence rates of the variable metric and conjugate gradient techniques are similar.
Multiscale optimization in neural nets.
Mjolsness, E; Garrett, C D; Miranker, W L
1991-01-01
One way to speed up convergence in a large optimization problem is to introduce a smaller, approximate version of the problem at a coarser scale and to alternate between relaxation steps for the fine-scale and coarse-scale problems. Such an optimization method for neural networks governed by quite general objective functions is presented. At the coarse scale, there is a smaller approximating neural net which, like the original net, is nonlinear and has a nonquadratic objective function. The transitions and information flow from fine to coarse scale and back do not disrupt the optimization, and the user need only specify a partition of the original fine-scale variables. Thus, the method can be applied easily to many problems and networks. There is generally about a fivefold improvement in estimated cost under the multiscale method. In the networks to which it was applied, a nontrivial speedup by a constant factor of between two and five was observed, independent of problem size. Further improvements in computational cost are very likely to be available, especially for problem-specific multiscale neural net methods.
Electronic neural networks for global optimization
NASA Technical Reports Server (NTRS)
Thakoor, A. P.; Moopenn, A. W.; Eberhardt, S.
1990-01-01
An electronic neural network with feedback architecture, implemented in analog custom VLSI is described. Its application to problems of global optimization for dynamic assignment is discussed. The convergence properties of the neural network hardware are compared with computer simulation results. The neural network's ability to provide optimal or near optimal solutions within only a few neuron time constants, a speed enhancement of several orders of magnitude over conventional search methods, is demonstrated. The effect of noise on the circuit dynamics and the convergence behavior of the neural network hardware is also examined.
Groundwater remediation optimization using artificial neural networks
Rogers, L. L., LLNL
1998-05-01
One continuing point of research in optimizing groundwater quality management is reduction of computational burden which is particularly limiting in field-scale applications. Often evaluation of a single pumping strategy, i.e. one call to the groundwater flow and transport model (GFTM) may take several hours on a reasonably fast workstation. For computational flexibility and efficiency, optimal groundwater remediation design at Lawrence Livermore National Laboratory (LLNL) has relied on artificial neural networks (ANNS) trained to approximate the outcome of 2-D field-scale, finite difference/finite element GFTMs. The search itself has been directed primarily by the genetic algorithm (GA) or the simulated annealing (SA) algorithm. This approach has advantages of (1) up to a million fold increase in speed of remediation pattern assessment during the searches and sensitivity analyses for the 2-D LLNL work, (2) freedom from sequential runs of the GFTM (enables workstation farming), and (3) recycling of the knowledge base (i.e. runs of the GFTM necessary to train the ANNS). Reviewed here are the background and motivation for such work, recent applications, and continuing issues of research.
Adaptive optimization and control using neural networks
Mead, W.C.; Brown, S.K.; Jones, R.D.; Bowling, P.S.; Barnes, C.W.
1993-10-22
Recent work has demonstrated the ability of neural-network-based controllers to optimize and control machines with complex, non-linear, relatively unknown control spaces. We present a brief overview of neural networks via a taxonomy illustrating some capabilities of different kinds of neural networks. We present some successful control examples, particularly the optimization and control of a small-angle negative ion source.
High-Gain AlxGa1-xAs/GaAs Transistors For Neural Networks
NASA Technical Reports Server (NTRS)
Kim, Jae-Hoon; Lin, Steven H.
1991-01-01
High-gain AlxGa1-xAs/GaAs npn double heterojunction bipolar transistors developed for use as phototransistors in optoelectronic integrated circuits, especially in artificial neural networks. Transistors perform both photodetection and saturating-amplification functions of neurons. Good candidates for such application because structurally compatible with laser diodes and light-emitting diodes, detect light, and provide high current gain needed to compensate for losses in holographic optical elements.
Application of neural nets in structural optimization
NASA Technical Reports Server (NTRS)
Berke, Laszlo; Hajela, Prabhat
1993-01-01
The biological motivation for Artificial Neural Net developments is briefly discussed, and the most popular paradigm, the feedforward supervised learning net with error back propagation training algorithm, is introduced. Possible approaches for utilization in structural optimization is illustrated through simple examples. Other currently ongoing developments for application in structural mechanics are also mentioned.
Optimal Decision Making in Neural Inhibition Models
ERIC Educational Resources Information Center
van Ravenzwaaij, Don; van der Maas, Han L. J.; Wagenmakers, Eric-Jan
2012-01-01
In their influential "Psychological Review" article, Bogacz, Brown, Moehlis, Holmes, and Cohen (2006) discussed optimal decision making as accomplished by the drift diffusion model (DDM). The authors showed that neural inhibition models, such as the leaky competing accumulator model (LCA) and the feedforward inhibition model (FFI), can mimic the…
The Prediction in Computer Color Matching of Dentistry Based on GA+BP Neural Network
Li, Haisheng; Lai, Long; Chen, Li; Lu, Cheng; Cai, Qiang
2015-01-01
Although the use of computer color matching can reduce the influence of subjective factors by technicians, matching the color of a natural tooth with a ceramic restoration is still one of the most challenging topics in esthetic prosthodontics. Back propagation neural network (BPNN) has already been introduced into the computer color matching in dentistry, but it has disadvantages such as unstable and low accuracy. In our study, we adopt genetic algorithm (GA) to optimize the initial weights and threshold values in BPNN for improving the matching precision. To our knowledge, we firstly combine the BPNN with GA in computer color matching in dentistry. Extensive experiments demonstrate that the proposed method improves the precision and prediction robustness of the color matching in restorative dentistry. PMID:25873990
Neural network training with global optimization techniques.
Yamazaki, Akio; Ludermir, Teresa B
2003-04-01
This paper presents an approach of using Simulated Annealing and Tabu Search for the simultaneous optimization of neural network architectures and weights. The problem considered is the odor recognition in an artificial nose. Both methods have produced networks with high classification performance and low complexity. Generalization has been improved by using the backpropagation algorithm for fine tuning. The combination of simple and traditional search methods has shown to be very suitable for generating compact and efficient networks.
Adeeyo, Adeyemi Ojutalayo; Lateef, Agbaje; Gueguim-Kana, Evariste Bosco
2016-01-01
Exopolysaccharide (EPS) production by a strain of Lentinus edodes was studied via the effects of treatments with ultraviolet (UV) irradiation and acridine orange. Furthermore, optimization of EPS production was studied using a genetic algorithm coupled with an artificial neural network in submerged fermentation. Exposure to irradiation and acridine orange resulted in improved EPS production (2.783 and 5.548 g/L, respectively) when compared with the wild strain (1.044 g/L), whereas optimization led to improved productivity (23.21 g/L). The EPS produced by various strains also demonstrated good DPPH scavenging activities of 45.40-88.90%, and also inhibited the growth of Escherichia coli and Klebsiella pneumoniae. This study shows that multistep optimization schemes involving physical-chemical mutation and media optimization can be an attractive strategy for improving the yield of bioactives from medicinal mushrooms. To the best of our knowledge, this report presents the first reference of a multistep approach to optimizing EPS production in L. edodes. PMID:27649726
GA-optimization for rapid prototype system demonstration
NASA Technical Reports Server (NTRS)
Kim, Jinwoo; Zeigler, Bernard P.
1994-01-01
An application of the Genetic Algorithm (GA) is discussed. A novel scheme of Hierarchical GA was developed to solve complicated engineering problems which require optimization of a large number of parameters with high precision. High level GAs search for few parameters which are much more sensitive to the system performance. Low level GAs search in more detail and employ a greater number of parameters for further optimization. Therefore, the complexity of the search is decreased and the computing resources are used more efficiently.
Convex quadratic optimization on artificial neural networks
Adler, I.; Verma, S.
1994-12-31
We present continuous-valued Hopfield recurrent neural networks on which we map convex quadratic optimization problems. We consider two different convex quadratic programs, each of which is mapped to a different neural network. Activation functions are shown to play a key role in the mapping under each model. The class of activation functions which can be used in this mapping is characterized in terms of the properties needed. It is shown that the first derivatives of penalty as well as barrier functions belong to this class. The trajectories of dynamics under the first model are shown to be closely related to affine-scaling trajectories of interior-point methods. On the other hand, the trajectories of dynamics under the second model correspond to projected steepest descent pathways.
Neural network optimization, components, and design selection
NASA Astrophysics Data System (ADS)
Weller, Scott W.
1990-07-01
Neural Networks are part of a revived technology which has received a lot of hype in recent years. As is apt to happen in any hyped technology, jargon and predictions make its assimilation and application difficult. Nevertheless, Neural Networks have found use in a number of areas, working on non-trivial and noncontrived problems. For example, one net has been trained to "read", translating English text into phoneme sequences. Other applications of Neural Networks include data base manipulation and the solving of muting and classification types of optimization problems. Neural Networks are constructed from neurons, which in electronics or software attempt to model but are not constrained by the real thing, i.e., neurons in our gray matter. Neurons are simple processing units connected to many other neurons over pathways which modify the incoming signals. A single synthetic neuron typically sums its weighted inputs, runs this sum through a non-linear function, and produces an output. In the brain, neurons are connected in a complex topology: in hardware/software the topology is typically much simpler, with neurons lying side by side, forming layers of neurons which connect to the layer of neurons which receive their outputs. This simplistic model is much easier to construct than the real thing, and yet can solve real problems. The information in a network, or its "memory", is completely contained in the weights on the connections from one neuron to another. Establishing these weights is called "training" the network. Some networks are trained by design -- once constructed no further learning takes place. Other types of networks require iterative training once wired up, but are not trainable once taught Still other types of networks can continue to learn after initial construction. The main benefit to using Neural Networks is their ability to work with conflicting or incomplete ("fuzzy") data sets. This ability and its usefulness will become evident in the following
Second-order neural nets for constrained optimization.
Zhang, S; Zhu, X; Zou, L H
1992-01-01
Analog neural nets for constrained optimization are proposed as an analogue of Newton's algorithm in numerical analysis. The neural model is globally stable and can converge to the constrained stationary points. Nonlinear neurons are introduced into the net, making it possible to solve optimization problems where the variables take discrete values, i.e., combinatorial optimization.
NASA Astrophysics Data System (ADS)
Thompson, D. E.; Rajkumar, T.
2002-12-01
The San Francisco Bay Delta is a large hydrodynamic complex that incorporates the Sacramento and San Joaquin Estuaries, the Suisan Marsh, and the San Francisco Bay proper. Competition exists for the use of this extensive water system both from the fisheries industry, the agricultural industry, and from the marine and estuarine animal species within the Delta. As tidal fluctuations occur, more saline water pushes upstream allowing fish to migrate beyond the Suisan Marsh for breeding and habitat occupation. However, the agriculture industry does not want extensive salinity intrusion to impact water quality for human and plant consumption. The balance is regulated by pumping stations located along the estuaries and reservoirs whereby flushing of fresh water keeps the saline intrusion at bay. The pumping schedule is driven by data collected at various locations within the Bay Delta and by numerical models that predict the salinity intrusion as part of a larger model of the system. The Interagency Ecological Program (IEP) for the San Francisco Bay / Sacramento-San Joaquin Estuary collects, monitors, and archives the data, and the Department of Water Resources provides a numerical model simulation (DSM2) from which predictions are made that drive the pumping schedule. A problem with DSM2 is that the numerical simulation takes roughly 16 hours to complete a prediction. We have created a neural net, optimized with a genetic algorithm, that takes as input the archived data from multiple gauging stations and predicts stage, salinity, and flow at the Carquinez Straits (at the downstream end of the Suisan Marsh). This model seems to be robust in its predictions and operates much faster than the current numerical DSM2 model. Because the Bay-Delta is strongly tidally driven, we used both Principal Component Analysis and Fast Fourier Transforms to discover dominant features within the IEP data. We then filtered out the dominant tidal forcing to discover non-primary tidal effects
NASA Technical Reports Server (NTRS)
Rajkumar, T.; Bardina, Jorge; Clancy, Daniel (Technical Monitor)
2002-01-01
Wind tunnels use scale models to characterize aerodynamic coefficients, Wind tunnel testing can be slow and costly due to high personnel overhead and intensive power utilization. Although manual curve fitting can be done, it is highly efficient to use a neural network to define the complex relationship between variables. Numerical simulation of complex vehicles on the wide range of conditions required for flight simulation requires static and dynamic data. Static data at low Mach numbers and angles of attack may be obtained with simpler Euler codes. Static data of stalled vehicles where zones of flow separation are usually present at higher angles of attack require Navier-Stokes simulations which are costly due to the large processing time required to attain convergence. Preliminary dynamic data may be obtained with simpler methods based on correlations and vortex methods; however, accurate prediction of the dynamic coefficients requires complex and costly numerical simulations. A reliable and fast method of predicting complex aerodynamic coefficients for flight simulation I'S presented using a neural network. The training data for the neural network are derived from numerical simulations and wind-tunnel experiments. The aerodynamic coefficients are modeled as functions of the flow characteristics and the control surfaces of the vehicle. The basic coefficients of lift, drag and pitching moment are expressed as functions of angles of attack and Mach number. The modeled and training aerodynamic coefficients show good agreement. This method shows excellent potential for rapid development of aerodynamic models for flight simulation. Genetic Algorithms (GA) are used to optimize a previously built Artificial Neural Network (ANN) that reliably predicts aerodynamic coefficients. Results indicate that the GA provided an efficient method of optimizing the ANN model to predict aerodynamic coefficients. The reliability of the ANN using the GA includes prediction of aerodynamic
A Projection Neural Network for Constrained Quadratic Minimax Optimization.
Liu, Qingshan; Wang, Jun
2015-11-01
This paper presents a projection neural network described by a dynamic system for solving constrained quadratic minimax programming problems. Sufficient conditions based on a linear matrix inequality are provided for global convergence of the proposed neural network. Compared with some of the existing neural networks for quadratic minimax optimization, the proposed neural network in this paper is capable of solving more general constrained quadratic minimax optimization problems, and the designed neural network does not include any parameter. Moreover, the neural network has lower model complexities, the number of state variables of which is equal to that of the dimension of the optimization problems. The simulation results on numerical examples are discussed to demonstrate the effectiveness and characteristics of the proposed neural network.
Neural network for constrained nonsmooth optimization using Tikhonov regularization.
Qin, Sitian; Fan, Dejun; Wu, Guangxi; Zhao, Lijun
2015-03-01
This paper presents a one-layer neural network to solve nonsmooth convex optimization problems based on the Tikhonov regularization method. Firstly, it is shown that the optimal solution of the original problem can be approximated by the optimal solution of a strongly convex optimization problems. Then, it is proved that for any initial point, the state of the proposed neural network enters the equality feasible region in finite time, and is globally convergent to the unique optimal solution of the related strongly convex optimization problems. Compared with the existing neural networks, the proposed neural network has lower model complexity and does not need penalty parameters. In the end, some numerical examples and application are given to illustrate the effectiveness and improvement of the proposed neural network.
Long, Yi; Du, Zhi-Jiang; Wang, Wei-Dong; Dong, Wei
2016-01-01
A lower limb assistive exoskeleton is designed to help operators walk or carry payloads. The exoskeleton is required to shadow human motion intent accurately and compliantly to prevent incoordination. If the user's intention is estimated accurately, a precise position control strategy will improve collaboration between the user and the exoskeleton. In this paper, a hybrid position control scheme, combining sliding mode control (SMC) with a cerebellar model articulation controller (CMAC) neural network, is proposed to control the exoskeleton to react appropriately to human motion intent. A genetic algorithm (GA) is utilized to determine the optimal sliding surface and the sliding control law to improve performance of SMC. The proposed control strategy (SMC_GA_CMAC) is compared with three other types of approaches, that is, conventional SMC without optimization, optimal SMC with GA (SMC_GA), and SMC with CMAC compensation (SMC_CMAC), all of which are employed to track the desired joint angular position which is deduced from Clinical Gait Analysis (CGA) data. Position tracking performance is investigated with cosimulation using ADAMS and MATLAB/SIMULINK in two cases, of which the first case is without disturbances while the second case is with a bounded disturbance. The cosimulation results show the effectiveness of the proposed control strategy which can be employed in similar exoskeleton systems. PMID:27069353
Long, Yi; Du, Zhi-jiang; Wang, Wei-dong; Dong, Wei
2016-01-01
A lower limb assistive exoskeleton is designed to help operators walk or carry payloads. The exoskeleton is required to shadow human motion intent accurately and compliantly to prevent incoordination. If the user's intention is estimated accurately, a precise position control strategy will improve collaboration between the user and the exoskeleton. In this paper, a hybrid position control scheme, combining sliding mode control (SMC) with a cerebellar model articulation controller (CMAC) neural network, is proposed to control the exoskeleton to react appropriately to human motion intent. A genetic algorithm (GA) is utilized to determine the optimal sliding surface and the sliding control law to improve performance of SMC. The proposed control strategy (SMC_GA_CMAC) is compared with three other types of approaches, that is, conventional SMC without optimization, optimal SMC with GA (SMC_GA), and SMC with CMAC compensation (SMC_CMAC), all of which are employed to track the desired joint angular position which is deduced from Clinical Gait Analysis (CGA) data. Position tracking performance is investigated with cosimulation using ADAMS and MATLAB/SIMULINK in two cases, of which the first case is without disturbances while the second case is with a bounded disturbance. The cosimulation results show the effectiveness of the proposed control strategy which can be employed in similar exoskeleton systems. PMID:27069353
Long, Yi; Du, Zhi-Jiang; Wang, Wei-Dong; Dong, Wei
2016-01-01
A lower limb assistive exoskeleton is designed to help operators walk or carry payloads. The exoskeleton is required to shadow human motion intent accurately and compliantly to prevent incoordination. If the user's intention is estimated accurately, a precise position control strategy will improve collaboration between the user and the exoskeleton. In this paper, a hybrid position control scheme, combining sliding mode control (SMC) with a cerebellar model articulation controller (CMAC) neural network, is proposed to control the exoskeleton to react appropriately to human motion intent. A genetic algorithm (GA) is utilized to determine the optimal sliding surface and the sliding control law to improve performance of SMC. The proposed control strategy (SMC_GA_CMAC) is compared with three other types of approaches, that is, conventional SMC without optimization, optimal SMC with GA (SMC_GA), and SMC with CMAC compensation (SMC_CMAC), all of which are employed to track the desired joint angular position which is deduced from Clinical Gait Analysis (CGA) data. Position tracking performance is investigated with cosimulation using ADAMS and MATLAB/SIMULINK in two cases, of which the first case is without disturbances while the second case is with a bounded disturbance. The cosimulation results show the effectiveness of the proposed control strategy which can be employed in similar exoskeleton systems.
NASA Technical Reports Server (NTRS)
Thompson, David E.; Rajkumar, T.; Clancy, Daniel (Technical Monitor)
2002-01-01
The San Francisco Bay Delta is a large hydrodynamic complex that incorporates the Sacramento and San Joaquin Estuaries, the Burman Marsh, and the San Francisco Bay proper. Competition exists for the use of this extensive water system both from the fisheries industry, the agricultural industry, and from the marine and estuarine animal species within the Delta. As tidal fluctuations occur, more saline water pushes upstream allowing fish to migrate beyond the Burman Marsh for breeding and habitat occupation. However, the agriculture industry does not want extensive salinity intrusion to impact water quality for human and plant consumption. The balance is regulated by pumping stations located alone the estuaries and reservoirs whereby flushing of fresh water keeps the saline intrusion at bay. The pumping schedule is driven by data collected at various locations within the Bay Delta and by numerical models that predict the salinity intrusion as part of a larger model of the system. The Interagency Ecological Program (IEP) for the San Francisco Bay/Sacramento-San Joaquin Estuary collects, monitors, and archives the data, and the Department of Water Resources provides a numerical model simulation (DSM2) from which predictions are made that drive the pumping schedule. A problem with this procedure is that the numerical simulation takes roughly 16 hours to complete a C: prediction. We have created a neural net, optimized with a genetic algorithm, that takes as input the archived data from multiple stations and predicts stage, salinity, and flow at the Carquinez Straits (at the downstream end of the Burman Marsh). This model seems to be robust in its predictions and operates much faster than the current numerical DSM2 model. Because the system is strongly tidal driven, we used both Principal Component Analysis and Fast Fourier Transforms to discover dominant features within the IEP data. We then filtered out the dominant tidal forcing to discover non-primary tidal effects
RCQ-GA: RDF Chain Query Optimization Using Genetic Algorithms
NASA Astrophysics Data System (ADS)
Hogenboom, Alexander; Milea, Viorel; Frasincar, Flavius; Kaymak, Uzay
The application of Semantic Web technologies in an Electronic Commerce environment implies a need for good support tools. Fast query engines are needed for efficient querying of large amounts of data, usually represented using RDF. We focus on optimizing a special class of SPARQL queries, the so-called RDF chain queries. For this purpose, we devise a genetic algorithm called RCQ-GA that determines the order in which joins need to be performed for an efficient evaluation of RDF chain queries. The approach is benchmarked against a two-phase optimization algorithm, previously proposed in literature. The more complex a query is, the more RCQ-GA outperforms the benchmark in solution quality, execution time needed, and consistency of solution quality. When the algorithms are constrained by a time limit, the overall performance of RCQ-GA compared to the benchmark further improves.
Optimization Principles for the Neural Code
NASA Astrophysics Data System (ADS)
Deweese, Michael Robert
1995-01-01
Animals receive information from the world in the form of continuous functions of time. At a very early stage in processing, however, these continuous signals are converted into discrete sequences of identical "spikes". All information that the brain receives about the outside world is encoded in the arrival times of these spikes. The goal of this thesis is to determine if there is a universal principle at work in this neural code. We are motivated by several recent experiments on a wide range of sensory systems which share four main features: High information rates, moderate signal to noise ratio, efficient use of the spike train entropy to encode the signal, and the ability to extract nearly all the information encoded in the spike train with a linear response function triggered by the spikes. We propose that these features can be understood in terms of codes "designed" to maximize information flow. To test this idea, we use the fact that any point process encoding of an analog signal embedded in noise can be written in the language of a threshold crossing model to develop a systematic expansion for the transmitted information about the Poisson limit--the limit where there are no correlations between the spikes. All codes take the same simple form in the Poisson limit, and all of the seemingly unrelated features of the data arise naturally when we optimize a simple linear filtered threshold crossing model. We make a new prediction: Finding the optimum requires adaptation to the statistical structure of the signal and noise, not just to DC offsets. The only disagreement we find is that real neurons outperform our model in the task it was optimized for--they transmit much more information. We then place an upper bound on the amount of information available from the leading term in the Poisson expansion for any possible encoding, and find that real neurons do exceedingly well even by this standard. We conclude that several important features of the neural code can
Genetic Algorithm Based Neural Networks for Nonlinear Optimization
1994-09-28
This software develops a novel approach to nonlinear optimization using genetic algorithm based neural networks. To our best knowledge, this approach represents the first attempt at applying both neural network and genetic algorithm techniques to solve a nonlinear optimization problem. The approach constructs a neural network structure and an appropriately shaped energy surface whose minima correspond to optimal solutions of the problem. A genetic algorithm is employed to perform a parallel and powerful search ofmore » the energy surface.« less
Parameters optimization of laser brazing in crimping butt using Taguchi and BPNN-GA
NASA Astrophysics Data System (ADS)
Rong, Youmin; Zhang, Zhen; Zhang, Guojun; Yue, Chen; Gu, Yafei; Huang, Yu; Wang, Chunming; Shao, Xinyu
2015-04-01
The laser brazing (LB) is widely used in the automotive industry due to the advantages of high speed, small heat affected zone, high quality of welding seam, and low heat input. Welding parameters play a significant role in determining the bead geometry and hence quality of the weld joint. This paper addresses the optimization of the seam shape in LB process with welding crimping butt of 0.8 mm thickness using back propagation neural network (BPNN) and genetic algorithm (GA). A 3-factor, 5-level welding experiment is conducted by Taguchi L25 orthogonal array through the statistical design method. Then, the input parameters are considered here including welding speed, wire speed rate, and gap with 5 levels. The output results are efficient connection length of left side and right side, top width (WT) and bottom width (WB) of the weld bead. The experiment results are embed into the BPNN network to establish relationship between the input and output variables. The predicted results of the BPNN are fed to GA algorithm that optimizes the process parameters subjected to the objectives. Then, the effects of welding speed (WS), wire feed rate (WF), and gap (GAP) on the sum values of bead geometry is discussed. Eventually, the confirmation experiments are carried out to demonstrate the optimal values were effective and reliable. On the whole, the proposed hybrid method, BPNN-GA, can be used to guide the actual work and improve the efficiency and stability of LB process.
Simulation and optimization of 420-nm InGaN/GaN laser diodes
NASA Astrophysics Data System (ADS)
Piprek, Joachim; Sink, R. K.; Hansen, Monica A.; Bowers, John E.; DenBaars, Steven P.
2000-07-01
Using self-consistent laser simulation, we analyze the performance of nitride Fabry-Perot laser diodes grown on sapphire. The active region contains three 4 nm InGaN quantum wells. It is sandwiched between GaN separate confinement layers and superlattice AlGaN/GaN cladding layers. AlGaN is used as an electron barrier layer. Pulsed lasing is measured near 420 nm wavelength and at temperatures up to 120 degrees Celsius. Advanced laser simulation is applied to link microscopic device physics to measurable device performance. Our two-dimensional laser model considers carrier drift and diffusion including thermionic emission at hetero-boundaries. The local optical gain is calculated from the wurtzite band structure employing a non-Lorentzian line broadening model. All material parameters used in the model are evaluated based on recent literature values as well as our own experimental data. Simulation results are in good agreement with measurements. Multi-lateral mode lasing is calculated with a high order vertical mode. The carrier distribution among quantum wells is found to be strongly non-uniform leading to a parasitic (absorbing) quantum well. The influence of defect recombination, vertical carrier leakage and lateral current spreading is investigated. The reduction of such carrier losses is important to achieve lower threshold currents and less self-heating. Several device optimization options are proposed. Elimination of the parasitic quantum well is shown to substantially enhance the device performance.
Optimal neural computations require analog processors
Beiu, V.
1998-12-31
This paper discusses some of the limitations of hardware implementations of neural networks. The authors start by presenting neural structures and their biological inspirations, while mentioning the simplifications leading to artificial neural networks. Further, the focus will be on hardware imposed constraints. They will present recent results for three different alternatives of parallel implementations of neural networks: digital circuits, threshold gate circuits, and analog circuits. The area and the delay will be related to the neurons` fan-in and to the precision of their synaptic weights. The main conclusion is that hardware-efficient solutions require analog computations, and suggests the following two alternatives: (i) cope with the limitations imposed by silicon, by speeding up the computation of the elementary silicon neurons; (2) investigate solutions which would allow the use of the third dimension (e.g. using optical interconnections).
Neural networks for the optimization of crude oil blending.
Yu, Wen; Morales, América
2005-10-01
Crude oil blending is an important unit in petroleum refining industry. Many blend automation systems use real-time optimizer (RTO), which apply current process information to update the model and predict the optimal operating policy. The key unites of the conventional RTO are on-line analyzers. Sometimes oil fields cannot apply these analyzers. In this paper, we propose an off-line optimization technique to overcome the main drawback of RTO. We use the history data to approximate the output of the on-line analyzers, then the desired optimal inlet flow rates are calculated by the optimization technique. After this off-line optimization, the inlet flow rates are used for on-line control, for example PID control, which forces the flow rate to follow the desired inlet flow rates. Neural networks are applied to model the blending process from the history data. The new optimization is carried out via the neural model. The contributions of this paper are: (1) Stable learning for the discrete-time multilayer neural network is proposed. (2) Sensitivity analysis of the neural optimization is given. (3) Real data of a oil field is used to show effectiveness of the proposed method.
Optimization neural network for solving flow problems.
Perfetti, R
1995-01-01
This paper describes a neural network for solving flow problems, which are of interest in many areas of application as in fuel, hydro, and electric power scheduling. The neural network consist of two layers: a hidden layer and an output layer. The hidden units correspond to the nodes of the flow graph. The output units represent the branch variables. The network has a linear order of complexity, it is easily programmable, and it is suited for analog very large scale integration (VLSI) realization. The functionality of the proposed network is illustrated by a simulation example concerning the maximal flow problem. PMID:18263420
Optimal groundwater remediation using artificial neural networks and the genetic algorithm
Rogers, L.L.
1992-08-01
An innovative computational approach for the optimization of groundwater remediation is presented which uses artificial neural networks (ANNs) and the genetic algorithm (GA). In this approach, the ANN is trained to predict an aspect of the outcome of a flow and transport simulation. Then the GA searches through realizations or patterns of pumping and uses the trained network to predict the outcome of the realizations. This approach has advantages of parallel processing of the groundwater simulations and the ability to ``recycle`` or reuse the base of knowledge formed by these simulations. These advantages offer reduction of computational burden of the groundwater simulations relative to a more conventional approach which uses nonlinear programming (NLP) with a quasi-newtonian search. Also the modular nature of this approach facilitates substitution of different groundwater simulation models.
Genetic Algorithm (GA)-Based Inclinometer Layout Optimization
Liang, Weijie; Zhang, Ping; Chen, Xianping; Cai, Miao; Yang, Daoguo
2015-01-01
This paper presents numerical simulation results of an airflow inclinometer with sensitivity studies and thermal optimization of the printed circuit board (PCB) layout for an airflow inclinometer based on a genetic algorithm (GA). Due to the working principle of the gas sensor, the changes of the ambient temperature may cause dramatic voltage drifts of sensors. Therefore, eliminating the influence of the external environment for the airflow is essential for the performance and reliability of an airflow inclinometer. In this paper, the mechanism of an airflow inclinometer and the influence of different ambient temperatures on the sensitivity of the inclinometer will be examined by the ANSYS-FLOTRAN CFD program. The results show that with changes of the ambient temperature on the sensing element, the sensitivity of the airflow inclinometer is inversely proportional to the ambient temperature and decreases when the ambient temperature increases. GA is used to optimize the PCB thermal layout of the inclinometer. The finite-element simulation method (ANSYS) is introduced to simulate and verify the results of our optimal thermal layout, and the results indicate that the optimal PCB layout greatly improves (by more than 50%) the sensitivity of the inclinometer. The study may be useful in the design of PCB layouts that are related to sensitivity improvement of gas sensors. PMID:25897500
Sivapathasekaran, C; Mukherjee, Soumen; Ray, Arja; Gupta, Ashish; Sen, Ramkrishna
2010-04-01
A nonlinear model describing the relationship between the biosurfactant concentration as a process output and the critical medium components as the independent variables was developed by artificial neural network modeling. The model was optimized for the maximum biosurfactant production by using genetic algorithm. Based on a single-factor-at-a-time optimization strategy, the critical medium components were found to be glucose, urea, SrCl(2) and MgSO(4). The experimental results obtained from a statistical experimental design were used for the modeling and optimization by linking an artificial neural network (ANN) model with genetic algorithm (GA) in MATLAB. Using the optimized concentration of critical elements, the biosurfactant yield showed close agreement with the model prediction. An enhancement in biosurfactant production by approximately 70% was achieved by this optimization procedure. PMID:19914826
On limited fan-in optimal neural networks
Beiu, V.; Makaruk, H.E.; Draghici, S.
1998-03-01
Because VLSI implementations do not cope well with highly interconnected nets the area of a chip growing as the cube of the fan-in--this paper analyses the influence of limited fan in on the size and VLSI optimality of such nets. Two different approaches will show that VLSI- and size-optimal discrete neural networks can be obtained for small (i.e. lower than linear) fan-in values. They have applications to hardware implementations of neural networks. The first approach is based on implementing a certain sub class of Boolean functions, IF{sub n,m} functions. The authors will show that this class of functions can be implemented in VLSI optimal (i.e., minimizing AT{sup 2}) neural networks of small constant fan ins. The second approach is based on implementing Boolean functions for which the classical Shannon`s decomposition can be used. Such a solution has already been used to prove bounds on neural networks with fan-ins limited to 2. They generalize the result presented there to arbitrary fan-in, and prove that the size is minimized by small fan in values, while relative minimum size solutions can be obtained for fan-ins strictly lower than linear. Finally, a size-optimal neural network having small constant fan-ins will be suggested for IF{sub n,m} functions.
Applying neural networks to optimize instrumentation performance
Start, S.E.; Peters, G.G.
1995-06-01
Well calibrated instrumentation is essential in providing meaningful information about the status of a plant. Signals from plant instrumentation frequently have inherent non-linearities, may be affected by environmental conditions and can therefore cause calibration difficulties for the people who maintain them. Two neural network approaches are described in this paper for improving the accuracy of a non-linear, temperature sensitive level probe ised in Expermental Breeder Reactor II (EBR-II) that was difficult to calibrate.
Adaptive Optimization of Aircraft Engine Performance Using Neural Networks
NASA Technical Reports Server (NTRS)
Simon, Donald L.; Long, Theresa W.
1995-01-01
Preliminary results are presented on the development of an adaptive neural network based control algorithm to enhance aircraft engine performance. This work builds upon a previous National Aeronautics and Space Administration (NASA) effort known as Performance Seeking Control (PSC). PSC is an adaptive control algorithm which contains a model of the aircraft's propulsion system which is updated on-line to match the operation of the aircraft's actual propulsion system. Information from the on-line model is used to adapt the control system during flight to allow optimal operation of the aircraft's propulsion system (inlet, engine, and nozzle) to improve aircraft engine performance without compromising reliability or operability. Performance Seeking Control has been shown to yield reductions in fuel flow, increases in thrust, and reductions in engine fan turbine inlet temperature. The neural network based adaptive control, like PSC, will contain a model of the propulsion system which will be used to calculate optimal control commands on-line. Hopes are that it will be able to provide some additional benefits above and beyond those of PSC. The PSC algorithm is computationally intensive, it is valid only at near steady-state flight conditions, and it has no way to adapt or learn on-line. These issues are being addressed in the development of the optimal neural controller. Specialized neural network processing hardware is being developed to run the software, the algorithm will be valid at steady-state and transient conditions, and will take advantage of the on-line learning capability of neural networks. Future plans include testing the neural network software and hardware prototype against an aircraft engine simulation. In this paper, the proposed neural network software and hardware is described and preliminary neural network training results are presented.
NASA Astrophysics Data System (ADS)
Jokar, Ali; Godarzi, Ali Abbasi; Saber, Mohammad; Shafii, Mohammad Behshad
2016-11-01
In this paper, a novel approach has been presented to simulate and optimize the pulsating heat pipes (PHPs). The used pulsating heat pipe setup was designed and constructed for this study. Due to the lack of a general mathematical model for exact analysis of the PHPs, a method has been applied for simulation and optimization using the natural algorithms. In this way, the simulator consists of a kind of multilayer perceptron neural network, which is trained by experimental results obtained from our PHP setup. The results show that the complex behavior of PHPs can be successfully described by the non-linear structure of this simulator. The input variables of the neural network are input heat flux to evaporator (q″), filling ratio (FR) and inclined angle (IA) and its output is thermal resistance of PHP. Finally, based upon the simulation results and considering the heat pipe's operating constraints, the optimum operating point of the system is obtained by using genetic algorithm (GA). The experimental results show that the optimum FR (38.25 %), input heat flux to evaporator (39.93 W) and IA (55°) that obtained from GA are acceptable.
NASA Astrophysics Data System (ADS)
Jokar, Ali; Godarzi, Ali Abbasi; Saber, Mohammad; Shafii, Mohammad Behshad
2016-01-01
In this paper, a novel approach has been presented to simulate and optimize the pulsating heat pipes (PHPs). The used pulsating heat pipe setup was designed and constructed for this study. Due to the lack of a general mathematical model for exact analysis of the PHPs, a method has been applied for simulation and optimization using the natural algorithms. In this way, the simulator consists of a kind of multilayer perceptron neural network, which is trained by experimental results obtained from our PHP setup. The results show that the complex behavior of PHPs can be successfully described by the non-linear structure of this simulator. The input variables of the neural network are input heat flux to evaporator (q″), filling ratio (FR) and inclined angle (IA) and its output is thermal resistance of PHP. Finally, based upon the simulation results and considering the heat pipe's operating constraints, the optimum operating point of the system is obtained by using genetic algorithm (GA). The experimental results show that the optimum FR (38.25 %), input heat flux to evaporator (39.93 W) and IA (55°) that obtained from GA are acceptable.
Moghri, Mehdi; Madic, Milos; Omidi, Mostafa; Farahnakian, Masoud
2014-01-01
During the past decade, polymer nanocomposites attracted considerable investment in research and development worldwide. One of the key factors that affect the quality of polymer nanocomposite products in machining is surface roughness. To obtain high quality products and reduce machining costs it is very important to determine the optimal machining conditions so as to achieve enhanced machining performance. The objective of this paper is to develop a predictive model using a combined design of experiments and artificial intelligence approach for optimization of surface roughness in milling of polyamide-6 (PA-6) nanocomposites. A surface roughness predictive model was developed in terms of milling parameters (spindle speed and feed rate) and nanoclay (NC) content using artificial neural network (ANN). As the present study deals with relatively small number of data obtained from full factorial design, application of genetic algorithm (GA) for ANN training is thought to be an appropriate approach for the purpose of developing accurate and robust ANN model. In the optimization phase, a GA is considered in conjunction with the explicit nonlinear function derived from the ANN to determine the optimal milling parameters for minimization of surface roughness for each PA-6 nanocomposite. PMID:24578636
Moghri, Mehdi; Omidi, Mostafa; Farahnakian, Masoud
2014-01-01
During the past decade, polymer nanocomposites attracted considerable investment in research and development worldwide. One of the key factors that affect the quality of polymer nanocomposite products in machining is surface roughness. To obtain high quality products and reduce machining costs it is very important to determine the optimal machining conditions so as to achieve enhanced machining performance. The objective of this paper is to develop a predictive model using a combined design of experiments and artificial intelligence approach for optimization of surface roughness in milling of polyamide-6 (PA-6) nanocomposites. A surface roughness predictive model was developed in terms of milling parameters (spindle speed and feed rate) and nanoclay (NC) content using artificial neural network (ANN). As the present study deals with relatively small number of data obtained from full factorial design, application of genetic algorithm (GA) for ANN training is thought to be an appropriate approach for the purpose of developing accurate and robust ANN model. In the optimization phase, a GA is considered in conjunction with the explicit nonlinear function derived from the ANN to determine the optimal milling parameters for minimization of surface roughness for each PA-6 nanocomposite. PMID:24578636
Tian, H; Liu, C; Gao, X D; Yao, W B
2013-03-01
Granulocyte colony-stimulating factor (G-CSF) is a cytokine widely used in cancer patients receiving high doses of chemotherapeutic drugs to prevent the chemotherapy-induced suppression of white blood cells. The production of recombinant G-CSF should be increased to meet the increasing market demand. This study aims to model and optimize the carbon source of auto-induction medium to enhance G-CSF production using artificial neural networks coupled with genetic algorithm. In this approach, artificial neural networks served as bioprocess modeling tools, and genetic algorithm (GA) was applied to optimize the established artificial neural network models. Two artificial neural network models were constructed: the back-propagation (BP) network and the radial basis function (RBF) network. The root mean square error, coefficient of determination, and standard error of prediction of the BP model were 0.0375, 0.959, and 8.49 %, respectively, whereas those of the RBF model were 0.0257, 0.980, and 5.82 %, respectively. These values indicated that the RBF model possessed higher fitness and prediction accuracy than the BP model. Under the optimized auto-induction medium, the predicted maximum G-CSF yield by the BP-GA approach was 71.66 %, whereas that by the RBF-GA approach was 75.17 %. These predicted values are in agreement with the experimental results, with 72.4 and 76.014 % for the BP-GA and RBF-GA models, respectively. These results suggest that RBF-GA is superior to BP-GA. The developed approach in this study may be helpful in modeling and optimizing other multivariable, non-linear, and time-variant bioprocesses.
NASA Astrophysics Data System (ADS)
Qin, Sitian; Fan, Dejun; Su, Peng; Liu, Qinghe
2014-04-01
In this paper, the optimization techniques for solving pseudoconvex optimization problems are investigated. A simplified recurrent neural network is proposed according to the optimization problem. We prove that the optimal solution of the optimization problem is just the equilibrium point of the neural network, and vice versa if the equilibrium point satisfies the linear constraints. The proposed neural network is proven to be globally stable in the sense of Lyapunov and convergent to an exact optimal solution of the optimization problem. A numerical simulation is given to illustrate the global convergence of the neural network. Applications in business and chemistry are given to demonstrate the effectiveness of the neural network.
The Neural Basis of Optimism and Pessimism
2013-01-01
Our survival and wellness require a balance between optimism and pessimism. Undue pessimism makes life miserable; however, excessive optimism can lead to dangerously risky behaviors. A review and synthesis of the literature on the neurophysiology subserving these two worldviews suggests that optimism and pessimism are differentially associated with the two cerebral hemispheres. High self-esteem, a cheerful attitude that tends to look at the positive aspects of a given situation, as well as an optimistic belief in a bright future are associated with physiological activity in the left-hemisphere (LH). In contrast, a gloomy viewpoint, an inclination to focus on the negative part and exaggerate its significance, low self-esteem as well as a pessimistic view on what the future holds are interlinked with neurophysiological processes in the right-hemisphere (RH). This hemispheric asymmetry in mediating optimistic and pessimistic outlooks is rooted in several biological and functional differences between the two hemispheres. The RH mediation of a watchful and inhibitive mode weaves a sense of insecurity that generates and supports pessimistic thought patterns. Conversely, the LH mediation of an active mode and the positive feedback it receives through its motor dexterity breed a sense of confidence in one's ability to manage life's challenges, and optimism about the future. PMID:24167413
Calibration of neural networks using genetic algorithms, with application to optimal path planning
NASA Technical Reports Server (NTRS)
Smith, Terence R.; Pitney, Gilbert A.; Greenwood, Daniel
1987-01-01
Genetic algorithms (GA) are used to search the synaptic weight space of artificial neural systems (ANS) for weight vectors that optimize some network performance function. GAs do not suffer from some of the architectural constraints involved with other techniques and it is straightforward to incorporate terms into the performance function concerning the metastructure of the ANS. Hence GAs offer a remarkably general approach to calibrating ANS. GAs are applied to the problem of calibrating an ANS that finds optimal paths over a given surface. This problem involves training an ANS on a relatively small set of paths and then examining whether the calibrated ANS is able to find good paths between arbitrary start and end points on the surface.
Optimization of neural networks using variable structure systems.
Mohseni, Seyed Alireza; Tan, Ai Hui
2012-12-01
This paper proposes a new mixed training algorithm consisting of error backpropagation (EBP) and variable structure systems (VSSs) to optimize parameter updating of neural networks. For the optimization of the number of neurons in the hidden layer, a new term based on the output of the hidden layer is added to the cost function as a penalty term to make optimal use of hidden units related to weights corresponding to each unit in the hidden layer. VSS is used to control the dynamic model of the training process, whereas EBP attempts to minimize the cost function. In addition to the analysis of the imposed dynamics of the EBP technique, the global stability of the mixed training methodology and constraints on the design parameters are considered. The advantages of the proposed technique are guaranteed convergence, improved robustness, and lower sensitivity to initial weights of the neural network.
An optimization methodology for neural network weights and architectures.
Ludermir, Teresa B; Yamazaki, Akio; Zanchettin, Cleber
2006-11-01
This paper introduces a methodology for neural network global optimization. The aim is the simultaneous optimization of multilayer perceptron (MLP) network weights and architectures, in order to generate topologies with few connections and high classification performance for any data sets. The approach combines the advantages of simulated annealing, tabu search and the backpropagation training algorithm in order to generate an automatic process for producing networks with high classification performance and low complexity. Experimental results obtained with four classification problems and one prediction problem has shown to be better than those obtained by the most commonly used optimization techniques.
Qiu, Mingyue; Song, Yu
2016-01-01
In the business sector, it has always been a difficult task to predict the exact daily price of the stock market index; hence, there is a great deal of research being conducted regarding the prediction of the direction of stock price index movement. Many factors such as political events, general economic conditions, and traders' expectations may have an influence on the stock market index. There are numerous research studies that use similar indicators to forecast the direction of the stock market index. In this study, we compare two basic types of input variables to predict the direction of the daily stock market index. The main contribution of this study is the ability to predict the direction of the next day's price of the Japanese stock market index by using an optimized artificial neural network (ANN) model. To improve the prediction accuracy of the trend of the stock market index in the future, we optimize the ANN model using genetic algorithms (GA). We demonstrate and verify the predictability of stock price direction by using the hybrid GA-ANN model and then compare the performance with prior studies. Empirical results show that the Type 2 input variables can generate a higher forecast accuracy and that it is possible to enhance the performance of the optimized ANN model by selecting input variables appropriately.
Qiu, Mingyue; Song, Yu
2016-01-01
In the business sector, it has always been a difficult task to predict the exact daily price of the stock market index; hence, there is a great deal of research being conducted regarding the prediction of the direction of stock price index movement. Many factors such as political events, general economic conditions, and traders’ expectations may have an influence on the stock market index. There are numerous research studies that use similar indicators to forecast the direction of the stock market index. In this study, we compare two basic types of input variables to predict the direction of the daily stock market index. The main contribution of this study is the ability to predict the direction of the next day’s price of the Japanese stock market index by using an optimized artificial neural network (ANN) model. To improve the prediction accuracy of the trend of the stock market index in the future, we optimize the ANN model using genetic algorithms (GA). We demonstrate and verify the predictability of stock price direction by using the hybrid GA-ANN model and then compare the performance with prior studies. Empirical results show that the Type 2 input variables can generate a higher forecast accuracy and that it is possible to enhance the performance of the optimized ANN model by selecting input variables appropriately. PMID:27196055
Concurrent subspace width optimization method for RBF neural network modeling.
Yao, Wen; Chen, Xiaoqian; Zhao, Yong; van Tooren, Michel
2012-02-01
Radial basis function neural networks (RBFNNs) are widely used in nonlinear function approximation. One of the challenges in RBFNN modeling is determining how to effectively optimize width parameters to improve approximation accuracy. To solve this problem, a width optimization method, concurrent subspace width optimization (CSWO), is proposed based on a decomposition and coordination strategy. This method decomposes the large-scale width optimization problem into several subspace optimization (SSO) problems, each of which has a single optimization variable and smaller training and validation data sets so as to greatly simplify optimization complexity. These SSOs can be solved concurrently, thus computational time can be effectively reduced. With top-level system coordination, the optimization of SSOs can converge to a consistent optimum, which is equivalent to the optimum of the original width optimization problem. The proposed method is tested with four mathematical examples and one practical engineering approximation problem. The results demonstrate the efficiency and robustness of CSWO in optimizing width parameters over the traditional width optimization methods.
Zhukov, A. E.; Asryan, L. V.; Semenova, E. S.; Zubov, F. I.; Kryzhanovskaya, N. V.; Maximov, M. V.
2015-07-15
Band offsets at the heterointerface are calculated for various combinations of InAlGaAs/AlGaAs heteropairs that can be synthesized on GaAs substrates in the layer-by-layer pseudomorphic growth mode. Patterns which make it possible to obtain an asymmetric barrier layer providing the almost obstruction-free transport of holes and the highest possible barrier height for electrons are found. The optimal compositions of both compounds (In{sup 0.232}Al{sup 0.594}Ga{sup 0.174}As/Al{sup 0.355}Ga{sup 0.645}As) at which the flux of electrons across the barrier is at a minimum are determined with consideration for the critical thickness of the indium-containing quaternary solid solution.
A Bi-Projection Neural Network for Solving Constrained Quadratic Optimization Problems.
Xia, Youshen; Wang, Jun
2016-02-01
In this paper, a bi-projection neural network for solving a class of constrained quadratic optimization problems is proposed. It is proved that the proposed neural network is globally stable in the sense of Lyapunov, and the output trajectory of the proposed neural network will converge globally to an optimal solution. Compared with existing projection neural networks (PNNs), the proposed neural network has a very small model size owing to its bi-projection structure. Furthermore, an application to data fusion shows that the proposed neural network is very effective. Numerical results demonstrate that the proposed neural network is much faster than the existing PNNs.
SEMICONDUCTOR DEVICES: Gate-structure optimization for high frequency power AlGaN/GaN HEMTs
NASA Astrophysics Data System (ADS)
Dongfang, Wang; Tingting, Yuan; Ke, Wei; Xiaojuan, Chen; Xinyu, Liu
2010-05-01
The influence of gate-head and gate-source-spacing on the performance of AlGaN/GaN HEMTs was studied. Suggestions are then made to improve the performance of high frequency power AlGaN/GaN HEMTs by optimizing the gate-structure. Reducing the field-plate length can effectively enhance gain, current gain cutoff frequency and maximum frequency of oscillation. By reducing the field-plate length, devices with 0.35 μm gate length have exhibited a current gain cutoff frequency of 30 GHz and a maximum frequency of oscillation of 80 GHz. The maximum frequency of oscillation can be further optimized either by increasing the gate-metal thickness, or by using a τ-shape gate (the gate where the gate-head tends to the source side). Reducing the gate-source spacing can enhance the maximum drain-current and breakdown voltage, which is beneficial in enhancing the maximum output power of AlGaN/GaN HEMTs.
Neural mechanism of optimal limb coordination in crustacean swimming.
Zhang, Calvin; Guy, Robert D; Mulloney, Brian; Zhang, Qinghai; Lewis, Timothy J
2014-09-23
A fundamental challenge in neuroscience is to understand how biologically salient motor behaviors emerge from properties of the underlying neural circuits. Crayfish, krill, prawns, lobsters, and other long-tailed crustaceans swim by rhythmically moving limbs called swimmerets. Over the entire biological range of animal size and paddling frequency, movements of adjacent swimmerets maintain an approximate quarter-period phase difference with the more posterior limbs leading the cycle. We use a computational fluid dynamics model to show that this frequency-invariant stroke pattern is the most effective and mechanically efficient paddling rhythm across the full range of biologically relevant Reynolds numbers in crustacean swimming. We then show that the organization of the neural circuit underlying swimmeret coordination provides a robust mechanism for generating this stroke pattern. Specifically, the wave-like limb coordination emerges robustly from a combination of the half-center structure of the local central pattern generating circuits (CPGs) that drive the movements of each limb, the asymmetric network topology of the connections between local CPGs, and the phase response properties of the local CPGs, which we measure experimentally. Thus, the crustacean swimmeret system serves as a concrete example in which the architecture of a neural circuit leads to optimal behavior in a robust manner. Furthermore, we consider all possible connection topologies between local CPGs and show that the natural connectivity pattern generates the biomechanically optimal stroke pattern most robustly. Given the high metabolic cost of crustacean swimming, our results suggest that natural selection has pushed the swimmeret neural circuit toward a connection topology that produces optimal behavior.
Neural mechanism of optimal limb coordination in crustacean swimming
Zhang, Calvin; Guy, Robert D.; Mulloney, Brian; Zhang, Qinghai; Lewis, Timothy J.
2014-01-01
A fundamental challenge in neuroscience is to understand how biologically salient motor behaviors emerge from properties of the underlying neural circuits. Crayfish, krill, prawns, lobsters, and other long-tailed crustaceans swim by rhythmically moving limbs called swimmerets. Over the entire biological range of animal size and paddling frequency, movements of adjacent swimmerets maintain an approximate quarter-period phase difference with the more posterior limbs leading the cycle. We use a computational fluid dynamics model to show that this frequency-invariant stroke pattern is the most effective and mechanically efficient paddling rhythm across the full range of biologically relevant Reynolds numbers in crustacean swimming. We then show that the organization of the neural circuit underlying swimmeret coordination provides a robust mechanism for generating this stroke pattern. Specifically, the wave-like limb coordination emerges robustly from a combination of the half-center structure of the local central pattern generating circuits (CPGs) that drive the movements of each limb, the asymmetric network topology of the connections between local CPGs, and the phase response properties of the local CPGs, which we measure experimentally. Thus, the crustacean swimmeret system serves as a concrete example in which the architecture of a neural circuit leads to optimal behavior in a robust manner. Furthermore, we consider all possible connection topologies between local CPGs and show that the natural connectivity pattern generates the biomechanically optimal stroke pattern most robustly. Given the high metabolic cost of crustacean swimming, our results suggest that natural selection has pushed the swimmeret neural circuit toward a connection topology that produces optimal behavior. PMID:25201976
The neural optimal control hierarchy for motor control
NASA Astrophysics Data System (ADS)
DeWolf, T.; Eliasmith, C.
2011-10-01
Our empirical, neuroscientific understanding of biological motor systems has been rapidly growing in recent years. However, this understanding has not been systematically mapped to a quantitative characterization of motor control based in control theory. Here, we attempt to bridge this gap by describing the neural optimal control hierarchy (NOCH), which can serve as a foundation for biologically plausible models of neural motor control. The NOCH has been constructed by taking recent control theoretic models of motor control, analyzing the required processes, generating neurally plausible equivalent calculations and mapping them on to the neural structures that have been empirically identified to form the anatomical basis of motor control. We demonstrate the utility of the NOCH by constructing a simple model based on the identified principles and testing it in two ways. First, we perturb specific anatomical elements of the model and compare the resulting motor behavior with clinical data in which the corresponding area of the brain has been damaged. We show that damaging the assigned functions of the basal ganglia and cerebellum can cause the movement deficiencies seen in patients with Huntington's disease and cerebellar lesions. Second, we demonstrate that single spiking neuron data from our model's motor cortical areas explain major features of single-cell responses recorded from the same primate areas. We suggest that together these results show how NOCH-based models can be used to unify a broad range of data relevant to biological motor control in a quantitative, control theoretic framework.
Neural suppression of irrelevant information underlies optimal working memory performance
Zanto, Theodore P.; Gazzaley, Adam
2009-01-01
Our ability to focus attention on task-relevant information and ignore distractions is reflected by differential enhancement and suppression of neural activity in sensory cortex (i.e., top-down modulation). Such selective, goal-directed modulation of activity may be intimately related to memory, such that the focus of attention biases the likelihood of successfully maintaining relevant information by limiting interference from irrelevant stimuli. Despite recent studies elucidating the mechanistic overlap between attention and memory, the relationship between top-down modulation of visual processing during working memory (WM) encoding and subsequent recognition performance has not yet been established. Here, we provide neurophysiological evidence in healthy, young adults that top-down modulation of early visual processing (< 200 ms from stimulus onset) is intimately related to subsequent WM performance, such that the likelihood of successfully remembering relevant information is associated with limiting interference from irrelevant stimuli. The consequences of a failure to ignore distractors on recognition performance was replicated for two types of feature-based memory, motion direction and color. Moreover, attention to irrelevant stimuli was reflected neurally during the WM maintenance period as an increased memory load. These results suggest that neural enhancement of relevant information is not the primary determinant of high-level performance, but rather, optimal WM performance is dependent on effectively filtering irrelevant information through neural suppression to prevent overloading a limited memory capacity. PMID:19279242
On the probabilistic optimization of spiking neural networks.
Schliebs, Stefan; Kasabov, Nikola; Defoin-Platel, Michaël
2010-12-01
The construction of a Spiking Neural Network (SNN), i.e. the choice of an appropriate topology and the configuration of its internal parameters, represents a great challenge for SNN based applications. Evolutionary Algorithms (EAs) offer an elegant solution for these challenges and methods capable of exploring both types of search spaces simultaneously appear to be the most promising ones. A variety of such heterogeneous optimization algorithms have emerged recently, in particular in the field of probabilistic optimization. In this paper, a literature review on heterogeneous optimization algorithms is presented and an example of probabilistic optimization of SNN is discussed in detail. The paper provides an experimental analysis of a novel Heterogeneous Multi-Model Estimation of Distribution Algorithm (hMM-EDA). First, practical guidelines for configuring the method are derived and then the performance of hMM-EDA is compared to state-of-the-art optimization algorithms. Results show hMM-EDA as a light-weight, fast and reliable optimization method that requires the configuration of only very few parameters. Its performance on a synthetic heterogeneous benchmark problem is highly competitive and suggests its suitability for the optimization of SNN.
Optimal groundwater remediation using artificial neural networks and the genetic algorithm
Rogers, L.L.
1992-01-01
An innovative computational approach for the optimization of groundwater remediation is presented which uses artificial neural networks (ANNs) and the genetic algorithm (GA). In this approach, the ANN is trained to predict an aspect of the outcome of a flow and transport simulation. Then the trained network searches through realizations or patterns of pumping selected by the GA, predicting the outcome. This approach has advantages of parallel processing of the groundwater simulations and the ability to [open quotes]recycle[close quotes] or reuse the base of knowledge formed by these simulations. These advantages offer reduction of computational burden of the groundwater simulations relative to a more conventional approach which uses nonlinear programming (NLP) with a quasi-newtonian search. Also the modular nature of this approach facilitates substitution of different groundwater simulation models. The ANN technology, inspired by neurobiological theories of massive interconnection and parallelism, has been applied to a variety of optimization problems. In the ANN groundwater management approach presented here, the behavior of complex groundwater scenarios with spatially-variable transport parameters and multiple contaminant plumes are simulated with 2-D flow and transport codes. An ANN is trained upon a set of examples developed from groundwater simulations. The input of the ANN characterizes the different realizations of pumping. The output characterizes the objectives and constraints of the optimization, such as whether regulatory goals have been met, value of cost functions or cleanup time, and mass of contaminant removal. The supervised learning algorithm of backpropagation is used to train the network. The conjugate gradient method and weight-elimination procedures are used to speed convergence and improve performance, respectively. Then a search is made through possible pumping realizations to find optimal realizations.
Optimal Control Problem of Feeding Adaptations of Daphnia and Neural Network Simulation
NASA Astrophysics Data System (ADS)
Kmet', Tibor; Kmet'ov, Mria
2010-09-01
A neural network based optimal control synthesis is presented for solving optimal control problems with control and state constraints and open final time. The optimal control problem is transcribed into nonlinear programming problem, which is implemented with adaptive critic neural network [9] and recurrent neural network for solving nonlinear proprojection equations [10]. The proposed simulation methods is illustrated by the optimal control problem of feeding adaptation of filter feeders of Daphnia. Results show that adaptive critic based systematic approach and neural network solving of nonlinear equations hold promise for obtaining the optimal control with control and state constraints and open final time.
NASA Astrophysics Data System (ADS)
Rybalchenko, D. V.; Mintairov, S. A.; Shvarts, M. Z.; Kalyuzhnyy, N. A.
2016-08-01
Metamorphic Ga0.76In0.24As heterostructures for PV converters of 1064 nm laser radiation have been grown by the MOCVD. Parameters of the GaInAs metamorphic buffer layer with a stepwise profile of In composition variation were calculated. Its epitaxial growth conditions have been optimized, which allowed improving collection of charge carriers from the n-GaInAs base region and obtaining the photo-response quantum yield of 83% at 1064 nm wavelength. It has been found that, due to discontinuity of valence bands at the In0.24Al0.76As- p/Ga0.76In0.24As-p heterointerface (window/emitter) a potential barrier for holes arises as a result of low carrier concentration in the wide-band-gap material. The use of InAlGaAs solid solution with Al concentration of < 40% has allowed raising the holes concentration in the wide-band-gap window, eliminating completely the potential barrier and reducing the device series resistance. Optimization of the PV converter metamorphic heterostructure has resulted in obtaining 1064 nm laser radiation conversion efficiency at the level of 38.5%.
Neural network learning of optimal Kalman prediction and control.
Linsker, Ralph
2008-11-01
Although there are many neural network (NN) algorithms for prediction and for control, and although methods for optimal estimation (including filtering and prediction) and for optimal control in linear systems were provided by Kalman in 1960 (with nonlinear extensions since then), there has been, to my knowledge, no NN algorithm that learns either Kalman prediction or Kalman control (apart from the special case of stationary control). Here we show how optimal Kalman prediction and control (KPC), as well as system identification, can be learned and executed by a recurrent neural network composed of linear-response nodes, using as input only a stream of noisy measurement data. The requirements of KPC appear to impose significant constraints on the allowed NN circuitry and signal flows. The NN architecture implied by these constraints bears certain resemblances to the local-circuit architecture of mammalian cerebral cortex. We discuss these resemblances, as well as caveats that limit our current ability to draw inferences for biological function. It has been suggested that the local cortical circuit (LCC) architecture may perform core functions (as yet unknown) that underlie sensory, motor, and other cortical processing. It is reasonable to conjecture that such functions may include prediction, the estimation or inference of missing or noisy sensory data, and the goal-driven generation of control signals. The resemblances found between the KPC NN architecture and that of the LCC are consistent with this conjecture.
Implementing size-optimal discrete neural networks require analog circuitry
Beiu, V.
1998-12-01
This paper starts by overviewing results dealing with the approximation capabilities of neural networks, as well as bounds on the size of threshold gate circuits. Based on a constructive solution for Kolmogorov`s superpositions the authors show that implementing Boolean functions can be done using neurons having an identity transfer function. Because in this case the size of the network is minimized, it follows that size-optimal solutions for implementing Boolean functions can be obtained using analog circuitry. Conclusions and several comments on the required precision are ending the paper.
Neural dynamic optimization for autonomous aerial vehicle trajectory design
NASA Astrophysics Data System (ADS)
Xu, Peng; Verma, Ajay; Mayer, Richard J.
2007-04-01
Online aerial vehicle trajectory design and reshaping are crucial for a class of autonomous aerial vehicles such as reusable launch vehicles in order to achieve flexibility in real-time flying operations. An aerial vehicle is modeled as a nonlinear multi-input-multi-output (MIMO) system. The inputs include the control parameters and current system states that include velocity and position coordinates of the vehicle. The outputs are the new system states. An ideal trajectory control design system generates a series of control commands to achieve a desired trajectory under various disturbances and vehicle model uncertainties including aerodynamic perturbations caused by geometric damage to the vehicle. Conventional approaches suffer from the nonlinearity of the MIMO system, and the high-dimensionality of the system state space. In this paper, we apply a Neural Dynamic Optimization (NDO) based approach to overcome these difficulties. The core of an NDO model is a multilayer perceptron (MLP) neural network, which generates the control parameters online. The inputs of the MLP are the time-variant states of the MIMO systems. The outputs of the MLP and the control parameters will be used by the MIMO to generate new system states. By such a formulation, an NDO model approximates the time-varying optimal feedback solution.
Optimization via intermittency with a self-organizing neural network.
Kwok, Terence; Smith, Kate A
2005-11-01
One of the major obstacles in using neural networks to solve combinatorial optimization problems is the convergence toward one of the many local minima instead of the global minima. In this letter, we propose a technique that enables a self-organizing neural network to escape from local minima by virtue of the intermittency phenomenon. It gives rise to novel search dynamics that allow the system to visit multiple global minima as meta-stable states. Numerical experiments performed suggest that the phenomenon is a combined effect of Kohonen-type competitive learning and the iterated softmax function operating near bifurcation. The resultant intermittent search exhibits fractal characteristics when the optimization performance is at its peak in the form of 1/f signals in the time evolution of the cost, as well as power law distributions in the meta-stable solution states. TheN-Queens problem is used as an example to illustrate the meta-stable convergence process that sequentially generates, in a single run, 92 solutions to the 8-Queens problem and 4024 solutions to the 17-Queens problem.
Optimization via intermittency with a self-organizing neural network.
Kwok, Terence; Smith, Kate A
2005-11-01
One of the major obstacles in using neural networks to solve combinatorial optimization problems is the convergence toward one of the many local minima instead of the global minima. In this letter, we propose a technique that enables a self-organizing neural network to escape from local minima by virtue of the intermittency phenomenon. It gives rise to novel search dynamics that allow the system to visit multiple global minima as meta-stable states. Numerical experiments performed suggest that the phenomenon is a combined effect of Kohonen-type competitive learning and the iterated softmax function operating near bifurcation. The resultant intermittent search exhibits fractal characteristics when the optimization performance is at its peak in the form of 1/f signals in the time evolution of the cost, as well as power law distributions in the meta-stable solution states. TheN-Queens problem is used as an example to illustrate the meta-stable convergence process that sequentially generates, in a single run, 92 solutions to the 8-Queens problem and 4024 solutions to the 17-Queens problem. PMID:16156935
Kamel, Mohamed S; Xia, Youshen
2009-03-01
Constrained optimization problems arise in a wide variety of scientific and engineering applications. Since several single recurrent neural networks when applied to solve constrained optimization problems for real-time engineering applications have shown some limitations, cooperative recurrent neural network approaches have been developed to overcome drawbacks of these single recurrent neural networks. This paper surveys in details work on cooperative recurrent neural networks for solving constrained optimization problems and their engineering applications, and points out their standing models from viewpoint of both convergence to the optimal solution and model complexity. We provide examples and comparisons to shown advantages of these models in the given applications.
2008-01-01
Constrained optimization problems arise in a wide variety of scientific and engineering applications. Since several single recurrent neural networks when applied to solve constrained optimization problems for real-time engineering applications have shown some limitations, cooperative recurrent neural network approaches have been developed to overcome drawbacks of these single recurrent neural networks. This paper surveys in details work on cooperative recurrent neural networks for solving constrained optimization problems and their engineering applications, and points out their standing models from viewpoint of both convergence to the optimal solution and model complexity. We provide examples and comparisons to shown advantages of these models in the given applications. PMID:19003467
Feed Forward Neural Network and Optimal Control Problem with Control and State Constraints
Kmet', Tibor; Kmet'ova, Maria
2009-09-09
A feed forward neural network based optimal control synthesis is presented for solving optimal control problems with control and state constraints. The paper extends adaptive critic neural network architecture proposed by [5] to the optimal control problems with control and state constraints. The optimal control problem is transcribed into a nonlinear programming problem which is implemented with adaptive critic neural network. The proposed simulation method is illustrated by the optimal control problem of nitrogen transformation cycle model. Results show that adaptive critic based systematic approach holds promise for obtaining the optimal control with control and state constraints.
Tool Steel Heat Treatment Optimization Using Neural Network Modeling
NASA Astrophysics Data System (ADS)
Podgornik, Bojan; Belič, Igor; Leskovšek, Vojteh; Godec, Matjaz
2016-08-01
Optimization of tool steel properties and corresponding heat treatment is mainly based on trial and error approach, which requires tremendous experimental work and resources. Therefore, there is a huge need for tools allowing prediction of mechanical properties of tool steels as a function of composition and heat treatment process variables. The aim of the present work was to explore the potential and possibilities of artificial neural network-based modeling to select and optimize vacuum heat treatment conditions depending on the hot work tool steel composition and required properties. In the current case training of the feedforward neural network with error backpropagation training scheme and four layers of neurons (8-20-20-2) scheme was based on the experimentally obtained tempering diagrams for ten different hot work tool steel compositions and at least two austenitizing temperatures. Results show that this type of modeling can be successfully used for detailed and multifunctional analysis of different influential parameters as well as to optimize heat treatment process of hot work tool steels depending on the composition. In terms of composition, V was found as the most beneficial alloying element increasing hardness and fracture toughness of hot work tool steel; Si, Mn, and Cr increase hardness but lead to reduced fracture toughness, while Mo has the opposite effect. Optimum concentration providing high KIc/HRC ratios would include 0.75 pct Si, 0.4 pct Mn, 5.1 pct Cr, 1.5 pct Mo, and 0.5 pct V, with the optimum heat treatment performed at lower austenitizing and intermediate tempering temperatures.
Tool Steel Heat Treatment Optimization Using Neural Network Modeling
NASA Astrophysics Data System (ADS)
Podgornik, Bojan; Belič, Igor; Leskovšek, Vojteh; Godec, Matjaz
2016-11-01
Optimization of tool steel properties and corresponding heat treatment is mainly based on trial and error approach, which requires tremendous experimental work and resources. Therefore, there is a huge need for tools allowing prediction of mechanical properties of tool steels as a function of composition and heat treatment process variables. The aim of the present work was to explore the potential and possibilities of artificial neural network-based modeling to select and optimize vacuum heat treatment conditions depending on the hot work tool steel composition and required properties. In the current case training of the feedforward neural network with error backpropagation training scheme and four layers of neurons (8-20-20-2) scheme was based on the experimentally obtained tempering diagrams for ten different hot work tool steel compositions and at least two austenitizing temperatures. Results show that this type of modeling can be successfully used for detailed and multifunctional analysis of different influential parameters as well as to optimize heat treatment process of hot work tool steels depending on the composition. In terms of composition, V was found as the most beneficial alloying element increasing hardness and fracture toughness of hot work tool steel; Si, Mn, and Cr increase hardness but lead to reduced fracture toughness, while Mo has the opposite effect. Optimum concentration providing high KIc/HRC ratios would include 0.75 pct Si, 0.4 pct Mn, 5.1 pct Cr, 1.5 pct Mo, and 0.5 pct V, with the optimum heat treatment performed at lower austenitizing and intermediate tempering temperatures.
A Method to Optimize Transport Properties of AlGaN/GaN on Silicon
NASA Astrophysics Data System (ADS)
Daniel, J. D.; Elhamri, S.; Berney, R.; Ahoujja, M.; Mitchel, W. C.; Roberts, J. C.; Rajagopal, P.; Cook, J. W., Jr.; Piner, E. L.; Linthicum, K. J.
2007-10-01
We report on a study to investigate the impact of a thin AlN interlayer on the transport properties of AlGaN/GaN heterostructures grown by MOCVD on silicon substrates. Hall and Shubnikov-de Haas (SdH) measurements were used to compare the transport parameters of the conventional, AlGaN/GaN, structure to those of an AlGaN/AlN/GaN. The results clearly indicate that the interlayer leads to an enhancement of both the mobility and the carrier density. At 300 K, the carrier density and mobility for the conventional structure were roughly 8.57x10^12 cm-2 and 1523 cm^2/Vs, respectively. For the structure containing the AlN interlayer these numbers were 10.03 x 10^12 cm-2 and 1937 cm^2/Vs respectively. While the carrier density remained relatively unchanged down to 10 K, the mobility for the modified structure increased substantially. Shubnikov-de Haas measurements confirmed the presence of a high quality 2DEG in both structures. However, the amplitudes of the SdH oscillations in the conventional structure were higher.
Liu, Qingshan; Dang, Chuangyin; Huang, Tingwen
2013-02-01
This paper presents a decision-making model described by a recurrent neural network for dynamic portfolio optimization. The portfolio-optimization problem is first converted into a constrained fractional programming problem. Since the objective function in the programming problem is not convex, the traditional optimization techniques are no longer applicable for solving this problem. Fortunately, the objective function in the fractional programming is pseudoconvex on the feasible region. It leads to a one-layer recurrent neural network modeled by means of a discontinuous dynamic system. To ensure the optimal solutions for portfolio optimization, the convergence of the proposed neural network is analyzed and proved. In fact, the neural network guarantees to get the optimal solutions for portfolio-investment advice if some mild conditions are satisfied. A numerical example with simulation results substantiates the effectiveness and illustrates the characteristics of the proposed neural network.
Designing Artificial Neural Networks Using Particle Swarm Optimization Algorithms.
Garro, Beatriz A; Vázquez, Roberto A
2015-01-01
Artificial Neural Network (ANN) design is a complex task because its performance depends on the architecture, the selected transfer function, and the learning algorithm used to train the set of synaptic weights. In this paper we present a methodology that automatically designs an ANN using particle swarm optimization algorithms such as Basic Particle Swarm Optimization (PSO), Second Generation of Particle Swarm Optimization (SGPSO), and a New Model of PSO called NMPSO. The aim of these algorithms is to evolve, at the same time, the three principal components of an ANN: the set of synaptic weights, the connections or architecture, and the transfer functions for each neuron. Eight different fitness functions were proposed to evaluate the fitness of each solution and find the best design. These functions are based on the mean square error (MSE) and the classification error (CER) and implement a strategy to avoid overtraining and to reduce the number of connections in the ANN. In addition, the ANN designed with the proposed methodology is compared with those designed manually using the well-known Back-Propagation and Levenberg-Marquardt Learning Algorithms. Finally, the accuracy of the method is tested with different nonlinear pattern classification problems. PMID:26221132
Designing Artificial Neural Networks Using Particle Swarm Optimization Algorithms
Garro, Beatriz A.; Vázquez, Roberto A.
2015-01-01
Artificial Neural Network (ANN) design is a complex task because its performance depends on the architecture, the selected transfer function, and the learning algorithm used to train the set of synaptic weights. In this paper we present a methodology that automatically designs an ANN using particle swarm optimization algorithms such as Basic Particle Swarm Optimization (PSO), Second Generation of Particle Swarm Optimization (SGPSO), and a New Model of PSO called NMPSO. The aim of these algorithms is to evolve, at the same time, the three principal components of an ANN: the set of synaptic weights, the connections or architecture, and the transfer functions for each neuron. Eight different fitness functions were proposed to evaluate the fitness of each solution and find the best design. These functions are based on the mean square error (MSE) and the classification error (CER) and implement a strategy to avoid overtraining and to reduce the number of connections in the ANN. In addition, the ANN designed with the proposed methodology is compared with those designed manually using the well-known Back-Propagation and Levenberg-Marquardt Learning Algorithms. Finally, the accuracy of the method is tested with different nonlinear pattern classification problems. PMID:26221132
Statistical process control using optimized neural networks: a case study.
Addeh, Jalil; Ebrahimzadeh, Ata; Azarbad, Milad; Ranaee, Vahid
2014-09-01
The most common statistical process control (SPC) tools employed for monitoring process changes are control charts. A control chart demonstrates that the process has altered by generating an out-of-control signal. This study investigates the design of an accurate system for the control chart patterns (CCPs) recognition in two aspects. First, an efficient system is introduced that includes two main modules: feature extraction module and classifier module. In the feature extraction module, a proper set of shape features and statistical feature are proposed as the efficient characteristics of the patterns. In the classifier module, several neural networks, such as multilayer perceptron, probabilistic neural network and radial basis function are investigated. Based on an experimental study, the best classifier is chosen in order to recognize the CCPs. Second, a hybrid heuristic recognition system is introduced based on cuckoo optimization algorithm (COA) algorithm to improve the generalization performance of the classifier. The simulation results show that the proposed algorithm has high recognition accuracy. PMID:24210290
Hybrid Neural Network and Support Vector Machine Method for Optimization
NASA Technical Reports Server (NTRS)
Rai, Man Mohan (Inventor)
2007-01-01
System and method for optimization of a design associated with a response function, using a hybrid neural net and support vector machine (NN/SVM) analysis to minimize or maximize an objective function, optionally subject to one or more constraints. As a first example, the NN/SVM analysis is applied iteratively to design of an aerodynamic component, such as an airfoil shape, where the objective function measures deviation from a target pressure distribution on the perimeter of the aerodynamic component. As a second example, the NN/SVM analysis is applied to data classification of a sequence of data points in a multidimensional space. The NN/SVM analysis is also applied to data regression.
Artificial neural networks optimization method for radioactive source localization
Wacholder, E.; Elias, E.; Merlis, Y.
1995-05-01
An optimization artificial neural networks model is developed for solving the ill-posed inverse transport problem associated with localizing radioactive sources in a medium with known properties and dimensions. The model is based on the recurrent (or feedback) Hopfield network with fixed weights. The source distribution is determined based on the response of a limited number of external detectors of known spatial deployment in conjunction with a radiation transport model. The algorithm is tested and evaluated for a large number of simulated two-dimensional cases. Computations are carried out at different noise levels to account for statistical errors encountered in engineering applications. The sensitivity to noise is found to depend on the number of detectors and on their spatial deployment. A pretest empirical procedure is, therefore, suggested for determining an effective arrangement of detectors for a given problem.
HYBRID NEURAL NETWORK AND SUPPORT VECTOR MACHINE METHOD FOR OPTIMIZATION
NASA Technical Reports Server (NTRS)
Rai, Man Mohan (Inventor)
2005-01-01
System and method for optimization of a design associated with a response function, using a hybrid neural net and support vector machine (NN/SVM) analysis to minimize or maximize an objective function, optionally subject to one or more constraints. As a first example, the NN/SVM analysis is applied iteratively to design of an aerodynamic component, such as an airfoil shape, where the objective function measures deviation from a target pressure distribution on the perimeter of the aerodynamic component. As a second example, the NN/SVM analysis is applied to data classification of a sequence of data points in a multidimensional space. The NN/SVM analysis is also applied to data regression.
Woodward, Alexander; Froese, Tom; Ikegami, Takashi
2015-02-01
The state space of a conventional Hopfield network typically exhibits many different attractors of which only a small subset satisfies constraints between neurons in a globally optimal fashion. It has recently been demonstrated that combining Hebbian learning with occasional alterations of normal neural states avoids this problem by means of self-organized enlargement of the best basins of attraction. However, so far it is not clear to what extent this process of self-optimization is also operative in real brains. Here we demonstrate that it can be transferred to more biologically plausible neural networks by implementing a self-optimizing spiking neural network model. In addition, by using this spiking neural network to emulate a Hopfield network with Hebbian learning, we attempt to make a connection between rate-based and temporal coding based neural systems. Although further work is required to make this model more realistic, it already suggests that the efficacy of the self-optimizing process is independent from the simplifying assumptions of a conventional Hopfield network. We also discuss natural and cultural processes that could be responsible for occasional alteration of neural firing patterns in actual brains.
A one-layer recurrent neural network for constrained nonsmooth invex optimization.
Li, Guocheng; Yan, Zheng; Wang, Jun
2014-02-01
Invexity is an important notion in nonconvex optimization. In this paper, a one-layer recurrent neural network is proposed for solving constrained nonsmooth invex optimization problems, designed based on an exact penalty function method. It is proved herein that any state of the proposed neural network is globally convergent to the optimal solution set of constrained invex optimization problems, with a sufficiently large penalty parameter. In addition, any neural state is globally convergent to the unique optimal solution, provided that the objective function and constraint functions are pseudoconvex. Moreover, any neural state is globally convergent to the feasible region in finite time and stays there thereafter. The lower bounds of the penalty parameter and convergence time are also estimated. Two numerical examples are provided to illustrate the performances of the proposed neural network.
Neural network based optimal control of HVAC&R systems
NASA Astrophysics Data System (ADS)
Ning, Min
Heating, Ventilation, Air-Conditioning and Refrigeration (HVAC&R) systems have wide applications in providing a desired indoor environment for different types of buildings. It is well acknowledged that 30%-40% of the total energy generated is consumed by buildings and HVAC&R systems alone account for more than 50% of the building energy consumption. Low operational efficiency especially under partial load conditions and poor control are part of reasons for such high energy consumption. To improve energy efficiency, HVAC&R systems should be properly operated to maintain a comfortable and healthy indoor environment under dynamic ambient and indoor conditions with the least energy consumption. This research focuses on the optimal operation of HVAC&R systems. The optimization problem is formulated and solved to find the optimal set points for the chilled water supply temperature, discharge air temperature and AHU (air handling unit) fan static pressure such that the indoor environment is maintained with the least chiller and fan energy consumption. To achieve this objective, a dynamic system model is developed first to simulate the system behavior under different control schemes and operating conditions. The system model is modular in structure, which includes a water-cooled vapor compression chiller model and a two-zone VAV system model. A fuzzy-set based extended transformation approach is then applied to investigate the uncertainties of this model caused by uncertain parameters and the sensitivities of the control inputs with respect to the interested model outputs. A multi-layer feed forward neural network is constructed and trained in unsupervised mode to minimize the cost function which is comprised of overall energy cost and penalty cost when one or more constraints are violated. After training, the network is implemented as a supervisory controller to compute the optimal settings for the system. In order to implement the optimal set points predicted by the
Discrete-time neural inverse optimal control for nonlinear systems via passivation.
Ornelas-Tellez, Fernando; Sanchez, Edgar N; Loukianov, Alexander G
2012-08-01
This paper presents a discrete-time inverse optimal neural controller, which is constituted by combination of two techniques: 1) inverse optimal control to avoid solving the Hamilton-Jacobi-Bellman equation associated with nonlinear system optimal control and 2) on-line neural identification, using a recurrent neural network trained with an extended Kalman filter, in order to build a model of the assumed unknown nonlinear system. The inverse optimal controller is based on passivity theory. The applicability of the proposed approach is illustrated via simulations for an unstable nonlinear system and a planar robot. PMID:24807528
Sub-problem Optimization With Regression and Neural Network Approximators
NASA Technical Reports Server (NTRS)
Guptill, James D.; Hopkins, Dale A.; Patnaik, Surya N.
2003-01-01
Design optimization of large systems can be attempted through a sub-problem strategy. In this strategy, the original problem is divided into a number of smaller problems that are clustered together to obtain a sequence of sub-problems. Solution to the large problem is attempted iteratively through repeated solutions to the modest sub-problems. This strategy is applicable to structures and to multidisciplinary systems. For structures, clustering the substructures generates the sequence of sub-problems. For a multidisciplinary system, individual disciplines, accounting for coupling, can be considered as sub-problems. A sub-problem, if required, can be further broken down to accommodate sub-disciplines. The sub-problem strategy is being implemented into the NASA design optimization test bed, referred to as "CometBoards." Neural network and regression approximators are employed for reanalysis and sensitivity analysis calculations at the sub-problem level. The strategy has been implemented in sequential as well as parallel computational environments. This strategy, which attempts to alleviate algorithmic and reanalysis deficiencies, has the potential to become a powerful design tool. However, several issues have to be addressed before its full potential can be harnessed. This paper illustrates the strategy and addresses some issues.
A one-layer recurrent neural network for constrained nonconvex optimization.
Li, Guocheng; Yan, Zheng; Wang, Jun
2015-01-01
In this paper, a one-layer recurrent neural network is proposed for solving nonconvex optimization problems subject to general inequality constraints, designed based on an exact penalty function method. It is proved herein that any neuron state of the proposed neural network is convergent to the feasible region in finite time and stays there thereafter, provided that the penalty parameter is sufficiently large. The lower bounds of the penalty parameter and convergence time are also estimated. In addition, any neural state of the proposed neural network is convergent to its equilibrium point set which satisfies the Karush-Kuhn-Tucker conditions of the optimization problem. Moreover, the equilibrium point set is equivalent to the optimal solution to the nonconvex optimization problem if the objective function and constraints satisfy given conditions. Four numerical examples are provided to illustrate the performances of the proposed neural network.
Amaritsakul, Yongyut; Chao, Ching-Kong
2013-01-01
Short-segment instrumentation for spine fractures is threatened by relatively high failure rates. Failure of the spinal pedicle screws including breakage and loosening may jeopardize the fixation integrity and lead to treatment failure. Two important design objectives, bending strength and pullout strength, may conflict with each other and warrant a multiobjective optimization study. In the present study using the three-dimensional finite element (FE) analytical results based on an L25 orthogonal array, bending and pullout objective functions were developed by an artificial neural network (ANN) algorithm, and the trade-off solutions known as Pareto optima were explored by a genetic algorithm (GA). The results showed that the knee solutions of the Pareto fronts with both high bending and pullout strength ranged from 92% to 94% of their maxima, respectively. In mechanical validation, the results of mathematical analyses were closely related to those of experimental tests with a correlation coefficient of −0.91 for bending and 0.93 for pullout (P < 0.01 for both). The optimal design had significantly higher fatigue life (P < 0.01) and comparable pullout strength as compared with commercial screws. Multiobjective optimization study of spinal pedicle screws using the hybrid of ANN and GA could achieve an ideal with high bending and pullout performances simultaneously. PMID:23983810
A complex-valued neural dynamical optimization approach and its stability analysis.
Zhang, Songchuan; Xia, Youshen; Zheng, Weixing
2015-01-01
In this paper, we propose a complex-valued neural dynamical method for solving a complex-valued nonlinear convex programming problem. Theoretically, we prove that the proposed complex-valued neural dynamical approach is globally stable and convergent to the optimal solution. The proposed neural dynamical approach significantly generalizes the real-valued nonlinear Lagrange network completely in the complex domain. Compared with existing real-valued neural networks and numerical optimization methods for solving complex-valued quadratic convex programming problems, the proposed complex-valued neural dynamical approach can avoid redundant computation in a double real-valued space and thus has a low model complexity and storage capacity. Numerical simulations are presented to show the effectiveness of the proposed complex-valued neural dynamical approach.
Daszykowski, M; Walczak, B; Massart, D L
2002-01-01
In this paper, the performance of new clustering methods such as Neural Gas (NG) and Growing Neural Gas (GNG) is compared with the K-means method for real and simulated data sets. Moreover, a new algorithm called growing K-means, GK, is introduced as the alternative to Neural Gas and Growing Neural Gas. It has small input requirements and is conceptually very simple. The GK leads to nearly optimal values of the cost function, and, contrary to K-means, it is independent of the initial data set partition. The incremental property of GK additionally helps to estimate the number of "natural" clusters in data, i.e., the well-separated groups of objects in the data space. PMID:12444735
A non-penalty recurrent neural network for solving a class of constrained optimization problems.
Hosseini, Alireza
2016-01-01
In this paper, we explain a methodology to analyze convergence of some differential inclusion-based neural networks for solving nonsmooth optimization problems. For a general differential inclusion, we show that if its right hand-side set valued map satisfies some conditions, then solution trajectory of the differential inclusion converges to optimal solution set of its corresponding in optimization problem. Based on the obtained methodology, we introduce a new recurrent neural network for solving nonsmooth optimization problems. Objective function does not need to be convex on R(n) nor does the new neural network model require any penalty parameter. We compare our new method with some penalty-based and non-penalty based models. Moreover for differentiable cases, we implement circuit diagram of the new neural network.
NASA Astrophysics Data System (ADS)
Liu, Chao; Cai, Yuefei; Jiang, Huaxing; Lau, Kei May
2016-04-01
For the development of a metal-interconnection-free integration scheme for monolithic integration of InGaN/GaN light-emitting diodes (LEDs) and AlGaN/GaN high-electron-mobility transistors (HEMTs), a common buffer to achieve high brightness, low leakage current, and high breakdown in the integrated HEMT-LED device is essential. Different buffer structures have been investigated, and their impacts upon both the LED and HEMT parts of the HEMT-LED device have been analyzed. Results indicated that a GaN/AlN buffer structure is the most ideal to serve as a common buffer platform, offering both the excellent crystalline quality and superior buffer resistivity required by the HEMT-LED device. Growth of the AlN layer was particularly crucial for engineering the dislocation density, surface morphology, as well as resistivity of the buffer layer. Using the optimized GaN/AlN buffer structure, the LED part of the HEMT-LED device was improved, showing greatly enhanced light output power and suppressed reverse leakage current, while the breakdown characteristics of the HEMT part were also improved.
Watrous, R.; Towell, G.; Glassman, M.S.
1995-12-31
Results are reported from the application of tools for synthesizing, optimizing and analyzing neural networks to an ECG Patient Monitoring task. A neural network was synthesized from a rule-based classifier and optimized over a set of normal and abnormal heartbeats. The classification error rate on a separate and larger test set was reduced by a factor of 2. When the network was analyzed and reduced in size by a factor of 40%, the same level of performance was maintained.
Application of a neural network to simulate analysis in an optimization process
NASA Technical Reports Server (NTRS)
Rogers, James L.; Lamarsh, William J., II
1992-01-01
A new experimental software package called NETS/PROSSS aimed at reducing the computing time required to solve a complex design problem is described. The software combines a neural network for simulating the analysis program with an optimization program. The neural network is applied to approximate results of a finite element analysis program to quickly obtain a near-optimal solution. Results of the NETS/PROSSS optimization process can also be used as an initial design in a normal optimization process and make it possible to converge to an optimum solution with significantly fewer iterations.
A two-layer recurrent neural network for nonsmooth convex optimization problems.
Qin, Sitian; Xue, Xiaoping
2015-06-01
In this paper, a two-layer recurrent neural network is proposed to solve the nonsmooth convex optimization problem subject to convex inequality and linear equality constraints. Compared with existing neural network models, the proposed neural network has a low model complexity and avoids penalty parameters. It is proved that from any initial point, the state of the proposed neural network reaches the equality feasible region in finite time and stays there thereafter. Moreover, the state is unique if the initial point lies in the equality feasible region. The equilibrium point set of the proposed neural network is proved to be equivalent to the Karush-Kuhn-Tucker optimality set of the original optimization problem. It is further proved that the equilibrium point of the proposed neural network is stable in the sense of Lyapunov. Moreover, from any initial point, the state is proved to be convergent to an equilibrium point of the proposed neural network. Finally, as applications, the proposed neural network is used to solve nonlinear convex programming with linear constraints and L1 -norm minimization problems.
NASA Technical Reports Server (NTRS)
Decker, Arthur J.
2001-01-01
Artificial neural networks have been used for a number of years to process holography-generated characteristic patterns of vibrating structures. This technology depends critically on the selection and the conditioning of the training sets. A scaling operation called folding is discussed for conditioning training sets optimally for training feed-forward neural networks to process characteristic fringe patterns. Folding allows feed-forward nets to be trained easily to detect damage-induced vibration-displacement-distribution changes as small as 10 nm. A specific application to aerospace of neural-net processing of characteristic patterns is presented to motivate the conditioning and optimization effort.
Optimization of multilayer neural network parameters for speaker recognition
NASA Astrophysics Data System (ADS)
Tovarek, Jaromir; Partila, Pavol; Rozhon, Jan; Voznak, Miroslav; Skapa, Jan; Uhrin, Dominik; Chmelikova, Zdenka
2016-05-01
This article discusses the impact of multilayer neural network parameters for speaker identification. The main task of speaker identification is to find a specific person in the known set of speakers. It means that the voice of an unknown speaker (wanted person) belongs to a group of reference speakers from the voice database. One of the requests was to develop the text-independent system, which means to classify wanted person regardless of content and language. Multilayer neural network has been used for speaker identification in this research. Artificial neural network (ANN) needs to set parameters like activation function of neurons, steepness of activation functions, learning rate, the maximum number of iterations and a number of neurons in the hidden and output layers. ANN accuracy and validation time are directly influenced by the parameter settings. Different roles require different settings. Identification accuracy and ANN validation time were evaluated with the same input data but different parameter settings. The goal was to find parameters for the neural network with the highest precision and shortest validation time. Input data of neural networks are a Mel-frequency cepstral coefficients (MFCC). These parameters describe the properties of the vocal tract. Audio samples were recorded for all speakers in a laboratory environment. Training, testing and validation data set were split into 70, 15 and 15 %. The result of the research described in this article is different parameter setting for the multilayer neural network for four speakers.
Device and Design Optimization for AlGaN/GaN X-Band-Power-Amplifiers with High Efficiency
NASA Astrophysics Data System (ADS)
Kühn, Jutta; van Raay, Friedbert; Quay, Rüdiger; Kiefer, Rudolf; Mikulla, Michael; Seelmann-Eggebert, Matthias; Bronner, Wolfgang; Schlechtweg, Michael; Ambacher, Oliver; Thumm, Manfred
2010-03-01
The design, realization and characterization of dual-stage X-band high-power and highly-efficient monolithic microwave integrated circuit (MMIC) power amplifiers (PAs) with AlGaN/GaN high electronic mobility transistors (HEMTs) is presented. These high power amplifiers (HPAs) are based on a precise investigation of circuit-relevant HEMT behavior using two different field-plate variants and its effects on PA performance as well as optimization of HPA driver stage size which also has a deep impact on the entire HPA. Two broadband (3 GHz) MMICs with different field-plate variants and two narrowband (1 GHz) PAs with different driver- to final-stage gate-width ratio are realized with a maximum output power of 19-23 W, a maximum power-added efficiency (PAE) of ≥40%, and an associated power gain of 17 dB at X-band. Furthermore, two 1 mm test transistors of the same technology with the mentioned field-plate variants and a 1 mm test MMIC support VSWR-ratio tests of 6:1 and 4:1, respectively.
Wu, Jianfa; Peng, Dahao; Li, Zhuping; Zhao, Li; Ling, Huanzhang
2015-01-01
To effectively and accurately detect and classify network intrusion data, this paper introduces a general regression neural network (GRNN) based on the artificial immune algorithm with elitist strategies (AIAE). The elitist archive and elitist crossover were combined with the artificial immune algorithm (AIA) to produce the AIAE-GRNN algorithm, with the aim of improving its adaptivity and accuracy. In this paper, the mean square errors (MSEs) were considered the affinity function. The AIAE was used to optimize the smooth factors of the GRNN; then, the optimal smooth factor was solved and substituted into the trained GRNN. Thus, the intrusive data were classified. The paper selected a GRNN that was separately optimized using a genetic algorithm (GA), particle swarm optimization (PSO), and fuzzy C-mean clustering (FCM) to enable a comparison of these approaches. As shown in the results, the AIAE-GRNN achieves a higher classification accuracy than PSO-GRNN, but the running time of AIAE-GRNN is long, which was proved first. FCM and GA-GRNN were eliminated because of their deficiencies in terms of accuracy and convergence. To improve the running speed, the paper adopted principal component analysis (PCA) to reduce the dimensions of the intrusive data. With the reduction in dimensionality, the PCA-AIAE-GRNN decreases in accuracy less and has better convergence than the PCA-PSO-GRNN, and the running speed of the PCA-AIAE-GRNN was relatively improved. The experimental results show that the AIAE-GRNN has a higher robustness and accuracy than the other algorithms considered and can thus be used to classify the intrusive data. PMID:25807466
Optimization of Electric Power Leveling Systems by a Novel Cluster-Structured GA with Masking
NASA Astrophysics Data System (ADS)
Itoh, Jyunpei; Fujii, Toshinori; Funabiki, Shigeyuki
Power fluctuations of the rolling mill may cause the instability of electric power systems, and increase the cost of the electric power facility and electricity charges. Therefore, in order to compensate the power fluctuations, the development of the electric power-leveling systems (EPLS) is very important in the future electric power system. The EPLS with a SMES has been proposed as one of the countermeasures for the electric power quality improvement. However, the SMES is very expensive and it is difficult to decide the gains of the controller. It is essential in the practical use that the reduction of SMES capacity is realized. This paper proposes a new optimization method of the EPLS. The proposed algorithm is Cluster-Structured GA with Masking (CSGA). The optimization of the EPLS can be achieved by the proposed CSGA compared to the GA.
High-Lift Optimization Design Using Neural Networks on a Multi-Element Airfoil
NASA Technical Reports Server (NTRS)
Greenman, Roxana M.; Roth, Karlin R.; Smith, Charles A. (Technical Monitor)
1998-01-01
The high-lift performance of a multi-element airfoil was optimized by using neural-net predictions that were trained using a computational data set. The numerical data was generated using a two-dimensional, incompressible, Navier-Stokes algorithm with the Spalart-Allmaras turbulence model. Because it is difficult to predict maximum lift for high-lift systems, an empirically-based maximum lift criteria was used in this study to determine both the maximum lift and the angle at which it occurs. Multiple input, single output networks were trained using the NASA Ames variation of the Levenberg-Marquardt algorithm for each of the aerodynamic coefficients (lift, drag, and moment). The artificial neural networks were integrated with a gradient-based optimizer. Using independent numerical simulations and experimental data for this high-lift configuration, it was shown that this design process successfully optimized flap deflection, gap, overlap, and angle of attack to maximize lift. Once the neural networks were trained and integrated with the optimizer, minimal additional computer resources were required to perform optimization runs with different initial conditions and parameters. Applying the neural networks within the high-lift rigging optimization process reduced the amount of computational time and resources by 83% compared with traditional gradient-based optimization procedures for multiple optimization runs.
A recurrent neural network for solving a class of generalized convex optimization problems.
Hosseini, Alireza; Wang, Jun; Hosseini, S Mohammad
2013-08-01
In this paper, we propose a penalty-based recurrent neural network for solving a class of constrained optimization problems with generalized convex objective functions. The model has a simple structure described by using a differential inclusion. It is also applicable for any nonsmooth optimization problem with affine equality and convex inequality constraints, provided that the objective function is regular and pseudoconvex on feasible region of the problem. It is proven herein that the state vector of the proposed neural network globally converges to and stays thereafter in the feasible region in finite time, and converges to the optimal solution set of the problem.
An optimized efficient dual junction InGaN/CIGS solar cell: A numerical simulation
NASA Astrophysics Data System (ADS)
Farhadi, Bita; Naseri, Mosayeb
2016-08-01
The photovoltaic performance of an efficient double junction InGaN/CIGS solar cell including a CdS antireflector top cover layer is studied using Silvaco ATLAS software. In this study, to gain a desired structure, the different design parameters, including the CIGS various band gaps, the doping concentration and the thickness of CdS layer are optimized. The simulation indicates that under current matching condition, an optimum efficiency of 40.42% is achieved.
NASA Technical Reports Server (NTRS)
Leong, Harrison Monfook
1988-01-01
General formulae for mapping optimization problems into systems of ordinary differential equations associated with artificial neural networks are presented. A comparison is made to optimization using gradient-search methods. The performance measure is the settling time from an initial state to a target state. A simple analytical example illustrates a situation where dynamical systems representing artificial neural network methods would settle faster than those representing gradient-search. Settling time was investigated for a more complicated optimization problem using computer simulations. The problem was a simplified version of a problem in medical imaging: determining loci of cerebral activity from electromagnetic measurements at the scalp. The simulations showed that gradient based systems typically settled 50 to 100 times faster than systems based on current neural network optimization methods.
Optimal structural design of the midship of a VLCC based on the strategy integrating SVM and GA
NASA Astrophysics Data System (ADS)
Sun, Li; Wang, Deyu
2012-03-01
In this paper a hybrid process of modeling and optimization, which integrates a support vector machine (SVM) and genetic algorithm (GA), was introduced to reduce the high time cost in structural optimization of ships. SVM, which is rooted in statistical learning theory and an approximate implementation of the method of structural risk minimization, can provide a good generalization performance in metamodeling the input-output relationship of real problems and consequently cuts down on high time cost in the analysis of real problems, such as FEM analysis. The GA, as a powerful optimization technique, possesses remarkable advantages for the problems that can hardly be optimized with common gradient-based optimization methods, which makes it suitable for optimizing models built by SVM. Based on the SVM-GA strategy, optimization of structural scantlings in the midship of a very large crude carrier (VLCC) ship was carried out according to the direct strength assessment method in common structural rules (CSR), which eventually demonstrates the high efficiency of SVM-GA in optimizing the ship structural scantlings under heavy computational complexity. The time cost of this optimization with SVM-GA has been sharply reduced, many more loops have been processed within a small amount of time and the design has been improved remarkably.
Genetic learning in rule-based and neural systems
NASA Technical Reports Server (NTRS)
Smith, Robert E.
1993-01-01
The design of neural networks and fuzzy systems can involve complex, nonlinear, and ill-conditioned optimization problems. Often, traditional optimization schemes are inadequate or inapplicable for such tasks. Genetic Algorithms (GA's) are a class of optimization procedures whose mechanics are based on those of natural genetics. Mathematical arguments show how GAs bring substantial computational leverage to search problems, without requiring the mathematical characteristics often necessary for traditional optimization schemes (e.g., modality, continuity, availability of derivative information, etc.). GA's have proven effective in a variety of search tasks that arise in neural networks and fuzzy systems. This presentation begins by introducing the mechanism and theoretical underpinnings of GA's. GA's are then related to a class of rule-based machine learning systems called learning classifier systems (LCS's). An LCS implements a low-level production-system that uses a GA as its primary rule discovery mechanism. This presentation illustrates how, despite its rule-based framework, an LCS can be thought of as a competitive neural network. Neural network simulator code for an LCS is presented. In this context, the GA is doing more than optimizing and objective function. It is searching for an ecology of hidden nodes with limited connectivity. The GA attempts to evolve this ecology such that effective neural network performance results. The GA is particularly well adapted to this task, given its naturally-inspired basis. The LCS/neural network analogy extends itself to other, more traditional neural networks. Conclusions to the presentation discuss the implications of using GA's in ecological search problems that arise in neural and fuzzy systems.
ERIC Educational Resources Information Center
Nikelshpur, Dmitry O.
2014-01-01
Similar to mammalian brains, Artificial Neural Networks (ANN) are universal approximators, capable of yielding near-optimal solutions to a wide assortment of problems. ANNs are used in many fields including medicine, internet security, engineering, retail, robotics, warfare, intelligence control, and finance. "ANNs have a tendency to get…
High uniform growth of 4-inch GaN wafer via flow field optimization by HVPE
NASA Astrophysics Data System (ADS)
Cheng, Yutian; Liu, Peng; Wu, Jiejun; Xiang, Yong; Chen, Xinjuan; Ji, Cheng; Yu, Tongjun; Zhang, Guoyi
2016-07-01
The uniformity of flow field inner the reactor plays a crucial role for hydride vapor phase epitaxy (HVPE) crystal growth and its more important for large scale substrate. A new nozzle structure was designed by adding a push and dilution (PD) gas pipe in the center of gas channels for a 4-inch HVPE (PD-HVPE) system. Experimental results showed that the thickness inhomogeneity of 46 μm 4-inch GaN layer could reach ±1.8% by optimizing PD gas, greatly improved from ±14% grown with conventional nozzle. The simulations of the internal flow field were consistent with our experiment, and the enhancement in uniformity should be attributed to the redistribution of GaCl and NH3 upon the wafer induced by PD pipe. The full width at half maximum (FWHM) of X-ray diffraction rocking curves for the 4-inch GaN film were about 224 and 200 arcsec for (002) and (102) reflection. The dislocation density of as-grown GaN was about 6.4×107 cm-2.
NASA Astrophysics Data System (ADS)
Jang, Seon-Ho; Jo, Yong-Ryun; Lee, Young-Woong; Kim, Sei-Min; Kim, Bong-Joong; Bae, Jae-Hyun; An, Huei-Chun; Jang, Ja-Soon
2015-05-01
We report a highly transparent conducting electrode (TCE) scheme of MgxZn1-xO:Ga/Au/NiOx which was deposited on p-GaN by e-beam for GaN-based light emitting diodes (LEDs). The optical and electrical properties of the electrode were optimized by thermal annealing at 500°C for 1 minute in N2 + O2 (5:3) ambient. The light transmittance at the optimal condition increased up to 84-97% from the UV-A to yellow region. The specific contact resistance decreased to 4.3(±0.3) × 10-5 Ωcm2. The improved properties of the electrode were attributed to the directionally elongated crystalline nanostructures formed in the MgxZn1-xO:Ga layer which is compositionally uniform. Interestingly, the Au alloy nano-clusters created in the MgxZn1-xO:Ga layer during annealing at 500°C may also enhance the properties of the electrode by acting as a conducting bridge and a nano-sized mirror. Based on studies of the external quantum efficiency of blue LED devices, the proposed electrode scheme combined with an optimized annealing treatment suggests a potential alternative to ITO. [Figure not available: see fulltext.
Optimal design of systems that evolve over time using neural networks
NASA Astrophysics Data System (ADS)
Nolan, Michael K.
2007-04-01
Design optimization is challenging when the number of variables becomes large. One method of addressing this problem is to use pattern recognition to decrease the solution space in which the optimizer searches. Human "common sense" is used by designers to narrow the scope of search to a confined area defined by patterns conforming to likely solution candidates. However, computer-based optimization generally does not apply similar heuristics. In this paper, a system is presented that recognizes patterns and adjusts its search for optimal solutions based on these patterns. A design problem was selected that requires the optimization algorithm to assess designs that evolve over time. A small sensor network design is evolved into a larger sensor network design. Optimal design solutions for the small network do not necessarily lead to optimal solutions for the larger network. Systems that are well-positioned to evolve have characteristics that distinguish themselves from systems that are not well-positioned to evolve. In this study, a neural network was able to recognize a pattern whereby flexible sensor networks evolved more successfully than less flexible networks. The optimizing algorithm used this pattern to select candidate systems that showed promise for evolution. A genetic algorithm assisted by a neural network achieved better performance than an unassisted genetic algorithm did. This thesis advocates the merit of neural network use in multi-objective system design optimization and to lay a basis for future study.
InGaN/GaN multilayer quantum dots yellow-green light-emitting diode with optimized GaN barriers.
Lv, Wenbin; Wang, Lai; Wang, Jiaxing; Hao, Zhibiao; Luo, Yi
2012-11-07
InGaN/GaN multilayer quantum dot (QD) structure is a potential type of active regions for yellow-green light-emitting diodes (LEDs). The surface morphologies and crystalline quality of GaN barriers are critical to the uniformity of InGaN QD layers. While GaN barriers were grown in multi-QD layers, we used improved growth parameters by increasing the growth temperature and switching the carrier gas from N2 to H2 in the metal organic vapor phase epitaxy. As a result, a 10-layer InGaN/GaN QD LED is demonstrated successfully. The transmission electron microscopy image shows the uniform multilayer InGaN QDs clearly. As the injection current increases from 5 to 50 mA, the electroluminescence peak wavelength shifts from 574 to 537 nm.
InGaN/GaN multilayer quantum dots yellow-green light-emitting diode with optimized GaN barriers
2012-01-01
InGaN/GaN multilayer quantum dot (QD) structure is a potential type of active regions for yellow-green light-emitting diodes (LEDs). The surface morphologies and crystalline quality of GaN barriers are critical to the uniformity of InGaN QD layers. While GaN barriers were grown in multi-QD layers, we used improved growth parameters by increasing the growth temperature and switching the carrier gas from N2 to H2 in the metal organic vapor phase epitaxy. As a result, a 10-layer InGaN/GaN QD LED is demonstrated successfully. The transmission electron microscopy image shows the uniform multilayer InGaN QDs clearly. As the injection current increases from 5 to 50 mA, the electroluminescence peak wavelength shifts from 574 to 537 nm. PMID:23134721
NASA Technical Reports Server (NTRS)
Jules, Kenol; Lin, Paul P.
2002-01-01
This paper reviews some of the recent applications of artificial neural networks taken from various works performed by the authors over the last four years at the NASA Glenn Research Center. This paper focuses mainly on two areas. First, artificial neural networks application in design and optimization of aircraft/engine propulsion systems to shorten the overall design cycle. Out of that specific application, a generic design tool was developed, which can be used for most design optimization process. Second, artificial neural networks application in monitoring the microgravity quality onboard the International Space Station, using on-board accelerometers for data acquisition. These two different applications are reviewed in this paper to show the broad applicability of artificial intelligence in various disciplines. The intent of this paper is not to give in-depth details of these two applications, but to show the need to combine different artificial intelligence techniques or algorithms in order to design an optimized or versatile system.
Optimal Formation of Multirobot Systems Based on a Recurrent Neural Network.
Wang, Yunpeng; Cheng, Long; Hou, Zeng-Guang; Yu, Junzhi; Tan, Min
2016-02-01
The optimal formation problem of multirobot systems is solved by a recurrent neural network in this paper. The desired formation is described by the shape theory. This theory can generate a set of feasible formations that share the same relative relation among robots. An optimal formation means that finding one formation from the feasible formation set, which has the minimum distance to the initial formation of the multirobot system. Then, the formation problem is transformed into an optimization problem. In addition, the orientation, scale, and admissible range of the formation can also be considered as the constraints in the optimization problem. Furthermore, if all robots are identical, their positions in the system are exchangeable. Then, each robot does not necessarily move to one specific position in the formation. In this case, the optimal formation problem becomes a combinational optimization problem, whose optimal solution is very hard to obtain. Inspired by the penalty method, this combinational optimization problem can be approximately transformed into a convex optimization problem. Due to the involvement of the Euclidean norm in the distance, the objective function of these optimization problems are nonsmooth. To solve these nonsmooth optimization problems efficiently, a recurrent neural network approach is employed, owing to its parallel computation ability. Finally, some simulations and experiments are given to validate the effectiveness and efficiency of the proposed optimal formation approach.
Optimized biogas-fermentation by neural network control.
Holubar, P; Zani, L; Hager, M; Fröschl, W; Radak, Z; Braun, R
2003-01-01
In this work several feed-forward back-propagation neural networks (FFBP) were trained in order to model, and subsequently control, methane production in anaerobic digesters. To produce data for the training of the neural nets, four anaerobic continuous stirred tank reactors (CSTR) were operated in steady-state conditions at organic loading rates (Br) of about 2 kg x m(-3) x d(-1) chemical oxygen demand (COD), and disturbed by pulse-like increase of the organic loading rate. For the pulses additional carbon sources were added to the basic feed (surplus- and primary sludge) to simulate cofermentation and to increase the COD. Measured parameters were: gas composition, methane production rate, volatile fatty acid concentration, pH, redox potential, volatile suspended solids and COD of feed and effluent. A hierarchical system of neural nets was developed and embedded in a Decision Support System (DSS). A 3-3-1 FFBP simulated the pH with a regression coefficient of 0.82. A 9-3-3 FFBP simulated the volatile fatty acid concentration in the sludge with a regression coefficient of 0.86. And a 9-3-2 FFBP simulated the gas production and gas composition with a regression coefficient of 0.90 and 0.80 respectively. A lab-scale anaerobic CSTR controlled by this tool was able to maintain a methane concentration of about 60% at a rather high gas production rate of between 5 to 5.6 m3 x m(-3) x d(-1).
A new approach to self-organizing fuzzy polynomial neural networks guided by genetic optimization
NASA Astrophysics Data System (ADS)
Oh, Sung-Kwun; Pedrycz, Witold
2005-09-01
In this study, we introduce a new topology of Fuzzy Polynomial Neural Networks (FPNN) that is based on a genetically optimized multilayer perceptron with fuzzy polynomial neurons (FPNs) and discuss its comprehensive design methodology. The underlying methodology involves mechanisms of genetic optimization, especially genetic algorithms (GAs). Let us recall that the design of the “conventional” FPNNs uses an extended Group Method of Data Handling (GMDH) and exploits a fixed fuzzy inference type located at each FPN of the FPNN as well as considers a fixed number of input nodes at FPNs (or nodes) located in each layer. The proposed FPNN gives rise to a structurally optimized structure and comes with a substantial level of flexibility in comparison to the one we encounter in conventional FPNNs. The structural optimization is realized via GAs whereas in the case of the parametric optimization we proceed with a standard least square method based learning. Through the consecutive process of such structural and parametric optimization, an optimized and flexible fuzzy neural network is generated in a dynamic fashion. The performance of the proposed gFPNN is quantified through experimentation that exploits standard data already being used in fuzzy modeling. The results reveal superiority of the proposed networks over the existing fuzzy and neural models.
A neural network model of reliably optimized spike transmission.
Samura, Toshikazu; Ikegaya, Yuji; Sato, Yasuomi D
2015-06-01
We studied the detailed structure of a neuronal network model in which the spontaneous spike activity is correctly optimized to match the experimental data and discuss the reliability of the optimized spike transmission. Two stochastic properties of the spontaneous activity were calculated: the spike-count rate and synchrony size. The synchrony size, expected to be an important factor for optimization of spike transmission in the network, represents a percentage of observed coactive neurons within a time bin, whose probability approximately follows a power-law. We systematically investigated how these stochastic properties could matched to those calculated from the experimental data in terms of the log-normally distributed synaptic weights between excitatory and inhibitory neurons and synaptic background activity induced by the input current noise in the network model. To ensure reliably optimized spike transmission, the synchrony size as well as spike-count rate were simultaneously optimized. This required changeably balanced log-normal distributions of synaptic weights between excitatory and inhibitory neurons and appropriately amplified synaptic background activity. Our results suggested that the inhibitory neurons with a hub-like structure driven by intensive feedback from excitatory neurons were a key factor in the simultaneous optimization of the spike-count rate and synchrony size, regardless of different spiking types between excitatory and inhibitory neurons.
Benyamini, Miri; Zacksenhouse, Miriam
2015-01-01
Recent experiments with brain-machine-interfaces (BMIs) indicate that the extent of neural modulations increased abruptly upon starting to operate the interface, and especially after the monkey stopped moving its hand. In contrast, neural modulations that are correlated with the kinematics of the movement remained relatively unchanged. Here we demonstrate that similar changes are produced by simulated neurons that encode the relevant signals generated by an optimal feedback controller during simulated BMI experiments. The optimal feedback controller relies on state estimation that integrates both visual and proprioceptive feedback with prior estimations from an internal model. The processing required for optimal state estimation and control were conducted in the state-space, and neural recording was simulated by modeling two populations of neurons that encode either only the estimated state or also the control signal. Spike counts were generated as realizations of doubly stochastic Poisson processes with linear tuning curves. The model successfully reconstructs the main features of the kinematics and neural activity during regular reaching movements. Most importantly, the activity of the simulated neurons successfully reproduces the observed changes in neural modulations upon switching to brain control. Further theoretical analysis and simulations indicate that increasing the process noise during normal reaching movement results in similar changes in neural modulations. Thus, we conclude that the observed changes in neural modulations during BMI experiments can be attributed to increasing process noise associated with the imperfect BMI filter, and, more directly, to the resulting increase in the variance of the encoded signals associated with state estimation and the required control signal.
Artificial neural networks in evaluation and optimization of modified release solid dosage forms.
Ibrić, Svetlana; Djuriš, Jelena; Parojčić, Jelena; Djurić, Zorica
2012-10-18
Implementation of the Quality by Design (QbD) approach in pharmaceutical development has compelled researchers in the pharmaceutical industry to employ Design of Experiments (DoE) as a statistical tool, in product development. Among all DoE techniques, response surface methodology (RSM) is the one most frequently used. Progress of computer science has had an impact on pharmaceutical development as well. Simultaneous with the implementation of statistical methods, machine learning tools took an important place in drug formulation. Twenty years ago, the first papers describing application of artificial neural networks in optimization of modified release products appeared. Since then, a lot of work has been done towards implementation of new techniques, especially Artificial Neural Networks (ANN) in modeling of production, drug release and drug stability of modified release solid dosage forms. The aim of this paper is to review artificial neural networks in evaluation and optimization of modified release solid dosage forms.
Two-Dimensional High-Lift Aerodynamic Optimization Using Neural Networks
NASA Technical Reports Server (NTRS)
Greenman, Roxana M.
1998-01-01
The high-lift performance of a multi-element airfoil was optimized by using neural-net predictions that were trained using a computational data set. The numerical data was generated using a two-dimensional, incompressible, Navier-Stokes algorithm with the Spalart-Allmaras turbulence model. Because it is difficult to predict maximum lift for high-lift systems, an empirically-based maximum lift criteria was used in this study to determine both the maximum lift and the angle at which it occurs. The 'pressure difference rule,' which states that the maximum lift condition corresponds to a certain pressure difference between the peak suction pressure and the pressure at the trailing edge of the element, was applied and verified with experimental observations for this configuration. Multiple input, single output networks were trained using the NASA Ames variation of the Levenberg-Marquardt algorithm for each of the aerodynamic coefficients (lift, drag and moment). The artificial neural networks were integrated with a gradient-based optimizer. Using independent numerical simulations and experimental data for this high-lift configuration, it was shown that this design process successfully optimized flap deflection, gap, overlap, and angle of attack to maximize lift. Once the neural nets were trained and integrated with the optimizer, minimal additional computer resources were required to perform optimization runs with different initial conditions and parameters. Applying the neural networks within the high-lift rigging optimization process reduced the amount of computational time and resources by 44% compared with traditional gradient-based optimization procedures for multiple optimization runs.
Radosavljević, S.; Radovanović, J. Milanović, V.; Tomić, S.
2014-07-21
We have described a method for structural parameters optimization of GaN/AlGaN multiple quantum well based up-converter for silicon solar cells. It involves a systematic tuning of individual step quantum wells by use of the genetic algorithm for global optimization. In quantum well structures, the up-conversion process can be achieved by utilizing nonlinear optical effects based on intersubband transitions. Both single and double step quantum wells have been tested in order to maximize the second order susceptibility derived from the density matrix formalism. The results obtained for single step wells proved slightly better and have been further pursued to obtain a more complex design, optimized for conversion of an entire range of incident photon energies.
On the Current State of the Research in Neural Network and Optimization Methods
NASA Astrophysics Data System (ADS)
Onoda, Takashi; Someya, Hiroshi
This article surveys the history of the field of Neural Network research and presents a review of several techniques developed in the field. Attempts at statistical analysis of search dynamics of the optimization methods in Soft Computing and recent advances on implementation in parallel computers are briefly introduced.
A Subsonic Aircraft Design Optimization With Neural Network and Regression Approximators
NASA Technical Reports Server (NTRS)
Patnaik, Surya N.; Coroneos, Rula M.; Guptill, James D.; Hopkins, Dale A.; Haller, William J.
2004-01-01
The Flight-Optimization-System (FLOPS) code encountered difficulty in analyzing a subsonic aircraft. The limitation made the design optimization problematic. The deficiencies have been alleviated through use of neural network and regression approximations. The insight gained from using the approximators is discussed in this paper. The FLOPS code is reviewed. Analysis models are developed and validated for each approximator. The regression method appears to hug the data points, while the neural network approximation follows a mean path. For an analysis cycle, the approximate model required milliseconds of central processing unit (CPU) time versus seconds by the FLOPS code. Performance of the approximators was satisfactory for aircraft analysis. A design optimization capability has been created by coupling the derived analyzers to the optimization test bed CometBoards. The approximators were efficient reanalysis tools in the aircraft design optimization. Instability encountered in the FLOPS analyzer was eliminated. The convergence characteristics were improved for the design optimization. The CPU time required to calculate the optimum solution, measured in hours with the FLOPS code was reduced to minutes with the neural network approximation and to seconds with the regression method. Generation of the approximators required the manipulation of a very large quantity of data. Design sensitivity with respect to the bounds of aircraft constraints is easily generated.
NASA Technical Reports Server (NTRS)
Berke, Laszlo; Patnaik, Surya N.; Murthy, Pappu L. N.
1993-01-01
The application of artificial neural networks to capture structural design expertise is demonstrated. The principal advantage of a trained neural network is that it requires trivial computational effort to produce an acceptable new design. For the class of problems addressed, the development of a conventional expert system would be extremely difficult. In the present effort, a structural optimization code with multiple nonlinear programming algorithms and an artificial neural network code NETS were used. A set of optimum designs for a ring and two aircraft wings for static and dynamic constraints were generated by using the optimization codes. The optimum design data were processed to obtain input and output pairs, which were used to develop a trained artificial neural network with the code NETS. Optimum designs for new design conditions were predicted by using the trained network. Neural net prediction of optimum designs was found to be satisfactory for most of the output design parameters. However, results from the present study indicate that caution must be exercised to ensure that all design variables are within selected error bounds.
Study on Optimized Elman Neural Network Classification Algorithm Based on PLS and CA
Zhao, Dean; Shen, Tian; Zhao, Yuyan
2014-01-01
High-dimensional large sample data sets, between feature variables and between samples, may cause some correlative or repetitive factors, occupy lots of storage space, and consume much computing time. Using the Elman neural network to deal with them, too many inputs will influence the operating efficiency and recognition accuracy; too many simultaneous training samples, as well as being not able to get precise neural network model, also restrict the recognition accuracy. Aiming at these series of problems, we introduce the partial least squares (PLS) and cluster analysis (CA) into Elman neural network algorithm, by the PLS for dimension reduction which can eliminate the correlative and repetitive factors of the features. Using CA eliminates the correlative and repetitive factors of the sample. If some subclass becomes small sample, with high-dimensional feature and fewer numbers, PLS shows a unique advantage. Each subclass is regarded as one training sample to train the different precise neural network models. Then simulation samples are discriminated and classified into different subclasses, using the corresponding neural network to recognize it. An optimized Elman neural network classification algorithm based on PLS and CA (PLS-CA-Elman algorithm) is established. The new algorithm aims at improving the operating efficiency and recognition accuracy. By the case analysis, the new algorithm has unique superiority, worthy of further promotion. PMID:25165470
Optimization of sputter deposition parameters for magnetostrictive Fe62Co19Ga19/Si(100) films
NASA Astrophysics Data System (ADS)
Jen, S. U.; Tsai, T. L.
2012-04-01
A good magnetostrictive material should have large saturation magnetostriction (λS) and low saturation (or anisotropy) field (HS), such that its magnetostriction susceptibility (SH) can be as large as possible. In this study, we have made Fe62Co19Ga19/Si(100) nano-crystalline films by using the dc magnetron sputtering technique under various deposition conditions: Ar working gas pressure (pAr) was varied from 1 to 15 mTorr; sputtering power (Pw) was from 10 to 120 W; deposition temperature (TS) was from room temperature (RT) to 300 °C, The film thickness (tf) was fixed at 175 nm. Each magnetic domain looked like a long leaf, with a long-axis of about 12-15 μm and a short-axis of about 1.5 μm. The optimal magnetic and electrical properties were found from the Fe62Co19Ga19 film made with the sputter deposition parameters of pAr = 5 mTorr, Pw = 80 W, and TS = RT. Those optimal properties include λS = 80 ppm, HS = 19.8 Oe, SH = 6.1 ppm/Oe, and electrical resistivity ρ = 57.0 μΩ cm. Note that SH for the conventional magnetostrictive Terfenol-D film is, in general, equal to 1.5 ppm/Oe only.
NASA Technical Reports Server (NTRS)
Patniak, Surya N.; Guptill, James D.; Hopkins, Dale A.; Lavelle, Thomas M.
1998-01-01
Nonlinear mathematical-programming-based design optimization can be an elegant method. However, the calculations required to generate the merit function, constraints, and their gradients, which are frequently required, can make the process computational intensive. The computational burden can be greatly reduced by using approximating analyzers derived from an original analyzer utilizing neural networks and linear regression methods. The experience gained from using both of these approximation methods in the design optimization of a high speed civil transport aircraft is the subject of this paper. The Langley Research Center's Flight Optimization System was selected for the aircraft analysis. This software was exercised to generate a set of training data with which a neural network and a regression method were trained, thereby producing the two approximating analyzers. The derived analyzers were coupled to the Lewis Research Center's CometBoards test bed to provide the optimization capability. With the combined software, both approximation methods were examined for use in aircraft design optimization, and both performed satisfactorily. The CPU time for solution of the problem, which had been measured in hours, was reduced to minutes with the neural network approximation and to seconds with the regression method. Instability encountered in the aircraft analysis software at certain design points was also eliminated. On the other hand, there were costs and difficulties associated with training the approximating analyzers. The CPU time required to generate the input-output pairs and to train the approximating analyzers was seven times that required for solution of the problem.
Wang, Jie-sheng; Li, Shu-xia; Gao, Jie
2014-01-01
For meeting the real-time fault diagnosis and the optimization monitoring requirements of the polymerization kettle in the polyvinyl chloride resin (PVC) production process, a fault diagnosis strategy based on the self-organizing map (SOM) neural network is proposed. Firstly, a mapping between the polymerization process data and the fault pattern is established by analyzing the production technology of polymerization kettle equipment. The particle swarm optimization (PSO) algorithm with a new dynamical adjustment method of inertial weights is adopted to optimize the structural parameters of SOM neural network. The fault pattern classification of the polymerization kettle equipment is to realize the nonlinear mapping from symptom set to fault set according to the given symptom set. Finally, the simulation experiments of fault diagnosis are conducted by combining with the industrial on-site historical data of the polymerization kettle and the simulation results show that the proposed PSO-SOM fault diagnosis strategy is effective. PMID:25152929
Two neural network algorithms for designing optimal terminal controllers with open final time
NASA Technical Reports Server (NTRS)
Plumer, Edward S.
1992-01-01
Multilayer neural networks, trained by the backpropagation through time algorithm (BPTT), have been used successfully as state-feedback controllers for nonlinear terminal control problems. Current BPTT techniques, however, are not able to deal systematically with open final-time situations such as minimum-time problems. Two approaches which extend BPTT to open final-time problems are presented. In the first, a neural network learns a mapping from initial-state to time-to-go. In the second, the optimal number of steps for each trial run is found using a line-search. Both methods are derived using Lagrange multiplier techniques. This theoretical framework is used to demonstrate that the derived algorithms are direct extensions of forward/backward sweep methods used in N-stage optimal control. The two algorithms are tested on a Zermelo problem and the resulting trajectories compare favorably to optimal control results.
Wang, Jie-sheng; Li, Shu-xia; Gao, Jie
2014-01-01
For meeting the real-time fault diagnosis and the optimization monitoring requirements of the polymerization kettle in the polyvinyl chloride resin (PVC) production process, a fault diagnosis strategy based on the self-organizing map (SOM) neural network is proposed. Firstly, a mapping between the polymerization process data and the fault pattern is established by analyzing the production technology of polymerization kettle equipment. The particle swarm optimization (PSO) algorithm with a new dynamical adjustment method of inertial weights is adopted to optimize the structural parameters of SOM neural network. The fault pattern classification of the polymerization kettle equipment is to realize the nonlinear mapping from symptom set to fault set according to the given symptom set. Finally, the simulation experiments of fault diagnosis are conducted by combining with the industrial on-site historical data of the polymerization kettle and the simulation results show that the proposed PSO-SOM fault diagnosis strategy is effective.
Toward an optimal convolutional neural network for traffic sign recognition
NASA Astrophysics Data System (ADS)
Habibi Aghdam, Hamed; Jahani Heravi, Elnaz; Puig, Domenec
2015-12-01
Convolutional Neural Networks (CNN) beat the human performance on German Traffic Sign Benchmark competition. Both the winner and the runner-up teams trained CNNs to recognize 43 traffic signs. However, both networks are not computationally efficient since they have many free parameters and they use highly computational activation functions. In this paper, we propose a new architecture that reduces the number of the parameters 27% and 22% compared with the two networks. Furthermore, our network uses Leaky Rectified Linear Units (ReLU) as the activation function that only needs a few operations to produce the result. Specifically, compared with the hyperbolic tangent and rectified sigmoid activation functions utilized in the two networks, Leaky ReLU needs only one multiplication operation which makes it computationally much more efficient than the two other functions. Our experiments on the Gertman Traffic Sign Benchmark dataset shows 0:6% improvement on the best reported classification accuracy while it reduces the overall number of parameters 85% compared with the winner network in the competition.
2012-01-01
Good ohmic contacts with low contact resistance, smooth surface morphology, and a well-defined edge profile are essential to ensure optimal device performances for the AlGaN/GaN high electron mobility transistors [HEMTs]. A tantalum [Ta] metal layer and an SiNx thin film were used for the first time as an effective diffusion barrier and encapsulation layer in the standard Ti/Al/metal/Au ohmic metallization scheme in order to obtain high quality ohmic contacts with a focus on the thickness of Ta and SiNx. It is found that the Ta thickness is the dominant factor affecting the contact resistance, while the SiNx thickness affects the surface morphology significantly. An optimized Ti/Al/Ta/Au ohmic contact including a 40-nm thick Ta barrier layer and a 50-nm thick SiNx encapsulation layer is preferred when compared with the other conventional ohmic contact stacks as it produces a low contact resistance of around 7.27 × 10-7 Ω·cm2 and an ultra-low nanoscale surface morphology with a root mean square deviation of around 10 nm. Results from the proposed study play an important role in obtaining excellent ohmic contact formation in the fabrication of AlGaN/GaN HEMTs. PMID:22313812
Wang, Cong; Kim, Nam-Young
2012-02-07
Good ohmic contacts with low contact resistance, smooth surface morphology, and a well-defined edge profile are essential to ensure optimal device performances for the AlGaN/GaN high electron mobility transistors [HEMTs]. A tantalum [Ta] metal layer and an SiNx thin film were used for the first time as an effective diffusion barrier and encapsulation layer in the standard Ti/Al/metal/Au ohmic metallization scheme in order to obtain high quality ohmic contacts with a focus on the thickness of Ta and SiNx. It is found that the Ta thickness is the dominant factor affecting the contact resistance, while the SiNx thickness affects the surface morphology significantly. An optimized Ti/Al/Ta/Au ohmic contact including a 40-nm thick Ta barrier layer and a 50-nm thick SiNx encapsulation layer is preferred when compared with the other conventional ohmic contact stacks as it produces a low contact resistance of around 7.27 × 10-7 Ω·cm2 and an ultra-low nanoscale surface morphology with a root mean square deviation of around 10 nm. Results from the proposed study play an important role in obtaining excellent ohmic contact formation in the fabrication of AlGaN/GaN HEMTs.
Petrović, Jelena; Ibrić, Svetlana; Betz, Gabriele; Đurić, Zorica
2012-05-30
The main objective of the study was to develop artificial intelligence methods for optimization of drug release from matrix tablets regardless of the matrix type. Static and dynamic artificial neural networks of the same topology were developed to model dissolution profiles of different matrix tablets types (hydrophilic/lipid) using formulation composition, compression force used for tableting and tablets porosity and tensile strength as input data. Potential application of decision trees in discovering knowledge from experimental data was also investigated. Polyethylene oxide polymer and glyceryl palmitostearate were used as matrix forming materials for hydrophilic and lipid matrix tablets, respectively whereas selected model drugs were diclofenac sodium and caffeine. Matrix tablets were prepared by direct compression method and tested for in vitro dissolution profiles. Optimization of static and dynamic neural networks used for modeling of drug release was performed using Monte Carlo simulations or genetic algorithms optimizer. Decision trees were constructed following discretization of data. Calculated difference (f(1)) and similarity (f(2)) factors for predicted and experimentally obtained dissolution profiles of test matrix tablets formulations indicate that Elman dynamic neural networks as well as decision trees are capable of accurate predictions of both hydrophilic and lipid matrix tablets dissolution profiles. Elman neural networks were compared to most frequently used static network, Multi-layered perceptron, and superiority of Elman networks have been demonstrated. Developed methods allow simple, yet very precise way of drug release predictions for both hydrophilic and lipid matrix tablets having controlled drug release.
Optimization of cocoa butter analog synthesis variables using neural networks and genetic algorithm.
Shekarchizadeh, Hajar; Tikani, Reza; Kadivar, Mahdi
2014-09-01
Cocoa butter analog was prepared from camel hump fat and tristearin by enzymatic interesterification in supercritical carbon dioxide (SC-CO2) using immobilized Thermomyces lanuginosus lipase (Lipozyme TL IM) as a biocatalyst. Optimal process conditions were determined using neural networks and genetic algorithm optimization. Response surfaces methodology was used to design the experiments to collect data for the neural network modelling. A general regression neural network model was developed to predict the response of triacylglycerol (TAG) distribution of cocoa butter analog from the process pressure, temperature, tristearin/camel hump fat ratio, water content, and incubation time. A genetic algorithm was used to search for a combination of the process variables for production of most similar cocoa butter analog to the corresponding cocoa butter. The combinations of the process variables during genetic algorithm optimization were evaluated using the neural network model. The pressure of 10 MPa; temperature of 40 °C; SSS/CHF ratio of 0.6:1; water content of 13 % (w/w); and incubation time of 4.5 h were found to be the optimum conditions to achieve the most similar cocoa butter analog to the corresponding cocoa butter. PMID:25190869
A high-performance keyboard neural prosthesis enabled by task optimization
Nuyujukian, Paul; Fan, Joline M.; Kao, Jonathan C.; Ryu, Stephen I.; Shenoy, Krishna V.
2015-01-01
Communication neural prostheses are an emerging class of medical devices that aim to restore efficient communication to people suffering from paralysis. These systems rely on an interface with the user, either via the use of a continuously-moving cursor (e.g., mouse) or the discrete selection of symbols (e.g., keyboard). In developing these interfaces, many design choices have a significant impact on the performance of the system. The objective of this study was to explore the design choices of a continuously-moving cursor neural prosthesis and optimize the interface to maximize information theoretic performance. We swept interface parameters of two keyboard-like tasks to find task and subject specific optimal parameters as measured by achieved bitrate using two rhesus macaques implanted with multielectrode arrays. In this report, we present the highest performing free-paced neural prosthesis under any recording modality with sustainable communication rates of up to 3.5 bits per second (bps). These findings demonstrate that meaningful high performance can be achieved using an intracortical neural prosthesis, and that, when optimized, these systems may be appropriate for use as communication devices for those with physical disabilities. PMID:25203982
Optimization of cocoa butter analog synthesis variables using neural networks and genetic algorithm.
Shekarchizadeh, Hajar; Tikani, Reza; Kadivar, Mahdi
2014-09-01
Cocoa butter analog was prepared from camel hump fat and tristearin by enzymatic interesterification in supercritical carbon dioxide (SC-CO2) using immobilized Thermomyces lanuginosus lipase (Lipozyme TL IM) as a biocatalyst. Optimal process conditions were determined using neural networks and genetic algorithm optimization. Response surfaces methodology was used to design the experiments to collect data for the neural network modelling. A general regression neural network model was developed to predict the response of triacylglycerol (TAG) distribution of cocoa butter analog from the process pressure, temperature, tristearin/camel hump fat ratio, water content, and incubation time. A genetic algorithm was used to search for a combination of the process variables for production of most similar cocoa butter analog to the corresponding cocoa butter. The combinations of the process variables during genetic algorithm optimization were evaluated using the neural network model. The pressure of 10 MPa; temperature of 40 °C; SSS/CHF ratio of 0.6:1; water content of 13 % (w/w); and incubation time of 4.5 h were found to be the optimum conditions to achieve the most similar cocoa butter analog to the corresponding cocoa butter.
NASA Technical Reports Server (NTRS)
Decker, Arthur J. (Inventor)
2006-01-01
An artificial neural network is disclosed that processes holography generated characteristic pattern of vibrating structures along with finite-element models. The present invention provides for a folding operation for conditioning training sets for optimally training forward-neural networks to process characteristic fringe pattern. The folding pattern increases the sensitivity of the feed-forward network for detecting changes in the characteristic pattern The folding routine manipulates input pixels so as to be scaled according to the location in an intensity range rather than the position in the characteristic pattern.
A stimulus-dependent spike threshold is an optimal neural coder
Jones, Douglas L.; Johnson, Erik C.; Ratnam, Rama
2015-01-01
A neural code based on sequences of spikes can consume a significant portion of the brain's energy budget. Thus, energy considerations would dictate that spiking activity be kept as low as possible. However, a high spike-rate improves the coding and representation of signals in spike trains, particularly in sensory systems. These are competing demands, and selective pressure has presumably worked to optimize coding by apportioning a minimum number of spikes so as to maximize coding fidelity. The mechanisms by which a neuron generates spikes while maintaining a fidelity criterion are not known. Here, we show that a signal-dependent neural threshold, similar to a dynamic or adapting threshold, optimizes the trade-off between spike generation (encoding) and fidelity (decoding). The threshold mimics a post-synaptic membrane (a low-pass filter) and serves as an internal decoder. Further, it sets the average firing rate (the energy constraint). The decoding process provides an internal copy of the coding error to the spike-generator which emits a spike when the error equals or exceeds a spike threshold. When optimized, the trade-off leads to a deterministic spike firing-rule that generates optimally timed spikes so as to maximize fidelity. The optimal coder is derived in closed-form in the limit of high spike-rates, when the signal can be approximated as a piece-wise constant signal. The predicted spike-times are close to those obtained experimentally in the primary electrosensory afferent neurons of weakly electric fish (Apteronotus leptorhynchus) and pyramidal neurons from the somatosensory cortex of the rat. We suggest that KCNQ/Kv7 channels (underlying the M-current) are good candidates for the decoder. They are widely coupled to metabolic processes and do not inactivate. We conclude that the neural threshold is optimized to generate an energy-efficient and high-fidelity neural code. PMID:26082710
McMahon, W. E.; Kurtz, S.; Emery, K.; Young, M. S.
2002-05-01
This conference paper investigates which reference spectrum should be used to design GaInP/GaAs/Ge triple-junction cells (at 300 K) in order to optimize their performance outdoors (at elevated temperatures). The outdoor performance is simulated using direct spectra from the recently proposed Module Energy Rating Procedure. We find that triple-junction cells designed for AM1.5D, low-AOD and AM1.5G standard spectra at 300 K all work well for maximizing daily energy production at elevated temperatures. AM1.5G cells are the best choice for midday power production, whereas AM1.5D cells are the best choice for power production during the morning and evening. Performance of cells optimized for a newly proposed Low-AOD spectrum is intermediate between these two extremes.
McMahon, W. E.; Kurtz, S.; Emery, K.; Young, M. S.
2003-05-01
This paper investigates which reference spectrum should be used to design GaInP/GaAs/Ge triple-junction cells (at 300 K) in order to optimize their performance outdoors (at elevated temperatures). The outdoor performance is simulated using direct spectra from the recently proposed Module Energy Rating Procedure. We find that triple-junction cells designed for AM1.5D, low-AOD and AM1.5G standard spectra at 300 K all work well for maximizing daily energy production at elevated temperatures. AM1.5G cells are the best choice for midday power production, whereas AM1.5D cells are the best choice for power production during the morning and evening. Performance of cells optimized for a newly proposed Low-AOD spectrum is intermediate between these two extremes.
NASA Astrophysics Data System (ADS)
He, Xiao-Guang; Zhao, De-Gang; Jiang, De-Sheng; Zhu, Jian-Jun; Chen, Ping; Liu, Zong-Shun; Le, Ling-Cong; Yang, Jing; Li, Xiao-Jing; Zhang, Shu-Ming; Yang, Hui
2015-09-01
AlGaN/AlN/GaN structures are grown by metalorganic vapor phase epitaxy on sapphire substrates. Influences of AlN interlayer thickness, AlGaN barrier thickness, and Al composition on the two-dimensional electron gas (2DEG) performance are investigated. Lowering the V/III ratio and enhancing the reactor pressure at the initial stage of the high-temperature GaN layer growth will prolong the GaN nuclei coalescence process and effectively improve the crystalline quality and the interface morphology, diminishing the interface roughness scattering and improving 2DEG mobility. AlGaN/AlN/GaN structure with 2DEG sheet density of 1.19 × 1013 cm-2, electron mobility of 2101 cm2·V-1·s-1, and square resistance of 249 Ω is obtained. Project support by the National Natural Science Foundation of China (Grant Nos. 61474110, 61377020, 61376089, 61223005, and 61176126), the National Science Fund for Distinguished Young Scholars, China (Grant No. 60925017), the One Hundred Person Project of the Chinese Academy of Sciences, and the Basic Research Project of Jiangsu Province, China (Grant No. BK20130362).
Optimizing protocols for imaging neural cells and tissues using functionalized quantum dots
NASA Astrophysics Data System (ADS)
Pathak, Smita; Silva, Gabriel A.
2008-02-01
Chemically functionalized semiconductor quantum dot protocols were optimized for the specific labeling and imaging of neural cells, both neurons and macroglial cells. Beta-tubulin III was used to image primary cortical neurons and PC12 cells while glial fibrillary acidic protein (GFAP) was used to image primary spinal cord and cortical astrocytes and the rMC-1 retinal glial Muller cell line. Both proteins are the main components of intermediate filaments and are specific to the two classes of neural cells. We also specifically labeled and imaged at high resolutions using anti-GFAP conjugated quantum dots glial scars in situ in intact neural sensory retina in a rodent model of macular degeneration.
NASA Technical Reports Server (NTRS)
Rajkumar, T.; Aragon, Cecilia; Bardina, Jorge; Britten, Roy
2002-01-01
A fast, reliable way of predicting aerodynamic coefficients is produced using a neural network optimized by a genetic algorithm. Basic aerodynamic coefficients (e.g. lift, drag, pitching moment) are modelled as functions of angle of attack and Mach number. The neural network is first trained on a relatively rich set of data from wind tunnel tests of numerical simulations to learn an overall model. Most of the aerodynamic parameters can be well-fitted using polynomial functions. A new set of data, which can be relatively sparse, is then supplied to the network to produce a new model consistent with the previous model and the new data. Because the new model interpolates realistically between the sparse test data points, it is suitable for use in piloted simulations. The genetic algorithm is used to choose a neural network architecture to give best results, avoiding over-and under-fitting of the test data.
Vrabie, Draguna; Lewis, Frank
2009-04-01
In this paper we present in a continuous-time framework an online approach to direct adaptive optimal control with infinite horizon cost for nonlinear systems. The algorithm converges online to the optimal control solution without knowledge of the internal system dynamics. Closed-loop dynamic stability is guaranteed throughout. The algorithm is based on a reinforcement learning scheme, namely Policy Iterations, and makes use of neural networks, in an Actor/Critic structure, to parametrically represent the control policy and the performance of the control system. The two neural networks are trained to express the optimal controller and optimal cost function which describes the infinite horizon control performance. Convergence of the algorithm is proven under the realistic assumption that the two neural networks do not provide perfect representations for the nonlinear control and cost functions. The result is a hybrid control structure which involves a continuous-time controller and a supervisory adaptation structure which operates based on data sampled from the plant and from the continuous-time performance dynamics. Such control structure is unlike any standard form of controllers previously seen in the literature. Simulation results, obtained considering two second-order nonlinear systems, are provided.
Vrabie, Draguna; Lewis, Frank
2009-04-01
In this paper we present in a continuous-time framework an online approach to direct adaptive optimal control with infinite horizon cost for nonlinear systems. The algorithm converges online to the optimal control solution without knowledge of the internal system dynamics. Closed-loop dynamic stability is guaranteed throughout. The algorithm is based on a reinforcement learning scheme, namely Policy Iterations, and makes use of neural networks, in an Actor/Critic structure, to parametrically represent the control policy and the performance of the control system. The two neural networks are trained to express the optimal controller and optimal cost function which describes the infinite horizon control performance. Convergence of the algorithm is proven under the realistic assumption that the two neural networks do not provide perfect representations for the nonlinear control and cost functions. The result is a hybrid control structure which involves a continuous-time controller and a supervisory adaptation structure which operates based on data sampled from the plant and from the continuous-time performance dynamics. Such control structure is unlike any standard form of controllers previously seen in the literature. Simulation results, obtained considering two second-order nonlinear systems, are provided. PMID:19362449
NASA Astrophysics Data System (ADS)
Piotrowski, Adam P.; Napiorkowski, Jarosław J.
2011-09-01
SummaryAlthough neural networks have been widely applied to various hydrological problems, including river flow forecasting, for at least 15 years, they have usually been trained by means of gradient-based algorithms. Recently nature inspired Evolutionary Computation algorithms have rapidly developed as optimization methods able to cope not only with non-differentiable functions but also with a great number of local minima. Some of proposed Evolutionary Computation algorithms have been tested for neural networks training, but publications which compare their performance with gradient-based training methods are rare and present contradictory conclusions. The main goal of the present study is to verify the applicability of a number of recently developed Evolutionary Computation optimization methods, mostly from the Differential Evolution family, to multi-layer perceptron neural networks training for daily rainfall-runoff forecasting. In the present paper eight Evolutionary Computation methods, namely the first version of Differential Evolution (DE), Distributed DE with Explorative-Exploitative Population Families, Self-Adaptive DE, DE with Global and Local Neighbors, Grouping DE, JADE, Comprehensive Learning Particle Swarm Optimization and Efficient Population Utilization Strategy Particle Swarm Optimization are tested against the Levenberg-Marquardt algorithm - probably the most efficient in terms of speed and success rate among gradient-based methods. The Annapolis River catchment was selected as the area of this study due to its specific climatic conditions, characterized by significant seasonal changes in runoff, rapid floods, dry summers, severe winters with snowfall, snow melting, frequent freeze and thaw, and presence of river ice - conditions which make flow forecasting more troublesome. The overall performance of the Levenberg-Marquardt algorithm and the DE with Global and Local Neighbors method for neural networks training turns out to be superior to other
Optimal Recognition Method of Human Activities Using Artificial Neural Networks
NASA Astrophysics Data System (ADS)
Oniga, Stefan; József, Sütő
2015-12-01
The aim of this research is an exhaustive analysis of the various factors that may influence the recognition rate of the human activity using wearable sensors data. We made a total of 1674 simulations on a publically released human activity database by a group of researcher from the University of California at Berkeley. In a previous research, we analyzed the influence of the number of sensors and their placement. In the present research we have examined the influence of the number of sensor nodes, the type of sensor node, preprocessing algorithms, type of classifier and its parameters. The final purpose is to find the optimal setup for best recognition rates with lowest hardware and software costs.
The Optimal Operation of Multi-reservoir Floodwater Resources Control Based on GA-PSO
NASA Astrophysics Data System (ADS)
Huang, X.; Zhu, X.; Lian, Y.; Fang, G.; Zhu, L.
2015-12-01
Floodwater resources control operation has an important role to reduce flood disaster, ease the contradiction between water supply and demand and improve flood resource utilization. Based on the basin safety and floodwater resources utilization with the maximum benefit for floodwater optimal scheduling, the optimal operation of multi-reservoir floodwater resources control model is established. There are two objectives of floodwater resources control operation in multi-reservoir system. The first one is floodwater control safety, the other one is floodwater resource utilization with the maximum benefit. For the floodwater control safety target, the maximal flood peak reduction criterion is selected as the objective function. The maximal flood peak reduction criterion means that choosing reducing most peak flow as the judgment standard of the flood control operations optimal solution. For the floodwater resource utilization, maximum benefit of floodwater utilization refers to make full use of multi-reservoir capacity, accumulate transit flood as much as possible. In the other word, it refers to take releasing water as least as possible as the target in the case of determining the flood process. The model is solved by the coupling optimal method of genetic algorithm and particle swarm optimization (GA-PSO). GA-PSO uses the mutation for reference and takes PSO as a template, introduces the crossover and mutation into the search process of PSO in order to improve the search capabilities of particles. In order to make the particles have the characteristics of the current global best solution, crossover and mutation are used in the updated particles. Taking Shilianghe reservoir and Anfengshan reservoir in Jiangsu Province, China, for an case study, the results show that the optimal operation will reduce the floodwater resources control pressure, as well as keep nearly 81.11 million cubic meters floodwater resources accumulating in Longlianghe river and Anfengshan
Single- and Multiple-Objective Optimization with Differential Evolution and Neural Networks
NASA Technical Reports Server (NTRS)
Rai, Man Mohan
2006-01-01
Genetic and evolutionary algorithms have been applied to solve numerous problems in engineering design where they have been used primarily as optimization procedures. These methods have an advantage over conventional gradient-based search procedures became they are capable of finding global optima of multi-modal functions and searching design spaces with disjoint feasible regions. They are also robust in the presence of noisy data. Another desirable feature of these methods is that they can efficiently use distributed and parallel computing resources since multiple function evaluations (flow simulations in aerodynamics design) can be performed simultaneously and independently on ultiple processors. For these reasons genetic and evolutionary algorithms are being used more frequently in design optimization. Examples include airfoil and wing design and compressor and turbine airfoil design. They are also finding increasing use in multiple-objective and multidisciplinary optimization. This lecture will focus on an evolutionary method that is a relatively new member to the general class of evolutionary methods called differential evolution (DE). This method is easy to use and program and it requires relatively few user-specified constants. These constants are easily determined for a wide class of problems. Fine-tuning the constants will off course yield the solution to the optimization problem at hand more rapidly. DE can be efficiently implemented on parallel computers and can be used for continuous, discrete and mixed discrete/continuous optimization problems. It does not require the objective function to be continuous and is noise tolerant. DE and applications to single and multiple-objective optimization will be included in the presentation and lecture notes. A method for aerodynamic design optimization that is based on neural networks will also be included as a part of this lecture. The method offers advantages over traditional optimization methods. It is more
Autonomous Growing Neural Gas for applications with time constraint: optimal parameter estimation.
García-Rodríguez, José; Angelopoulou, Anastassia; García-Chamizo, Juan Manuel; Psarrou, Alexandra; Orts Escolano, Sergio; Morell Giménez, Vicente
2012-08-01
This paper aims to address the ability of self-organizing neural network models to manage real-time applications. Specifically, we introduce fAGNG (fast Autonomous Growing Neural Gas), a modified learning algorithm for the incremental model Growing Neural Gas (GNG) network. The Growing Neural Gas network with its attributes of growth, flexibility, rapid adaptation, and excellent quality of representation of the input space makes it a suitable model for real time applications. However, under time constraints GNG fails to produce the optimal topological map for any input data set. In contrast to existing algorithms, the proposed fAGNG algorithm introduces multiple neurons per iteration. The number of neurons inserted and input data generated is controlled autonomous and dynamically based on a priory or online learnt model. A detailed study of the topological preservation and quality of representation depending on the neural network parameter selection has been developed to find the best alternatives to represent different linear and non-linear input spaces under time restrictions or specific quality of representation requirements. PMID:22386599
An adaptive training method for optimal interpolative neural nets.
Liu, T Z; Yen, C W
1997-04-01
In contrast to conventional multilayered feedforward networks which are typically trained by iterative gradient search methods, an optimal interpolative (OI) net can be trained by a noniterative least squares algorithm called RLS-OI. The basic idea of RLS-OI is to use a subset of the training set, whose inputs are called subprototypes, to constrain the OI net solution. A subset of these subprototypes, called prototypes, is then chosen as the parameter vectors of the activation functions of the OI net to satisfy the subprototype constraints in the least squares (LS) sense. By dynamically increasing the numbers of subprototypes and prototypes, RLS-OI evolves the OI net from scratch to the extent sufficient to solve a given classification problem. To improve the performance of RLS-OI, this paper addresses two important problems in OI net training: the selection of the subprototypes and the selection of the prototypes. By choosing subprototypes from poorly classified regions, this paper proposes a new subprototype selection method which is adaptive to the changing classification performance of the growing OI net. This paper also proposes a new prototype selection criterion to reduce the complexity of the OI net. For the same training accuracy, simulation results demonstrate that the proposed approach produces smaller OI net than the RLS-OI algorithm. Experimental results also show that the proposed approach is less sensitive to the variation of the training set than RLS-OI.
Classification of land cover using optimized neural nets on SPOT data
Dreyer, P. )
1993-05-01
An optimized neural net was developed for land-cover classification in a multispectral SPOT satellite image covering 10 km x 10 km region which contains a mixture of densely built-up areas, suburbs, rural land, and waterbodies. In the technique, segments in the image are described by textural features calculated from gray-level difference statistics. The size of the input layer (i.e., the input variables to be used), as well as the size of the hidden layer in the neural net are determined using the optimization algorithm proposed by Mozer and Smolensky (1989). The textural features are calculated in segments generated by region growing in an image which has been processed iteratively with an edge enhancing adaptive filter. 15 refs.
Neural Network and Regression Methods Demonstrated in the Design Optimization of a Subsonic Aircraft
NASA Technical Reports Server (NTRS)
Hopkins, Dale A.; Lavelle, Thomas M.; Patnaik, Surya
2003-01-01
The neural network and regression methods of NASA Glenn Research Center s COMETBOARDS design optimization testbed were used to generate approximate analysis and design models for a subsonic aircraft operating at Mach 0.85 cruise speed. The analytical model is defined by nine design variables: wing aspect ratio, engine thrust, wing area, sweep angle, chord-thickness ratio, turbine temperature, pressure ratio, bypass ratio, fan pressure; and eight response parameters: weight, landing velocity, takeoff and landing field lengths, approach thrust, overall efficiency, and compressor pressure and temperature. The variables were adjusted to optimally balance the engines to the airframe. The solution strategy included a sensitivity model and the soft analysis model. Researchers generated the sensitivity model by training the approximators to predict an optimum design. The trained neural network predicted all response variables, within 5-percent error. This was reduced to 1 percent by the regression method. The soft analysis model was developed to replace aircraft analysis as the reanalyzer in design optimization. Soft models have been generated for a neural network method, a regression method, and a hybrid method obtained by combining the approximators. The performance of the models is graphed for aircraft weight versus thrust as well as for wing area and turbine temperature. The regression method followed the analytical solution with little error. The neural network exhibited 5-percent maximum error over all parameters. Performance of the hybrid method was intermediate in comparison to the individual approximators. Error in the response variable is smaller than that shown in the figure because of a distortion scale factor. The overall performance of the approximators was considered to be satisfactory because aircraft analysis with NASA Langley Research Center s FLOPS (Flight Optimization System) code is a synthesis of diverse disciplines: weight estimation, aerodynamic
NASA Astrophysics Data System (ADS)
Gao, Zhongmei; Shao, Xinyu; Jiang, Ping; Cao, Longchao; Zhou, Qi; Yue, Chen; Liu, Yang; Wang, Chunming
2016-09-01
It is of great significance to select appropriate welding process parameters for obtaining optimal weld geometry in hybrid laser-arc welding. An integrated optimization approach by combining Kriging model and GA is proposed to optimize process parameters. A four-factor, five-level experiment using Taguchi L25 is conducted considering laser power (P), welding current (A), distance between laser and arc (D) and traveling speed (V). Kriging model is adopted to approximate the relationship between process parameters and weld geometry, namely depth of penetration (DP), bead width (BW) and bead reinforcement (BR). The constructed Kriging model was used for parameters optimization by GA to maximize DP, minimize BW and ensure BR at a desired value. The effects of process parameters on weld geometry are analyzed. Microstructure and micro-hardness are also discussed. Verification experiments demonstrate that the obtained optimum values are in good agreement with experimental results.
Tahriri, Farzad; Dawal, Siti Zawiah Md; Taha, Zahari
2014-01-01
A new multiobjective dynamic fuzzy genetic algorithm is applied to solve a fuzzy mixed-model assembly line sequencing problem in which the primary goals are to minimize the total make-span and minimize the setup number simultaneously. Trapezoidal fuzzy numbers are implemented for variables such as operation and travelling time in order to generate results with higher accuracy and representative of real-case data. An improved genetic algorithm called fuzzy adaptive genetic algorithm (FAGA) is proposed in order to solve this optimization model. In establishing the FAGA, five dynamic fuzzy parameter controllers are devised in which fuzzy expert experience controller (FEEC) is integrated with automatic learning dynamic fuzzy controller (ALDFC) technique. The enhanced algorithm dynamically adjusts the population size, number of generations, tournament candidate, crossover rate, and mutation rate compared with using fixed control parameters. The main idea is to improve the performance and effectiveness of existing GAs by dynamic adjustment and control of the five parameters. Verification and validation of the dynamic fuzzy GA are carried out by developing test-beds and testing using a multiobjective fuzzy mixed production assembly line sequencing optimization problem. The simulation results highlight that the performance and efficacy of the proposed novel optimization algorithm are more efficient than the performance of the standard genetic algorithm in mixed assembly line sequencing model. PMID:24982962
Chang, P.S.; Poston, J.M.; Schroech, K.A.; Hou, H.S.
1995-12-31
Boiler performance optimization includes the preservation of efficiency, emission, capacity, and reliability. Competitive pressures require cost reduction and environmental compliance. It is a challenge for utility personnel to balance these requirements often demand tradeoffs. The Clean Air Act Amendment requires utilities to reduce NOx emission. NOx emission reduction has often been accomplished by installation of new low NOx burners. Boiler tuning for NOx control can be used as an alternative to low NOx burner installation. Specifically in tangentially-fired boilers, boiler tuning can be very effective in NOx reduction. A PC-based computer software program was developed to assist the tuning process. This software, System Optimization Analysis Program (SOAP), is a neural network based code which uses the self-adaptation learning process, with an adaptive filter added for data noise control. SOAP can use historical data as the knowledge base and provides a fast optimal solution to adaptive control problems. SOAP was tested at TVA`s Kingston Unit 3 tangentially coal-fired furnace for NOx reduction. With a well-organized test plan, the optimized solution was reached with 16 tests at each test series load level. SOAP will be used for other plant equipment or system optimization, such as pulverizer performance, combustion system optimization, compared thermal performance design, and boiler tube leak detection and allocation.
Wang, Shu-tao; Chen, Dong-ying; Hou, Pei-guo; Wang, Xing-long; Wang, Zhi-fang; Wei, Meng
2015-06-01
Sodium methylparaben as one kind of preservatives is widely used in our life, but it will do harm to health if it is eaten too much. So there are strict rules on the dosage of sodium methylparaben in every country. The fluorescence spectral properties of sodium methylparaben in aqueous solution and orange juice solution are analyzed with FS920 fluorescence spectrometer. The research result shows that the fluorescence characteristic peak of sodium methylparaben solution is in λ(ex)/λ(em) = 380/5 10 nm, while sodium methylparaben orange juice solution has two fluorescence characteristic peaks which are in λ(ex)/λ(em) = 440/520 nm and 470/530 nm, and its best excitation wavelength is 440 nm. So it can be concluded from the result that there is a significant change between the characteristic peaks of sodium methylparaben in the two solution. Compared with the fluorescence characteristic peak of sodium methylparaben solution, thoses of sodium methylparaben orange juice solution are changed significantly, which are caused by the interference of orange juice fluorescence characteristics. In order to determine the content of sodium methylparaben in the fresh orange juice, a detection model of sodium methylparaben content in orange juice is built based on GA-BP neural network, according to the relationship between fluorescence intensity in λ(ex) = 440 nm and the content of sodium methylparaben orange juice solution. When the accuracy of the mean square error in the process of network training reaches 10(-3), the correlation coefficient of network output and that of the expected is 0.996. At the same time, a better prediction result can be obtained that the average recovery of the forecast samples is 98.67% and the average relative standard deviation is 0.86%. When the concentration ranges from 0.02 to 1.0 g x L(-1), the results testify that detection method based on fluorescence spectroscopy and GA-BP neural network can accurately determine the content of sodium
Wang, Shu-tao; Chen, Dong-ying; Hou, Pei-guo; Wang, Xing-long; Wang, Zhi-fang; Wei, Meng
2015-06-01
Sodium methylparaben as one kind of preservatives is widely used in our life, but it will do harm to health if it is eaten too much. So there are strict rules on the dosage of sodium methylparaben in every country. The fluorescence spectral properties of sodium methylparaben in aqueous solution and orange juice solution are analyzed with FS920 fluorescence spectrometer. The research result shows that the fluorescence characteristic peak of sodium methylparaben solution is in λ(ex)/λ(em) = 380/5 10 nm, while sodium methylparaben orange juice solution has two fluorescence characteristic peaks which are in λ(ex)/λ(em) = 440/520 nm and 470/530 nm, and its best excitation wavelength is 440 nm. So it can be concluded from the result that there is a significant change between the characteristic peaks of sodium methylparaben in the two solution. Compared with the fluorescence characteristic peak of sodium methylparaben solution, thoses of sodium methylparaben orange juice solution are changed significantly, which are caused by the interference of orange juice fluorescence characteristics. In order to determine the content of sodium methylparaben in the fresh orange juice, a detection model of sodium methylparaben content in orange juice is built based on GA-BP neural network, according to the relationship between fluorescence intensity in λ(ex) = 440 nm and the content of sodium methylparaben orange juice solution. When the accuracy of the mean square error in the process of network training reaches 10(-3), the correlation coefficient of network output and that of the expected is 0.996. At the same time, a better prediction result can be obtained that the average recovery of the forecast samples is 98.67% and the average relative standard deviation is 0.86%. When the concentration ranges from 0.02 to 1.0 g x L(-1), the results testify that detection method based on fluorescence spectroscopy and GA-BP neural network can accurately determine the content of sodium
NASA Astrophysics Data System (ADS)
Zhang, Enlai; Hou, Liang; Shen, Chao; Shi, Yingliang; Zhang, Yaxiang
2016-01-01
To better solve the complex non-linear problem between the subjective sound quality evaluation results and objective psychoacoustics parameters, a method for the prediction of the sound quality is put forward by using a back propagation neural network (BPNN) based on particle swarm optimization (PSO), which is optimizing the initial weights and thresholds of BP network neurons through the PSO. In order to verify the effectiveness and accuracy of this approach, the noise signals of the B-Class vehicles from the idle speed to 120 km h-1 measured by the artificial head, are taken as a target. In addition, this paper describes a subjective evaluation experiment on the sound quality annoyance inside the vehicles through a grade evaluation method, by which the annoyance of each sample is obtained. With the use of Artemis software, the main objective psychoacoustic parameters of each noise sample are calculated. These parameters include loudness, sharpness, roughness, fluctuation, tonality, articulation index (AI) and A-weighted sound pressure level. Furthermore, three evaluation models with the same artificial neural network (ANN) structure are built: the standard BPNN model, the genetic algorithm-back-propagation neural network (GA-BPNN) model and the PSO-back-propagation neural network (PSO-BPNN) model. After the network training and the evaluation prediction on the three models’ network based on experimental data, it proves that the PSO-BPNN method can achieve convergence more quickly and improve the prediction accuracy of sound quality, which can further lay a foundation for the control of the sound quality inside vehicles.
NASA Technical Reports Server (NTRS)
Patnaik, Surya N.; Guptill, James D.; Hopkins, Dale A.; Lavelle, Thomas M.
2000-01-01
The NASA Engine Performance Program (NEPP) can configure and analyze almost any type of gas turbine engine that can be generated through the interconnection of a set of standard physical components. In addition, the code can optimize engine performance by changing adjustable variables under a set of constraints. However, for engine cycle problems at certain operating points, the NEPP code can encounter difficulties: nonconvergence in the currently implemented Powell's optimization algorithm and deficiencies in the Newton-Raphson solver during engine balancing. A project was undertaken to correct these deficiencies. Nonconvergence was avoided through a cascade optimization strategy, and deficiencies associated with engine balancing were eliminated through neural network and linear regression methods. An approximation-interspersed cascade strategy was used to optimize the engine's operation over its flight envelope. Replacement of Powell's algorithm by the cascade strategy improved the optimization segment of the NEPP code. The performance of the linear regression and neural network methods as alternative engine analyzers was found to be satisfactory. This report considers two examples-a supersonic mixed-flow turbofan engine and a subsonic waverotor-topped engine-to illustrate the results, and it discusses insights gained from the improved version of the NEPP code.
Neural Network Cascade Optimizes MicroRNA Biomarker Selection for Nasopharyngeal Cancer Prognosis
Zhu, Wenliang; Kan, Xuan
2014-01-01
MicroRNAs (miRNAs) have been shown to be promising biomarkers in predicting cancer prognosis. However, inappropriate or poorly optimized processing and modeling of miRNA expression data can negatively affect prediction performance. Here, we propose a holistic solution for miRNA biomarker selection and prediction model building. This work introduces the use of a neural network cascade, a cascaded constitution of small artificial neural network units, for evaluating miRNA expression and patient outcome. A miRNA microarray dataset of nasopharyngeal carcinoma was retrieved from Gene Expression Omnibus to illustrate the methodology. Results indicated a nonlinear relationship between miRNA expression and patient death risk, implying that direct comparison of expression values is inappropriate. However, this method performs transformation of miRNA expression values into a miRNA score, which linearly measures death risk. Spearman correlation was calculated between miRNA scores and survival status for each miRNA. Finally, a nine-miRNA signature was optimized to predict death risk after nasopharyngeal carcinoma by establishing a neural network cascade consisting of 13 artificial neural network units. Area under the ROC was 0.951 for the internal validation set and had a prediction accuracy of 83% for the external validation set. In particular, the established neural network cascade was found to have strong immunity against noise interference that disturbs miRNA expression values. This study provides an efficient and easy-to-use method that aims to maximize clinical application of miRNAs in prognostic risk assessment of patients with cancer. PMID:25310846
Neural network cascade optimizes microRNA biomarker selection for nasopharyngeal cancer prognosis.
Zhu, Wenliang; Kan, Xuan
2014-01-01
MicroRNAs (miRNAs) have been shown to be promising biomarkers in predicting cancer prognosis. However, inappropriate or poorly optimized processing and modeling of miRNA expression data can negatively affect prediction performance. Here, we propose a holistic solution for miRNA biomarker selection and prediction model building. This work introduces the use of a neural network cascade, a cascaded constitution of small artificial neural network units, for evaluating miRNA expression and patient outcome. A miRNA microarray dataset of nasopharyngeal carcinoma was retrieved from Gene Expression Omnibus to illustrate the methodology. Results indicated a nonlinear relationship between miRNA expression and patient death risk, implying that direct comparison of expression values is inappropriate. However, this method performs transformation of miRNA expression values into a miRNA score, which linearly measures death risk. Spearman correlation was calculated between miRNA scores and survival status for each miRNA. Finally, a nine-miRNA signature was optimized to predict death risk after nasopharyngeal carcinoma by establishing a neural network cascade consisting of 13 artificial neural network units. Area under the ROC was 0.951 for the internal validation set and had a prediction accuracy of 83% for the external validation set. In particular, the established neural network cascade was found to have strong immunity against noise interference that disturbs miRNA expression values. This study provides an efficient and easy-to-use method that aims to maximize clinical application of miRNAs in prognostic risk assessment of patients with cancer. PMID:25310846
Neural Network and Response Surface Methodology for Rocket Engine Component Optimization
NASA Technical Reports Server (NTRS)
Vaidyanathan, Rajkumar; Papita, Nilay; Shyy, Wei; Tucker, P. Kevin; Griffin, Lisa W.; Haftka, Raphael; Fitz-Coy, Norman; McConnaughey, Helen (Technical Monitor)
2000-01-01
The goal of this work is to compare the performance of response surface methodology (RSM) and two types of neural networks (NN) to aid preliminary design of two rocket engine components. A data set of 45 training points and 20 test points obtained from a semi-empirical model based on three design variables is used for a shear coaxial injector element. Data for supersonic turbine design is based on six design variables, 76 training, data and 18 test data obtained from simplified aerodynamic analysis. Several RS and NN are first constructed using the training data. The test data are then employed to select the best RS or NN. Quadratic and cubic response surfaces. radial basis neural network (RBNN) and back-propagation neural network (BPNN) are compared. Two-layered RBNN are generated using two different training algorithms, namely solverbe and solverb. A two layered BPNN is generated with Tan-Sigmoid transfer function. Various issues related to the training of the neural networks are addressed including number of neurons, error goals, spread constants and the accuracy of different models in representing the design space. A search for the optimum design is carried out using a standard gradient-based optimization algorithm over the response surfaces represented by the polynomials and trained neural networks. Usually a cubic polynominal performs better than the quadratic polynomial but exceptions have been noticed. Among the NN choices, the RBNN designed using solverb yields more consistent performance for both engine components considered. The training of RBNN is easier as it requires linear regression. This coupled with the consistency in performance promise the possibility of it being used as an optimization strategy for engineering design problems.
NASA Technical Reports Server (NTRS)
Hovel, H. J.; Woodall, J. M.
1976-01-01
The three types of solar cells investigated were: (1) one consisting of a nGaAs substrate, a Zn doped pGaAs region, and a Zn doped Ga(1-x)Al(x)As layer, (2) one consisting of an nGaAs substrate, a Ge doped pGaAs region, and a pGa(1-x)Al(x)As upper layer, and (3) one consisting of an n+GaAs substrate, an nGa(1-x)Al(X)As region, a pGa(1-x)Bl(X) As region, and a pGa(1-y)Al(y)As upper layer. In all three cases, the upper alloy layer is thin and of high Al composition in order to obtain high spectral response over the widest possible range of photon energies. Spectral response, capacitance-voltage, current-voltage, diffusion length, sunlight (or the equivalent)-efficiency, and efficiency-temperature measurements were made as a function of device parameters in order to analyze and optimize the solar cell behavior.
A novel constructive-optimizer neural network for the traveling salesman problem.
Saadatmand-Tarzjan, Mahdi; Khademi, Morteza; Akbarzadeh-T, Mohammad-R; Moghaddam, Hamid Abrishami
2007-08-01
In this paper, a novel constructive-optimizer neural network (CONN) is proposed for the traveling salesman problem (TSP). CONN uses a feedback structure similar to Hopfield-type neural networks and a competitive training algorithm similar to the Kohonen-type self-organizing maps (K-SOMs). Consequently, CONN is composed of a constructive part, which grows the tour and an optimizer part to optimize it. In the training algorithm, an initial tour is created first and introduced to CONN. Then, it is trained in the constructive phase for adding a number of cities to the tour. Next, the training algorithm switches to the optimizer phase for optimizing the current tour by displacing the tour cities. After convergence in this phase, the training algorithm switches to the constructive phase anew and is continued until all cities are added to the tour. Furthermore, we investigate a relationship between the number of TSP cities and the number of cities to be added in each constructive phase. CONN was tested on nine sets of benchmark TSPs from TSPLIB to demonstrate its performance and efficiency. It performed better than several typical Neural networks (NNs), including KNIES_TSP_Local, KNIES_TSP_Global, Budinich's SOM, Co-Adaptive Net, and multivalued Hopfield network as wall as computationally comparable variants of the simulated annealing algorithm, in terms of both CPU time and accuracy. Furthermore, CONN converged considerably faster than expanding SOM and evolved integrated SOM and generated shorter tours compared to KNIES_DECOMPOSE. Although CONN is not yet comparable in terms of accuracy with some sophisticated computationally intensive algorithms, it converges significantly faster than they do. Generally speaking, CONN provides the best compromise between CPU time and accuracy among currently reported NNs for TSP.
Shape Optimization of Supersonic Turbines Using Response Surface and Neural Network Methods
NASA Technical Reports Server (NTRS)
Papila, Nilay; Shyy, Wei; Griffin, Lisa W.; Dorney, Daniel J.
2001-01-01
Turbine performance directly affects engine specific impulse, thrust-to-weight ratio, and cost in a rocket propulsion system. A global optimization framework combining the radial basis neural network (RBNN) and the polynomial-based response surface method (RSM) is constructed for shape optimization of a supersonic turbine. Based on the optimized preliminary design, shape optimization is performed for the first vane and blade of a 2-stage supersonic turbine, involving O(10) design variables. The design of experiment approach is adopted to reduce the data size needed by the optimization task. It is demonstrated that a major merit of the global optimization approach is that it enables one to adaptively revise the design space to perform multiple optimization cycles. This benefit is realized when an optimal design approaches the boundary of a pre-defined design space. Furthermore, by inspecting the influence of each design variable, one can also gain insight into the existence of multiple design choices and select the optimum design based on other factors such as stress and materials considerations.
Modeling the motor cortex: Optimality, recurrent neural networks, and spatial dynamics.
Tanaka, Hirokazu
2016-03-01
Specialization of motor function in the frontal lobe was first discovered in the seminal experiments by Fritsch and Hitzig and subsequently by Ferrier in the 19th century. It is, however, ironical that the functional and computational role of the motor cortex still remains unresolved. A computational understanding of the motor cortex equals to understanding what movement variables the motor neurons represent (movement representation problem) and how such movement variables are computed through the interaction with anatomically connected areas (neural computation problem). Electrophysiological experiments in the 20th century demonstrated that the neural activities in motor cortex correlated with a number of motor-related and cognitive variables, thereby igniting the controversy over movement representations in motor cortex. Despite substantial experimental efforts, the overwhelming complexity found in neural activities has impeded our understanding of how movements are represented in the motor cortex. Recent progresses in computational modeling have rekindled this controversy in the 21st century. Here, I review the recent developments in computational models of the motor cortex, with a focus on optimality models, recurrent neural network models and spatial dynamics models. Although individual models provide consistent pictures within their domains, our current understanding about functions of the motor cortex is still fragmented.
2013-01-01
Background There is an increasing trend in using robots for medical purposes. One specific area is rehabilitation. Rehabilitation is one of the non-drug treatments in community health which means the restoration of the abilities to maximize independence. It is a prolonged work and costly labor. On the other hand, by using the flexible and efficient robots in rehabilitation area, this process will be more useful for handicapped patients. Methods In this study, a rule-based intelligent control methodology is proposed to mimic the behavior of a healthy limb in a satisfactory way by a 2-DOF planar robot. Inverse kinematic of the planar robot will be solved by neural networks and control parameters will be optimized by genetic algorithm, as rehabilitation progress. Results The results of simulations are presented by defining a physiotherapy simple mode on desired trajectory. MATLAB/Simulink is used for simulations. The system is capable of learning the action of the physiotherapist for each patient and imitating this behaviour in the absence of a physiotherapist that can be called robotherapy. Conclusions In this study, a therapeutic exercise planar 2-DOF robot is designed and controlled for lower-limb rehabilitation. The robot manipulator is controlled by combination of hybrid and adaptive controls. Some safety factors and stability constraints are defined and obtained. The robot is stopped when the safety factors are not satisfied. Kinematics of robot is estimated by an MLP neural network and proper control parameters are achieved using GA optimization. PMID:23945420
NASA Technical Reports Server (NTRS)
Hopkins, Dale A.; Patnaik, Surya N.
2000-01-01
A preliminary aircraft engine design methodology is being developed that utilizes a cascade optimization strategy together with neural network and regression approximation methods. The cascade strategy employs different optimization algorithms in a specified sequence. The neural network and regression methods are used to approximate solutions obtained from the NASA Engine Performance Program (NEPP), which implements engine thermodynamic cycle and performance analysis models. The new methodology is proving to be more robust and computationally efficient than the conventional optimization approach of using a single optimization algorithm with direct reanalysis. The methodology has been demonstrated on a preliminary design problem for a novel subsonic turbofan engine concept that incorporates a wave rotor as a cycle-topping device. Computations of maximum thrust were obtained for a specific design point in the engine mission profile. The results (depicted in the figure) show a significant improvement in the maximum thrust obtained using the new methodology in comparison to benchmark solutions obtained using NEPP in a manual design mode.
Xue, Y.; Liu, S.; Hu, Y.; Yang, J.; Chen, Q.
2007-01-01
To improve the accuracy in prediction, Genetic Algorithm based Adaptive Neural Network Ensemble (GA-ANNE) is presented. Intersections are allowed between different training sets based on the fuzzy clustering analysis, which ensures the diversity as well as the accuracy of individual Neural Networks (NNs). Moreover, to improve the accuracy of the adaptive weights of individual NNs, GA is used to optimize the cluster centers. Empirical results in predicting carbon flux of Duke Forest reveal that GA-ANNE can predict the carbon flux more accurately than Radial Basis Function Neural Network (RBFNN), Bagging NN ensemble, and ANNE. ?? 2007 IEEE.
NASA Astrophysics Data System (ADS)
Wagner, J.; Schmitz, J.; Herres, N.; Fuchs, F.; Serries, D.; Grietens, B.; Németh, S.; Hoof, C. Van; Borghs, G.
1998-07-01
The structural properties of InAs/(GaIn)Sb and (InGa)As/GaSb superlattices (SLs), grown by solid-source molecular-beam epitaxy on (0 0 1) GaAs substrates using a strain relaxed GaSb or InAs buffer layer or directly on (0 0 1) InAs substrates, were analyzed by high-resolution X-ray diffraction and Raman spectroscopy. The residual strain within the SL was found to depend critically on the type of interface bonds, which can be either InSb- or GaAs-like. Thus, to achieve lattice matching to the buffer layer or substrate by strain compensation within the SL stack, the controlled formation of the interface bonds is vital. On the other hand, minimization of the residual strain is shown to be a prerequisite for achieving a high photoluminescence yield and high responsivities for InAs/(GaIn)Sb SL based IR detectors.
Neural network-based optimal adaptive output feedback control of a helicopter UAV.
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.
NASA Astrophysics Data System (ADS)
Liu, Derong; Huang, Yuzhu; Wang, Ding; Wei, Qinglai
2013-09-01
In this paper, an observer-based optimal control scheme is developed for unknown nonlinear systems using adaptive dynamic programming (ADP) algorithm. First, a neural-network (NN) observer is designed to estimate system states. Then, based on the observed states, a neuro-controller is constructed via ADP method to obtain the optimal control. In this design, two NN structures are used: a three-layer NN is used to construct the observer which can be applied to systems with higher degrees of nonlinearity and without a priori knowledge of system dynamics, and a critic NN is employed to approximate the value function. The optimal control law is computed using the critic NN and the observer NN. Uniform ultimate boundedness of the closed-loop system is guaranteed. The actor, critic, and observer structures are all implemented in real-time, continuously and simultaneously. Finally, simulation results are presented to demonstrate the effectiveness of the proposed control scheme.
Constructing Optimal Prediction Intervals by Using Neural Networks and Bootstrap Method.
Khosravi, Abbas; Nahavandi, Saeid; Srinivasan, Dipti; Khosravi, Rihanna
2015-08-01
This brief proposes an efficient technique for the construction of optimized prediction intervals (PIs) by using the bootstrap technique. The method employs an innovative PI-based cost function in the training of neural networks (NNs) used for estimation of the target variance in the bootstrap method. An optimization algorithm is developed for minimization of the cost function and adjustment of NN parameters. The performance of the optimized bootstrap method is examined for seven synthetic and real-world case studies. It is shown that application of the proposed method improves the quality of constructed PIs by more than 28% over the existing technique, leading to narrower PIs with a coverage probability greater than the nominal confidence level.
Fast-convergent double-sigmoid Hopfield neural network as applied to optimization problems.
Uykan, Zekeriya
2013-06-01
The Hopfield neural network (HNN) has been widely used in numerous different optimization problems since the early 1980s. The convergence speed of the HNN (already in high gain) eventually plays a critical role in various real-time applications. In this brief, we propose and analyze a generalized HNN which drastically improves the convergence speed of the network, and thus allows benefiting from the HNN capabilities in solving the optimization problems in real time. By examining the channel allocation optimization problem in cellular radio systems, which is NP-complete and in which fast solution is necessary due to time-varying link gains, as well as the associative memory problem, computer simulations confirm the dramatic improvement in convergence speed at the expense of using a second nonlinear function in the proposed network.
Optimizing Semantic Pointer Representations for Symbol-Like Processing in Spiking Neural Networks.
Gosmann, Jan; Eliasmith, Chris
2016-01-01
The Semantic Pointer Architecture (SPA) is a proposal of specifying the computations and architectural elements needed to account for cognitive functions. By means of the Neural Engineering Framework (NEF) this proposal can be realized in a spiking neural network. However, in any such network each SPA transformation will accumulate noise. By increasing the accuracy of common SPA operations, the overall network performance can be increased considerably. As well, the representations in such networks present a trade-off between being able to represent all possible values and being only able to represent the most likely values, but with high accuracy. We derive a heuristic to find the near-optimal point in this trade-off. This allows us to improve the accuracy of common SPA operations by up to 25 times. Ultimately, it allows for a reduction of neuron number and a more efficient use of both traditional and neuromorphic hardware, which we demonstrate here. PMID:26900931
Elements of an algorithm for optimizing a parameter-structural neural network
NASA Astrophysics Data System (ADS)
Mrówczyńska, Maria
2016-06-01
The field of processing information provided by measurement results is one of the most important components of geodetic technologies. The dynamic development of this field improves classic algorithms for numerical calculations in the aspect of analytical solutions that are difficult to achieve. Algorithms based on artificial intelligence in the form of artificial neural networks, including the topology of connections between neurons have become an important instrument connected to the problem of processing and modelling processes. This concept results from the integration of neural networks and parameter optimization methods and makes it possible to avoid the necessity to arbitrarily define the structure of a network. This kind of extension of the training process is exemplified by the algorithm called the Group Method of Data Handling (GMDH), which belongs to the class of evolutionary algorithms. The article presents a GMDH type network, used for modelling deformations of the geometrical axis of a steel chimney during its operation.
Reifman, J. . Reactor Analysis Div.); Vitela, E.J. . Inst. de Ciencias Nucleares); Lee, J.C. . Dept. of Nuclear Engineering)
1993-01-01
Two complementary methods, statistical feature selection and nonlinear optimization through conjugate gradients, are used to expedite feedforward neural network training. Statistical feature selection techniques in the form of linear correlation coefficients and information-theoretic entropy are used to eliminate redundant and non-informative plant parameters to reduce the size of the network. The method of conjugate gradients is used to accelerate the network training convergence and to systematically calculate the Teaming and momentum constants at each iteration. The proposed techniques are compared with the backpropagation algorithm using the entire set of plant parameters in the training of neural networks to identify transients simulated with the Midland Nuclear Power Plant Unit 2 simulator. By using 25% of the plant parameters and the conjugate gradients, a 30-fold reduction in CPU time was obtained without degrading the diagnostic ability of the network.
Reifman, J.; Vitela, E.J.; Lee, J.C.
1993-03-01
Two complementary methods, statistical feature selection and nonlinear optimization through conjugate gradients, are used to expedite feedforward neural network training. Statistical feature selection techniques in the form of linear correlation coefficients and information-theoretic entropy are used to eliminate redundant and non-informative plant parameters to reduce the size of the network. The method of conjugate gradients is used to accelerate the network training convergence and to systematically calculate the Teaming and momentum constants at each iteration. The proposed techniques are compared with the backpropagation algorithm using the entire set of plant parameters in the training of neural networks to identify transients simulated with the Midland Nuclear Power Plant Unit 2 simulator. By using 25% of the plant parameters and the conjugate gradients, a 30-fold reduction in CPU time was obtained without degrading the diagnostic ability of the network.
Optimizing Semantic Pointer Representations for Symbol-Like Processing in Spiking Neural Networks
Gosmann, Jan; Eliasmith, Chris
2016-01-01
The Semantic Pointer Architecture (SPA) is a proposal of specifying the computations and architectural elements needed to account for cognitive functions. By means of the Neural Engineering Framework (NEF) this proposal can be realized in a spiking neural network. However, in any such network each SPA transformation will accumulate noise. By increasing the accuracy of common SPA operations, the overall network performance can be increased considerably. As well, the representations in such networks present a trade-off between being able to represent all possible values and being only able to represent the most likely values, but with high accuracy. We derive a heuristic to find the near-optimal point in this trade-off. This allows us to improve the accuracy of common SPA operations by up to 25 times. Ultimately, it allows for a reduction of neuron number and a more efficient use of both traditional and neuromorphic hardware, which we demonstrate here. PMID:26900931
Arab, Mohammad M.; Yadollahi, Abbas; Shojaeiyan, Abdolali; Ahmadi, Hamed
2016-01-01
One of the major obstacles to the micropropagation of Prunus rootstocks has, up until now, been the lack of a suitable tissue culture medium. Therefore, reformulation of culture media or modification of the mineral content might be a breakthrough to improve in vitro multiplication of G × N15 (garnem). We found artificial neural network in combination of genetic algorithm (ANN-GA) as a very precise and powerful modeling system for optimizing the culture medium, So that modeling the effects of MS mineral salts (NH4+, NO3-, PO42-, Ca2+, K+, SO42-, Mg2+, and Cl−) on in vitro multiplication parameters (the number of microshoots per explant, average length of microshoots, weight of calluses derived from the base of stem explants, and quality index of plantlets) of G × N15. Showed high R2 correlation values of 87, 91, 87, and 74 between observed and predicted values were found for these four growth parameters, respectively. According to the ANN-GA results, among the input variables, NH4+ and NO3- had the highest values of VSR in data set for the parameters studied. The ANN-GA showed that the best proliferation rate was obtained from medium containing (mM) 27.5 NO3-, 14 NH4+, 5 Ca2+, 25.9 K+, 0.7 Mg2+, 1.1 PO42-, 4.7 SO42-, and 0.96 Cl−. The performance of the medium optimized by ANN-GA, denoted as YAS (Yadollahi, Arab and Shojaeiyan), was compared to that of standard growth media for all Prunus rootstock, including the Murashige and Skoog (MS) medium, (specific media) EM, Quoirin and Lepoivre (QL) medium, and woody plant medium (WPM) Prunus. With respect to shoot length, shoot number per cultured explant and productivity (number of microshoots × length of microshoots), YAS was found to be superior to other media for in vitro multiplication of G × N15 rootstocks. In addition, our results indicated that by using ANN-GA, we were able to determine a suitable culture medium formulation to achieve the best in vitro productivity. PMID:27807436
NASA Technical Reports Server (NTRS)
Leyland, Jane Anne
2001-01-01
A closed-loop optimal neural-network controller technique was developed to optimize rotorcraft aeromechanical behaviour. This technique utilities a neural-network scheme to provide a general non-linear model of the rotorcraft. A modem constrained optimisation method is used to determine and update the constants in the neural-network plant model as well as to determine the optimal control vector. Current data is read, weighted, and added to a sliding data window. When the specified maximum number of data sets allowed in the data window is exceeded, the oldest data set is and the remaining data sets are re-weighted. This procedure provides at least four additional degrees-of-freedom in addition to the size and geometry of the neural-network itself with which to optimize the overall operation of the controller. These additional degrees-of-freedom are: 1. the maximum length of the sliding data window, 2. the frequency of neural-network updates, 3. the weighting of the individual data sets within the sliding window, and 4. the maximum number of optimisation iterations used for the neural-network updates.
Greenlee, Jordan D.; Feigelson, Boris N.; Anderson, Travis J.; Hite, Jennifer K.; Mastro, Michael A.; Eddy, Charles R.; Hobart, Karl D.; Kub, Francis J.; Tadjer, Marko J.
2014-08-14
The first step of a multi-cycle rapid thermal annealing process was systematically studied. The surface, structure, and optical properties of Mg implanted GaN thin films annealed at temperatures ranging from 900 to 1200 °C were investigated by Raman spectroscopy, photoluminescence, UV-visible spectroscopy, atomic force microscopy, and Nomarski microscopy. The GaN thin films are capped with two layers of in-situ metal organic chemical vapor deposition -grown AlN and annealed in 24 bar of N{sub 2} overpressure to avoid GaN decomposition. The crystal quality of the GaN improves with increasing annealing temperature as confirmed by UV-visible spectroscopy and the full widths at half maximums of the E{sub 2} and A{sub 1} (LO) Raman modes. The crystal quality of films annealed above 1100 °C exceeds the quality of the as-grown films. At 1200 °C, Mg is optically activated, which is determined by photoluminescence measurements. However, at 1200 °C, the GaN begins to decompose as evidenced by pit formation on the surface of the samples. Therefore, it was determined that the optimal temperature for the first step in a multi-cycle rapid thermal anneal process should be conducted at 1150 °C due to crystal quality and surface morphology considerations.
Kavitha, Ganesan; Ramakrishnan, Swaminathan
2010-01-01
Optic disc and retinal vasculature are important anatomical structures in the retina of the eye and any changes observed in these structures provide vital information on severity of various diseases. Digital retinal images are shown to provide a meaningful way of documenting and assessing some of the key elements inside the eye including the optic nerve and the tiny retinal blood vessels. In this work, an attempt has been made to detect and differentiate abnormalities of the retina using Digital image processing together with Optimization based segmentation and Artificial Neural Network methods. The retinal fundus images were recorded using standard protocols. Ant Colony Optimization is employed to extract the most significant objects namely the optic disc and blood vessel. The features related to these objects are obtained and corresponding indices are also derived. Further, these features are subjected to classification using Radial Basis Function Neural Networks and compared with conventional training algorithms. Results show that the Ant Colony Optimization is efficient in extracting useful information from retinal images. The features derived are effective for classification of normal and abnormal images using Radial basis function networks compared to other methods. As Optic disc and blood vessels are significant markers of abnormality in retinal images, the method proposed appears to be useful for mass screening. In this paper, the objectives of the study, methodology and significant observations are presented. PMID:20467104
Kavitha, Ganesan; Ramakrishnan, Swaminathan
2010-01-01
Optic disc and retinal vasculature are important anatomical structures in the retina of the eye and any changes observed in these structures provide vital information on severity of various diseases. Digital retinal images are shown to provide a meaningful way of documenting and assessing some of the key elements inside the eye including the optic nerve and the tiny retinal blood vessels. In this work, an attempt has been made to detect and differentiate abnormalities of the retina using Digital image processing together with Optimization based segmentation and Artificial Neural Network methods. The retinal fundus images were recorded using standard protocols. Ant Colony Optimization is employed to extract the most significant objects namely the optic disc and blood vessel. The features related to these objects are obtained and corresponding indices are also derived. Further, these features are subjected to classification using Radial Basis Function Neural Networks and compared with conventional training algorithms. Results show that the Ant Colony Optimization is efficient in extracting useful information from retinal images. The features derived are effective for classification of normal and abnormal images using Radial basis function networks compared to other methods. As Optic disc and blood vessels are significant markers of abnormality in retinal images, the method proposed appears to be useful for mass screening. In this paper, the objectives of the study, methodology and significant observations are presented.
NASA Technical Reports Server (NTRS)
Carpenter, William C.
1991-01-01
Engineering optimization problems involve minimizing some function subject to constraints. In areas such as aircraft optimization, the constraint equations may be from numerous disciplines such as transfer of information between these disciplines and the optimization algorithm. They are also suited to problems which may require numerous re-optimizations such as in multi-objective function optimization or to problems where the design space contains numerous local minima, thus requiring repeated optimizations from different initial designs. Their use has been limited, however, by the fact that development of response surfaces randomly selected or preselected points in the design space. Thus, they have been thought to be inefficient compared to algorithms to the optimum solution. A development has taken place in the last several years which may effect the desirability of using response surfaces. It may be possible that artificial neural nets are more efficient in developing response surfaces than polynomial approximations which have been used in the past. This development is the concern of the work.
NASA Astrophysics Data System (ADS)
Gurcan, Metin N.; Chan, Heang-Ping; Sahiner, Berkman; Hadjiiski, Lubomir M.; Petrick, Nicholas; Helvie, Mark A.
2002-05-01
We evaluated the effectiveness of an optimal convolution neural network (CNN) architecture selected by simulated annealing for improving the performance of a computer-aided diagnosis (CAD) system designed for the detection of microcalcification clusters on digitized mammograms. The performances of the CAD programs with manually and optimally selected CNNs were compared using an independent test set. This set included 472 mammograms and contained 253 biopsy-proven malignant clusters. Free-response receiver operating characteristic (FROC) analysis was used for evaluation of the detection accuracy. At a false positive (FP) rate of 0.7 per image, the film-based sensitivity was 84.6% with the optimized CNN, in comparison with 77.2% with the manually selected CNN. If clusters having images in both craniocaudal and mediolateral oblique views were analyzed together and a cluster was considered to be detected when it was detected in one or both views, at 0.7 FPs/image, the sensitivity was 93.3% with the optimized CNN and 87.0% with the manually selected CNN. This study indicates that classification of true positive and FP signals is an important step of the CAD program and that the detection accuracy of the program can be considerably improved by optimizing this step with an automated optimization algorithm.
Cao, Jianfang; Cui, Hongyan; Shi, Hao; Jiao, Lijuan
2016-01-01
A back-propagation (BP) neural network can solve complicated random nonlinear mapping problems; therefore, it can be applied to a wide range of problems. However, as the sample size increases, the time required to train BP neural networks becomes lengthy. Moreover, the classification accuracy decreases as well. To improve the classification accuracy and runtime efficiency of the BP neural network algorithm, we proposed a parallel design and realization method for a particle swarm optimization (PSO)-optimized BP neural network based on MapReduce on the Hadoop platform using both the PSO algorithm and a parallel design. The PSO algorithm was used to optimize the BP neural network's initial weights and thresholds and improve the accuracy of the classification algorithm. The MapReduce parallel programming model was utilized to achieve parallel processing of the BP algorithm, thereby solving the problems of hardware and communication overhead when the BP neural network addresses big data. Datasets on 5 different scales were constructed using the scene image library from the SUN Database. The classification accuracy of the parallel PSO-BP neural network algorithm is approximately 92%, and the system efficiency is approximately 0.85, which presents obvious advantages when processing big data. The algorithm proposed in this study demonstrated both higher classification accuracy and improved time efficiency, which represents a significant improvement obtained from applying parallel processing to an intelligent algorithm on big data. PMID:27304987
Cao, Jianfang; Cui, Hongyan; Shi, Hao; Jiao, Lijuan
2016-01-01
A back-propagation (BP) neural network can solve complicated random nonlinear mapping problems; therefore, it can be applied to a wide range of problems. However, as the sample size increases, the time required to train BP neural networks becomes lengthy. Moreover, the classification accuracy decreases as well. To improve the classification accuracy and runtime efficiency of the BP neural network algorithm, we proposed a parallel design and realization method for a particle swarm optimization (PSO)-optimized BP neural network based on MapReduce on the Hadoop platform using both the PSO algorithm and a parallel design. The PSO algorithm was used to optimize the BP neural network's initial weights and thresholds and improve the accuracy of the classification algorithm. The MapReduce parallel programming model was utilized to achieve parallel processing of the BP algorithm, thereby solving the problems of hardware and communication overhead when the BP neural network addresses big data. Datasets on 5 different scales were constructed using the scene image library from the SUN Database. The classification accuracy of the parallel PSO-BP neural network algorithm is approximately 92%, and the system efficiency is approximately 0.85, which presents obvious advantages when processing big data. The algorithm proposed in this study demonstrated both higher classification accuracy and improved time efficiency, which represents a significant improvement obtained from applying parallel processing to an intelligent algorithm on big data.
Cao, Jianfang; Cui, Hongyan; Shi, Hao; Jiao, Lijuan
2016-01-01
A back-propagation (BP) neural network can solve complicated random nonlinear mapping problems; therefore, it can be applied to a wide range of problems. However, as the sample size increases, the time required to train BP neural networks becomes lengthy. Moreover, the classification accuracy decreases as well. To improve the classification accuracy and runtime efficiency of the BP neural network algorithm, we proposed a parallel design and realization method for a particle swarm optimization (PSO)-optimized BP neural network based on MapReduce on the Hadoop platform using both the PSO algorithm and a parallel design. The PSO algorithm was used to optimize the BP neural network’s initial weights and thresholds and improve the accuracy of the classification algorithm. The MapReduce parallel programming model was utilized to achieve parallel processing of the BP algorithm, thereby solving the problems of hardware and communication overhead when the BP neural network addresses big data. Datasets on 5 different scales were constructed using the scene image library from the SUN Database. The classification accuracy of the parallel PSO-BP neural network algorithm is approximately 92%, and the system efficiency is approximately 0.85, which presents obvious advantages when processing big data. The algorithm proposed in this study demonstrated both higher classification accuracy and improved time efficiency, which represents a significant improvement obtained from applying parallel processing to an intelligent algorithm on big data. PMID:27304987
Optimal mapping of neural-network learning on message-passing multicomputers
NASA Technical Reports Server (NTRS)
Chu, Lon-Chan; Wah, Benjamin W.
1992-01-01
A minimization of learning-algorithm completion time is sought in the present optimal-mapping study of the learning process in multilayer feed-forward artificial neural networks (ANNs) for message-passing multicomputers. A novel approximation algorithm for mappings of this kind is derived from observations of the dominance of a parallel ANN algorithm over its communication time. Attention is given to both static and dynamic mapping schemes for systems with static and dynamic background workloads, as well as to experimental results obtained for simulated mappings on multicomputers with dynamic background workloads.
Electronic neural network for solving traveling salesman and similar global optimization problems
NASA Technical Reports Server (NTRS)
Thakoor, Anilkumar P. (Inventor); Moopenn, Alexander W. (Inventor); Duong, Tuan A. (Inventor); Eberhardt, Silvio P. (Inventor)
1993-01-01
This invention is a novel high-speed neural network based processor for solving the 'traveling salesman' and other global optimization problems. It comprises a novel hybrid architecture employing a binary synaptic array whose embodiment incorporates the fixed rules of the problem, such as the number of cities to be visited. The array is prompted by analog voltages representing variables such as distances. The processor incorporates two interconnected feedback networks, each of which solves part of the problem independently and simultaneously, yet which exchange information dynamically.
Tao, Tao; Zhi, Ting; Liu, Bin; Li, Mingxue; Zhuang, Zhe; Dai, Jiangping; Li, Yi; Jiang, Fulong; Luo, Wenjun; Xie, Zili; Chen, Dunjun; Chen, Peng; Li, Zhaosheng; Zou, Zhigang; Zhang, Rong; Zheng, Youdou
2016-01-01
The photoelectrodes based on III-nitride semiconductors with high energy conversion efficiency especially for those self-driven ones are greatly desirable for hydrogen generation. In this study, highly ordered InGaN/GaN multiple-quantum-well nanorod-based photoelectrodes have been fabricated by a soft UV-curing nano-imprint lithography and a top-down etching technique, which improve the incident photon conversion efficiency (IPCE) from 16% (planar structure) to 42% (@ wavelength = 400 nm). More significantly, the turn-on voltage is reduced low to −0.6 V, which indicates the possibility of achieving self-driven. Furthermore, SiO2/Si3N4 dielectric distributed Bragg reflectors are employed to further improve the IPCE up to 60%. And the photocurrent (@ 1.1 V) is enhanced from 0.37 mA/cm2 (original planar structure) to 1.5 mA/cm2. These improvements may accelerate the possible applications for hydrogen generation with high energy-efficiency. PMID:26853933
Tao, Tao; Zhi, Ting; Liu, Bin; Li, Mingxue; Zhuang, Zhe; Dai, Jiangping; Li, Yi; Jiang, Fulong; Luo, Wenjun; Xie, Zili; Chen, Dunjun; Chen, Peng; Li, Zhaosheng; Zou, Zhigang; Zhang, Rong; Zheng, Youdou
2016-02-08
The photoelectrodes based on III-nitride semiconductors with high energy conversion efficiency especially for those self-driven ones are greatly desirable for hydrogen generation. In this study, highly ordered InGaN/GaN multiple-quantum-well nanorod-based photoelectrodes have been fabricated by a soft UV-curing nano-imprint lithography and a top-down etching technique, which improve the incident photon conversion efficiency (IPCE) from 16% (planar structure) to 42% (@ wavelength = 400 nm). More significantly, the turn-on voltage is reduced low to -0.6 V, which indicates the possibility of achieving self-driven. Furthermore, SiO2/Si3N4 dielectric distributed Bragg reflectors are employed to further improve the IPCE up to 60%. And the photocurrent (@ 1.1 V) is enhanced from 0.37 mA/cm(2) (original planar structure) to 1.5 mA/cm(2). These improvements may accelerate the possible applications for hydrogen generation with high energy-efficiency.
a Hybrid Approach of Neural Network with Particle Swarm Optimization for Tobacco Pests Prediction
NASA Astrophysics Data System (ADS)
Lv, Jiake; Wang, Xuan; Xie, Deti; Wei, Chaofu
Forecasting pests emergence levels plays a significant role in regional crop planting and management. The accuracy, which is derived from the accuracy of the forecasting approach used, will determine the economics of the operation of the pests prediction. Conventional methods including time series, regression analysis or ARMA model entail exogenous input together with a number of assumptions. The use of neural networks has been shown to be a cost-effective technique. But their training, usually with back-propagation algorithm or other gradient algorithms, is featured with some drawbacks such as very slow convergence and easy entrapment in a local minimum. This paper presents a hybrid approach of neural network with particle swarm optimization for developing the accuracy of predictions. The approach is applied to forecast Alternaria alternate Keissl emergence level of the WuLong Country, one of the most important tobacco planting areas in Chongqing. Traditional ARMA model and BP neural network are investigated as comparison basis. The experimental results show that the proposed approach can achieve better prediction performance.
The neural underpinnings of an optimal exploitation of social information under uncertainty.
Toelch, Ulf; Bach, Dominik R; Dolan, Raymond J
2014-11-01
Social information influences decision-making through an integration of information derived from individual experience with that derived from observing the actions of others. This raises the question as to which extent one should utilize social information. One strategy is to make use of uncertainty estimates, leading to a copy-when-uncertain strategy that weights information from individual and social sources based on their respective reliabilities. Here, we investigate this integration process by extending models of Bayes optimal integration of sensory information to a social decision context. We then use a key parameter of our behavioral model in conjunction with functional magnetic resonance imaging to identify the neural substrate that is specifically linked to the fidelity of this integration process. We show that individuals behave near Bayes optimal when integrating two distinct sources of social information but systematically deviate from Bayes optimal choice when integrating individual with social information. This systematic behavioral deviation from optimality is linked to activity of left inferior frontal gyrus. Thus, an ability to optimally exploit social information depends on processes that overcome an egocentric bias, and this regulatory role involves the left inferior prefrontal cortex. The findings provide a mechanistic explanation for observations wherein individuals neglect the benefits from exploiting social information.
The neural underpinnings of an optimal exploitation of social information under uncertainty
Bach, Dominik R.; Dolan, Raymond J.
2014-01-01
Social information influences decision-making through an integration of information derived from individual experience with that derived from observing the actions of others. This raises the question as to which extent one should utilize social information. One strategy is to make use of uncertainty estimates, leading to a copy-when-uncertain strategy that weights information from individual and social sources based on their respective reliabilities. Here, we investigate this integration process by extending models of Bayes optimal integration of sensory information to a social decision context. We then use a key parameter of our behavioral model in conjunction with functional magnetic resonance imaging to identify the neural substrate that is specifically linked to the fidelity of this integration process. We show that individuals behave near Bayes optimal when integrating two distinct sources of social information but systematically deviate from Bayes optimal choice when integrating individual with social information. This systematic behavioral deviation from optimality is linked to activity of left inferior frontal gyrus. Thus, an ability to optimally exploit social information depends on processes that overcome an egocentric bias, and this regulatory role involves the left inferior prefrontal cortex. The findings provide a mechanistic explanation for observations wherein individuals neglect the benefits from exploiting social information. PMID:24194580
Crop classification by forward neural network with adaptive chaotic particle swarm optimization.
Zhang, Yudong; Wu, Lenan
2011-01-01
This paper proposes a hybrid crop classifier for polarimetric synthetic aperture radar (SAR) images. The feature sets consisted of span image, the H/A/α decomposition, and the gray-level co-occurrence matrix (GLCM) based texture features. Then, the features were reduced by principle component analysis (PCA). Finally, a two-hidden-layer forward neural network (NN) was constructed and trained by adaptive chaotic particle swarm optimization (ACPSO). K-fold cross validation was employed to enhance generation. The experimental results on Flevoland sites demonstrate the superiority of ACPSO to back-propagation (BP), adaptive BP (ABP), momentum BP (MBP), Particle Swarm Optimization (PSO), and Resilient back-propagation (RPROP) methods. Moreover, the computation time for each pixel is only 1.08 × 10(-7) s.
NASA Astrophysics Data System (ADS)
Ghaffari Razin, Mir Reza; Voosoghi, Behzad
2016-11-01
Wavelet neural networks (WNNs) are a new class of neural networks (NNs) that has been developed using a combined method of multi-layer artificial neural networks and wavelet analysis (WA). In this paper, WNNs is used for modeling and prediction of total electron content (TEC) of ionosphere with high spatial and temporal resolution. Generally, back-propagation (BP) algorithm is used to train the neural network. While this algorithm proves to be very effective and robust in training many types of network structures, it suffers from certain disadvantages such as easy entrapment in a local minimum and slow convergence. To improve the performance of WNN in training step, the adjustment of network weights using particle swarm optimization (PSO) was proposed. The results obtained in this paper were compared with standard NN (SNN) by BP training algorithm (SNN-BP), SNN by PSO training algorithm (SNN-PSO) and WNN by BP training algorithm (WNN-BP). For numerical experiments, observations collected at 36 GPS stations in 5 days of 2012 from Iranian permanent GPS network (IPGN) are used. The average minimum relative errors in 5 test stations for WNN-PSO, WNN-BP, SNN-BP and SNN-PSO compared with GPS TEC are 10.59%, 12.85%, 13.18%, 13.75% and average maximum relative errors are 14.70%, 17.30%, 18.53% and 20.83%, respectively. Comparison of diurnal predicted TEC values from the WNN-PSO, SNN-BP, SNN-PSO and WNN-BP models with GPS TEC revealed that the WNN-PSO provides more accurate predictions than the other methods in the test area.
NASA Astrophysics Data System (ADS)
Lazzús, J. A.; López-Caraballo, C. H.; Rojas, P.; Salfate, I.; Rivera, M.; Palma-Chilla, L.
2016-05-01
In this study, an artificial neural network was optimized with particle swarm algorithm and trained to predict the geomagmetic DST index one hour ahead using the past values of DST and auroral electrojet indices. The results show that the proposed neural network model can be properly trained for predicting of DST(t + 1) with acceptable accuracy, and that the geomagnetic indices used have influential effects on the good training and predicting capabilities of the chosen network.
Fast Back Propagation Learning Using Optimization of Learning Rate for Pulsed Neural Networks
NASA Astrophysics Data System (ADS)
Yamamoto, Kenji; Koakutsu, Seiichi; Okamoto, Takashi; Hirata, Hironori
Neural Networks (NN) are widely applied to information processing because of its nonlinear processing capability. Digital hardware implementation of NN seems to be effective in construction of NN systems in which real-time operation and much further wide applications are possible. However, the digital hardware implementation of analogue NN is very difficult because we have to fulfill the restrictions about circuit resource, such as circuit scale, arrangement, and wiring. A technique that uses pulsed neuron model instead of analogue neuron model as a method of solving this problem has been proposed, and its effectiveness has been confirmed. To construct Pulsed Neural Networks (PNN), Back Propagation (BP) learning has been proposed. However, BP learning takes much time to construct PNN compared with the learning of analogue NN. Therefore some method to speed up BP learning of PNN is necessary. In this paper, we propose a fast BP learning using optimization of learning rate for PNN. In the proposed method, the learning rate is optimized so as to speed up the learning at every learning epoch. To evaluate the proposed method, we apply it to some pattern recognition problems, such as XOR, 3-bits parity, and digit recognition. Results of computational experiments indicate the validity of the proposed method.
Poultangari, Iman; Shahnazi, Reza; Sheikhan, Mansour
2012-09-01
In order to control the pitch angle of blades in wind turbines, commonly the proportional and integral (PI) controller due to its simplicity and industrial usability is employed. The neural networks and evolutionary algorithms are tools that provide a suitable ground to determine the optimal PI gains. In this paper, a radial basis function (RBF) neural network based PI controller is proposed for collective pitch control (CPC) of a 5-MW wind turbine. In order to provide an optimal dataset to train the RBF neural network, particle swarm optimization (PSO) evolutionary algorithm is used. The proposed method does not need the complexities, nonlinearities and uncertainties of the system under control. The simulation results show that the proposed controller has satisfactory performance. PMID:22738782
Poultangari, Iman; Shahnazi, Reza; Sheikhan, Mansour
2012-09-01
In order to control the pitch angle of blades in wind turbines, commonly the proportional and integral (PI) controller due to its simplicity and industrial usability is employed. The neural networks and evolutionary algorithms are tools that provide a suitable ground to determine the optimal PI gains. In this paper, a radial basis function (RBF) neural network based PI controller is proposed for collective pitch control (CPC) of a 5-MW wind turbine. In order to provide an optimal dataset to train the RBF neural network, particle swarm optimization (PSO) evolutionary algorithm is used. The proposed method does not need the complexities, nonlinearities and uncertainties of the system under control. The simulation results show that the proposed controller has satisfactory performance.
Inversion of 2-D DC resistivity data using rapid optimization and minimal complexity neural network
NASA Astrophysics Data System (ADS)
Singh, U. K.; Tiwari, R. K.; Singh, S. B.
2010-02-01
The backpropagation (BP) artificial neural network (ANN) technique of optimization based on steepest descent algorithm is known to be inept for its poor performance and does not ensure global convergence. Nonlinear and complex DC resistivity data require efficient ANN model and more intensive optimization procedures for better results and interpretations. Improvements in the computational ANN modeling process are described with the goals of enhancing the optimization process and reducing ANN model complexity. Well-established optimization methods, such as Radial basis algorithm (RBA) and Levenberg-Marquardt algorithms (LMA) have frequently been used to deal with complexity and nonlinearity in such complex geophysical records. We examined here the efficiency of trained LMA and RB networks by using 2-D synthetic resistivity data and then finally applied to the actual field vertical electrical resistivity sounding (VES) data collected from the Puga Valley, Jammu and Kashmir, India. The resulting ANN reconstruction resistivity results are compared with the result of existing inversion approaches, which are in good agreement. The depths and resistivity structures obtained by the ANN methods also correlate well with the known drilling results and geologic boundaries. The application of the above ANN algorithms proves to be robust and could be used for fast estimation of resistive structures for other complex earth model also.
Chang, P.S.; Poston, J.
1996-07-01
Boiler performance optimization includes the preservation of efficiency, emission, capacity, and reliability. Competitive pressures require cost reduction and environmental compliance. It is a challenge for utility personnel to balance these requirements and to achieve specific company goals. Unfortunately, these requirements often demand tradeoffs. The Clean Air Act Amendment requires Utilities to reduce NO{sub x} emission. NO{sub x} emission reduction has often been accomplished by installation of new low NO{sub x} burners. Boiler tuning for NO{sub x} control can be used as an alternative to low NO{sub x} burner installation. A PC-based computer software program was developed to assist the tuning process. This software, System Optimization Analysis Program (SOAP), is a neural network based code which uses the self-adaptation learning process, with an adaptive filter added for data noise control. SOAP can use historical data as the knowledge base and it provides a fast optimal solution to adaptive control problems. SOAP was tested at several fossil plants. The tests were primarily for NO{sub x} reduction, but the performance parameters were optimized simultaneously.
Xu, Hao; Jagannathan, Sarangapani
2015-03-01
The stochastic optimal control of nonlinear networked control systems (NNCSs) using neuro-dynamic programming (NDP) over a finite time horizon is a challenging problem due to terminal constraints, system uncertainties, and unknown network imperfections, such as network-induced delays and packet losses. Since the traditional iteration or time-based infinite horizon NDP schemes are unsuitable for NNCS with terminal constraints, a novel time-based NDP scheme is developed to solve finite horizon optimal control of NNCS by mitigating the above-mentioned challenges. First, an online neural network (NN) identifier is introduced to approximate the control coefficient matrix that is subsequently utilized in conjunction with the critic and actor NNs to determine a time-based stochastic optimal control input over finite horizon in a forward-in-time and online manner. Eventually, Lyapunov theory is used to show that all closed-loop signals and NN weights are uniformly ultimately bounded with ultimate bounds being a function of initial conditions and final time. Moreover, the approximated control input converges close to optimal value within finite time. The simulation results are included to show the effectiveness of the proposed scheme. PMID:25720004
A stable and optimized neural network model for crash injury severity prediction.
Zeng, Qiang; Huang, Helai
2014-12-01
The study proposes a convex combination (CC) algorithm to fast and stably train a neural network (NN) model for crash injury severity prediction, and a modified NN pruning for function approximation (N2PFA) algorithm to optimize the network structure. To demonstrate the proposed approaches and to compare them with the NN trained by traditional back-propagation (BP) algorithm and an ordered logit (OL) model, a two-vehicle crash dataset in 2006 provided by the Florida Department of Highway Safety and Motor Vehicles (DHSMV) was employed. According to the results, the CC algorithm outperforms the BP algorithm both in convergence ability and training speed. Compared with a fully connected NN, the optimized NN contains much less network nodes and achieves comparable classification accuracy. Both of them have better fitting and predicting performance than the OL model, which again demonstrates the NN's superiority over statistical models for predicting crash injury severity. The pruned input nodes also justify the ability of the structure optimization method for identifying the factors irrelevant to crash-injury outcomes. A sensitivity analysis of the optimized NN is further conducted to determine the explanatory variables' impact on each injury severity outcome. While most of the results conform to the coefficient estimation in the OL model and previous studies, some variables are found to have non-linear relationships with injury severity, which further verifies the strength of the proposed method.
Fjodorova, Natalja; Novič, Marjana
2015-09-01
Engineering optimization is an actual goal in manufacturing and service industries. In the tutorial we represented the concept of traditional parametric estimation models (Factorial Design (FD) and Central Composite Design (CCD)) for searching optimal setting parameters of technological processes. Then the 2D mapping method based on Auto Associative Neural Networks (ANN) (particularly, the Feed Forward Bottle Neck Neural Network (FFBN NN)) was described in comparison with traditional methods. The FFBN NN mapping technique enables visualization of all optimal solutions in considered processes due to the projection of input as well as output parameters in the same coordinates of 2D map. This phenomenon supports the more efficient way of improving the performance of existing systems. Comparison of two methods was performed on the bases of optimization of solder paste printing processes as well as optimization of properties of cheese. Application of both methods enables the double check. This increases the reliability of selected optima or specification limits. PMID:26388367
NASA Astrophysics Data System (ADS)
Zhou, Shiqiong; Kang, Longyun; Cheng, Miaomiao; Cao, Binggang
Owing to sun's rays distributing randomly and discontinuously and load fluctuation, energy storage system is very important in Solar Energy Electric Vehicle (SEEV). The combinatorial optimization by genetic algorithm and neural network was used to optimize the energy storage system (including storage batteries and flywheel).In the optimization design, the operation strategy of the system was fixed and used to instruct the simulation about the system's operation. And the optimal objective was selected as minimizing the total capital cost of the energy storage system, subject to the main constraint of the Loss of Power Supply Probability (LPSP). Studies have proved that the combinatorial optimization by genetic algorithm and neural network converges well, lessen calculation time and it is feasible.
Fjodorova, Natalja; Novič, Marjana
2015-09-01
Engineering optimization is an actual goal in manufacturing and service industries. In the tutorial we represented the concept of traditional parametric estimation models (Factorial Design (FD) and Central Composite Design (CCD)) for searching optimal setting parameters of technological processes. Then the 2D mapping method based on Auto Associative Neural Networks (ANN) (particularly, the Feed Forward Bottle Neck Neural Network (FFBN NN)) was described in comparison with traditional methods. The FFBN NN mapping technique enables visualization of all optimal solutions in considered processes due to the projection of input as well as output parameters in the same coordinates of 2D map. This phenomenon supports the more efficient way of improving the performance of existing systems. Comparison of two methods was performed on the bases of optimization of solder paste printing processes as well as optimization of properties of cheese. Application of both methods enables the double check. This increases the reliability of selected optima or specification limits.
2014-01-01
An extensive study on molecular beam epitaxy growth conditions of quaternary GaAsSbN as a capping layer (CL) for InAs/GaAs quantum dots (QD) was carried out. In particular, CL thickness, growth temperature, and growth rate were optimized. Problems related to the simultaneous presence of Sb and N, responsible for a significant degradation of photoluminescence (PL), are thereby solved allowing the achievement of room-temperature (RT) emission. A particularly strong improvement on the PL is obtained when the growth rate of the CL is increased. This is likely due to an improvement in the structural quality of the quaternary alloy that resulted from reduced strain and composition inhomogeneities. Nevertheless, a significant reduction of Sb and N incorporation was found when the growth rate was increased. Indeed, the incorporation of N is intrinsically limited to a maximum value of approximately 1.6% when the growth rate is at 2.0 ML s−1. Therefore, achieving RT emission and extending it somewhat beyond 1.3 μm were possible by means of a compromise among the growth conditions. This opens the possibility of exploiting the versatility on band structure engineering offered by this QD-CL structure in devices working at RT. PACS 81.15.Hi (molecular beam epitaxy); 78.55.Cr (III-V semiconductors); 73.21.La (quantum dots) PMID:24438542
NASA Astrophysics Data System (ADS)
Francke, Till; Bronster, Axel; Shoemaker, Christine A.
2010-05-01
Calibrating complex hydrological models faces two major challenges: firstly, extended models, especially when spatially distributed, encompass a large number of parameters with different (and possibly a-priori unknown) sensitivity. Due to the usually rough surface of the objective function, this aggravates the risk of an algorithm to converge in a local optimum. Thus, gradient-based optimization methods are often bound to fail without a very good prior estimate. Secondly, despite growing computational power, it is not uncommon that models of large extent in space or time take several minutes to run, which severely restricts the total number of model evaluations under given computational and time resources. While various heuristic methods successfully address the first challenge, they tend to conflict with the second challenge due to the increased number of evaluations necessary. In that context we analyzed three methods (Dynamically Dimensioned Search / DDS, Particle Swarm Optimization / PSO, Genetic Algorithms /GA). We performed tests with common "synthetic" objective functions and a calibration of the hydrological model WASA-SED with different number of parameters. When looking at the reduction of the objective function within few (i.e.< 1000) evaluations, the methods generally perform in the order (best to worst) DDS-PSO-GA. Only at a larger number, GA can excel. To speed up optimization, we executed DDS and PSO as parallel applications, i.e. using multiple CPUs and/or computers. The parallelisation has been implemented in the ppso-package for the free computation environment R. Special focus has been laid onto the options to resume interrupted optimization runs and visualize progress.
Cervera, C.; Rodriguez, J. B.; Perez, J. P.; Aiet-Kaci, H.; Chaghi, R.; Christol, P.; Konczewicz, L.; Contreras, S.
2009-08-01
In this communication we report on electrical properties of nonintentionally doped (nid) type II InAs/GaSb superlattice grown by molecular beam epitaxy. We present a simple technological process which, thanks to the suppression of substrate, allows direct Hall measurement on superlattice structures grown on conductive GaSb substrate. Two samples were used to characterize the transport: one grown on a semi-insulating GaAs substrate and another grown on n-GaSb substrate where a etch stop layer was inserted to remove the conductive substrate. Mobilities and carrier concentrations have been measured as a function of temperature (77-300 K), and compared with capacitance-voltage characteristic at 80 K of a photodiode comprising a similar nid superlattice.
Modares, Hamidreza; Lewis, Frank L; Naghibi-Sistani, Mohammad-Bagher
2013-10-01
This paper presents an online policy iteration (PI) algorithm to learn the continuous-time optimal control solution for unknown constrained-input systems. The proposed PI algorithm is implemented on an actor-critic structure where two neural networks (NNs) are tuned online and simultaneously to generate the optimal bounded control policy. The requirement of complete knowledge of the system dynamics is obviated by employing a novel NN identifier in conjunction with the actor and critic NNs. It is shown how the identifier weights estimation error affects the convergence of the critic NN. A novel learning rule is developed to guarantee that the identifier weights converge to small neighborhoods of their ideal values exponentially fast. To provide an easy-to-check persistence of excitation condition, the experience replay technique is used. That is, recorded past experiences are used simultaneously with current data for the adaptation of the identifier weights. Stability of the whole system consisting of the actor, critic, system state, and system identifier is guaranteed while all three networks undergo adaptation. Convergence to a near-optimal control law is also shown. The effectiveness of the proposed method is illustrated with a simulation example. PMID:24808590
Liu, Feng; Liu, Wenhui; Tian, Shuge
2014-09-01
A combination of an orthogonal L16(4)4 test design and a three-layer artificial neural network (ANN) model was applied to optimize polysaccharides from Althaea rosea seeds extracted by hot water method. The highest optimal experimental yield of A. rosea seed polysaccharides (ARSPs) of 59.85 mg/g was obtained using three extraction numbers, 113 min extraction time, 60.0% ethanol concentration, and 1:41 solid-liquid ratio. Under these optimized conditions, the ARSP experimental yield was very close to the predicted yield of 60.07 mg/g and was higher than the orthogonal test results (40.86 mg/g). Structural characterizations were conducted using physicochemical property and FTIR analysis. In addition, the study of ARSP antioxidant activity demonstrated that polysaccharides exhibited high superoxide dismutase activity, strong reducing power, and positive scavenging activity on superoxide anion, hydroxyl radical, 2,2-diphenyl-1-picrylhydrazyl, and reducing power. Our results indicated that ANNs were efficient quantitative tools for predicting the total ARSP content.
Neural network-based optimal adaptive output feedback control of a helicopter UAV.
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. PMID:24808521
A new Jacobian matrix for optimal learning of single-layer neural networks.
Peng, Jian Xun; Li, Kang; Irwin, George W
2008-01-01
This paper investigates the learning of a wide class of single-hidden-layer feedforward neural networks (SLFNs) with two sets of adjustable parameters, i.e., the nonlinear parameters in the hidden nodes and the linear output weights. The main objective is to both speed up the convergence of second-order learning algorithms such as Levenberg-Marquardt (LM), as well as to improve the network performance. This is achieved here by reducing the dimension of the solution space and by introducing a new Jacobian matrix. Unlike conventional supervised learning methods which optimize these two sets of parameters simultaneously, the linear output weights are first converted into dependent parameters, thereby removing the need for their explicit computation. Consequently, the neural network (NN) learning is performed over a solution space of reduced dimension. A new Jacobian matrix is then proposed for use with the popular second-order learning methods in order to achieve a more accurate approximation of the cost function. The efficacy of the proposed method is shown through an analysis of the computational complexity and by presenting simulation results from four different examples. PMID:18269943
NASA Astrophysics Data System (ADS)
Gao, Xin-jiang; Tang, Zun-lie; Zhang, Xiu-chuan; Chen, Yang; Jiang, Li-qun; Cheng, Hong-bing
2009-07-01
Significant progress has been achieved in technology of the InGaAs focal plane arrays (FPA) detector operating in short wave infrared (SWIR) last two decades. The no cryogenic cooling, low manufacturing cost, low power, high sensitivity and maneuverability features inherent of InGaAs FPA make it as a mainstream SWIR FPA in a variety of critical military, national security, aerospace, telecommunications and industrial applications. These various types of passive image sensing or active illumination image detecting systems included range-gated imaging, 3-Dimensional Ladar, covert surveillance, pulsed laser beam profiling, machine vision, semiconductor inspection, free space optical communications beam tracker, hyperspectroscopy imaging and many others. In this paper the status and perspectives of hybrid InGaAs FPA which is composed of detector array (PDA) and CMOS readout integrate circuit (ROIC) are reviewed briefly. For various low light levels applications such as starlight or night sky illumination, we have made use of the interface circuit of capacitive feedback transimpedance amplifier (CTIA) in which the integration capacitor was adjustable, therefore implements of the physical and electrical characteristics matches between detector arrays and readout intergrate circuit was achieved excellently. Taking into account the influences of InGaAs detector arrays' optoelectronic characteristics on performance of the FPA, we discussed the key parameters of the photodiode in detailed, and the tradeoff between the responsivity, dark current, impedance at zero bias and junction capacitance of photosensitive element has been made to root out the impact factors. As a result of the educed approach of the photodiode's characteristics optimizing which involve with InGaAs PDA design and process, a high performance InGaAs FPA of 30um pixel pitch and 320×256 format has been developed of which the response spectrum range over 0.9um to 1.7um, the mean peak detectivity (λ=1.55
Mackey-Glass noisy chaotic time series prediction by a swarm-optimized neural network
NASA Astrophysics Data System (ADS)
López-Caraballo, C. H.; Salfate, I.; Lazzús, J. A.; Rojas, P.; Rivera, M.; Palma-Chilla, L.
2016-05-01
In this study, an artificial neural network (ANN) based on particle swarm optimization (PSO) was developed for the time series prediction. The hybrid ANN+PSO algorithm was applied on Mackey-Glass noiseless chaotic time series in the short-term and long-term prediction. The performance prediction is evaluated and compared with similar work in the literature, particularly for the long-term forecast. Also, we present properties of the dynamical system via the study of chaotic behaviour obtained from the time series prediction. Then, this standard hybrid ANN+PSO algorithm was complemented with a Gaussian stochastic procedure (called stochastic hybrid ANN+PSO) in order to obtain a new estimator of the predictions that also allowed us compute uncertainties of predictions for noisy Mackey-Glass chaotic time series. We study the impact of noise for three cases with a white noise level (σ N ) contribution of 0.01, 0.05 and 0.1.
Missile Guidance Law Based on Robust Model Predictive Control Using Neural-Network Optimization.
Li, Zhijun; Xia, Yuanqing; Su, Chun-Yi; Deng, Jun; Fu, Jun; He, Wei
2015-08-01
In this brief, the utilization of robust model-based predictive control is investigated for the problem of missile interception. Treating the target acceleration as a bounded disturbance, novel guidance law using model predictive control is developed by incorporating missile inside constraints. The combined model predictive approach could be transformed as a constrained quadratic programming (QP) problem, which may be solved using a linear variational inequality-based primal-dual neural network over a finite receding horizon. Online solutions to multiple parametric QP problems are used so that constrained optimal control decisions can be made in real time. Simulation studies are conducted to illustrate the effectiveness and performance of the proposed guidance control law for missile interception.
Learning and optimization with cascaded VLSI neural network building-block chips
NASA Technical Reports Server (NTRS)
Duong, T.; Eberhardt, S. P.; Tran, M.; Daud, T.; Thakoor, A. P.
1992-01-01
To demonstrate the versatility of the building-block approach, two neural network applications were implemented on cascaded analog VLSI chips. Weights were implemented using 7-b multiplying digital-to-analog converter (MDAC) synapse circuits, with 31 x 32 and 32 x 32 synapses per chip. A novel learning algorithm compatible with analog VLSI was applied to the two-input parity problem. The algorithm combines dynamically evolving architecture with limited gradient-descent backpropagation for efficient and versatile supervised learning. To implement the learning algorithm in hardware, synapse circuits were paralleled for additional quantization levels. The hardware-in-the-loop learning system allocated 2-5 hidden neurons for parity problems. Also, a 7 x 7 assignment problem was mapped onto a cascaded 64-neuron fully connected feedback network. In 100 randomly selected problems, the network found optimal or good solutions in most cases, with settling times in the range of 7-100 microseconds.
NASA Astrophysics Data System (ADS)
Zhu, Di
2011-12-01
-current efficiency and reduced efficiency droop. Compared with 4-QB-doped LEDs, 1-QB-doped LEDs show a 37.5% increase in light-output power at high currents. Consistent with the measurements, simulation shows a shift of radiative recombination among the MQWs and a reduced electron leakage current into the p-type GaN when fewer QBs are doped. The results can be attributed to a more symmetric carrier transport and uniform carrier distribution which help to reduce electron leakage and thus reduce the efficiency droop. In this dissertation, artificial evolution is introduced to the LED optimization process which combines a genetic algorithm (GA) and device-simulation software. We show that this approach is capable of generating novel concepts in designing and optimizing LED devices. Application of the GA to the QB-doping in the MQWs yields optimized structures which is consistent with the tailored QB doping experiments. Application of the GA to the EBL region suggests a novel structure with an inverted sheet charge at the spacer-EBL interface. The resulting repulsion of electrons can significantly reduce electron leakage and enhance the efficiency. Finally, dual-wavelength LEDs, which have two types of quantum wells (QWs) emitting at two different wavelengths, are experimentally characterized and compared with numerical simulations. These dual-wavelength LEDs allow us to determine which QW emits most of the light. An experimental observation and a quantitative analysis of the radiative recombination shift within the MQW active region are obtained. In addition, an injection-current dependence of the radiative recombination shift is predicted by numerical simulations and indeed observed in dual-wavelength LEDs. This injection-current dependence of the radiative recombination distribution can be explained very well by incorporating quantum-mechanical tunneling of carriers into and through the QBs into to the classical drift-diffusion model. In summary, using the LEDs with tailored QB
Li Dechun; Zhao Shengzhi; Li Guiqiu; Yang Kejian
2007-08-20
A doubly Q-switched laser with both an acousto-optic (AO) modulator and a GaAs saturable absorber can obtain a more symmetric and shorter pulse with high pulse peak power, which has been experimentally proved. The key parameters of an optimally coupled doubly Q-switched laser with both an AO modulator and a GaAs saturable absorber are determined, and a group of general curves are generated for what we believe is the first time, when the single-photon absorption (SPA) and two-photon absorption (TPA) processes of GaAs are combined, and the Gaussian spatial distributions of the intracavity photon density and the initial population-inversion density as well as the influence of the AO Q-switch are considered. These key parameters include the optimal normalized coupling parameter, the optimal normalized GaAs saturable absorber parameters, and the normalized parameters of the AO Q-switch, which can maximize the output energy. Meanwhile, the corresponding normalized energy, the normalized peak power, and the normalized pulse width are given. The curves clearly show the dependence of the optimal key parameters on the parameters of the gain medium, the GaAs saturable absorber,the AO Q-switch, and the resonator. Sample calculations for a diode-pumpedNd3+:YVO4 laser with both an AO modulator and a GaAs saturable absorber are presented to demonstrate the use of the curves and the relevant formulas.
Li, Dechun; Zhao, Shengzhi; Li, Guiqiu; Yang, Kejian
2007-08-20
A doubly Q-switched laser with both an acousto-optic (AO) modulator and a GaAs saturable absorber can obtain a more symmetric and shorter pulse with high pulse peak power, which has been experimentally proved. The key parameters of an optimally coupled doubly Q-switched laser with both an AO modulator and a GaAs saturable absorber are determined, and a group of general curves are generated for what we believe is the first time, when the single-photon absorption (SPA) and two-photon absorption (TPA) processes of GaAs are combined, and the Gaussian spatial distributions of the intracavity photon density and the initial population-inversion density as well as the influence of the AO Q-switch are considered. These key parameters include the optimal normalized coupling parameter, the optimal normalized GaAs saturable absorber parameters, and the normalized parameters of the AO Q-switch, which can maximize the output energy. Meanwhile, the corresponding normalized energy, the normalized peak power, and the normalized pulse width are given. The curves clearly show the dependence of the optimal key parameters on the parameters of the gain medium, the GaAs saturable absorber, the AO Q-switch, and the resonator. Sample calculations for a diode-pumped Nd3+:YVO4 laser with both an AO modulator and a GaAs saturable absorber are presented to demonstrate the use of the curves and the relevant formulas.
Chen, Ho-Wen; Ning, Shu-Kuang; Yu, Ruey-Fang; Hung, Ming-Sung
2007-02-01
This paper applies artificial neural network (ANN) to model the observed effluent quality data. The ANN's structure, involving the number of hidden layer and node and their connection, is determined endogenously by resorting to the compromise of data cost minimization and prediction accuracy maximization. To obtain the best compromise possible, the model introduces an aspiration variable (micro) that represents the level of aspiration achieved in one objective and the conjugate of micro, (1 - micro), represents level of aspiration achieved in the other objective. Because a massive amount of calculation is required, the model applies genetic algorithm (GA) for its computational flexibility and capability to ensure global solution. Feasibility and practicality of the model is tested by a case study with a set of 150 daily observations on 17 operational variables and quality parameters at an industrial wastewater treatment plant (WTP) located in southern Taiwan. Of these 17 variables open to selection, only 6 variables, wastewater flow rate (Q), CN(-), SS, MLSS, pH and COD are selected by the model to achieve the maximum accuracy of prediction, 0.94, with a total cost of 5,950 NT$. By constraining budget availability, the variables included in the model are reduced in number, causing a concomitant reduction in prediction accuracy, that is, by varying micro (aspiration level of accuracy), a trajectory of cost and accuracy is generated. The calculation results a cost of 3,650 NT$ and 0.54 accuracy for the case with variables including flow rate, SCN(-) and SS in equalization basin; aeration tank hydraulic retention time (HRT) and percentage of returned sludge (R%) are selected for building the prediction model when the importance of required budget is equal to the accuracy of prediction model. In addition, when required cost for building ANN model is between 3,650 NT$ and 3,900 NT$, the marginal return of budget input is highest in the entire range of calculation.
Ghai, Aanchal; Singh, Baljinder; Panwar Hazari, Puja; Schultz, Michael K; Parmar, Ambika; Kumar, Pardeep; Sharma, Sarika; Dhawan, Devinder; Kumar Mishra, Anil
2015-11-01
The present study describes the optimization of (68)Ga radiolabeling with PAMAM dendrimer-DOTA conjugate. A conjugate (PAMAM-DOTA) concentration of 11.69µM, provided best radiolabeling efficiency of more than 93.0% at pH 4.0, incubation time of 30.0min and reaction temperature ranging between 90 and 100°C. The decay corrected radiochemical yield was found to be 79.4±0.01%. The radiolabeled preparation ([(68)Ga]-DOTA-PAMAM-D) remained stable (radiolabeling efficiency of 96.0%) at room temperature and in serum for up to 4-h. The plasma protein binding was observed to be 21.0%. After intravenous administration, 50.0% of the tracer cleared from the blood circulation by 30-min and less than 1.0% of the injected activity remained in blood by 1.0h. The animal biodistribution studies demonstrated that the tracer excretes through the kidneys and about 0.33% of the %ID/g accumulated in the tumor at 1h post injection. The animal organ's biodistribution data was supported by animal PET imaging showing good 'non-specific' tracer uptake in tumor and excretion is primarily through kidneys. Additionally, DOTA-PAMAM-D conjugation with αVβ3 receptors targeting peptides and drug loading on the dendrimers may improve the specificity of the (68)Ga labeled product for imaging and treating angiogenesis respectively. PMID:26232562
OCReP: An Optimally Conditioned Regularization for pseudoinversion based neural training.
Cancelliere, Rossella; Gai, Mario; Gallinari, Patrick; Rubini, Luca
2015-11-01
In this paper we consider the training of single hidden layer neural networks by pseudoinversion, which, in spite of its popularity, is sometimes affected by numerical instability issues. Regularization is known to be effective in such cases, so that we introduce, in the framework of Tikhonov regularization, a matricial reformulation of the problem which allows us to use the condition number as a diagnostic tool for identification of instability. By imposing well-conditioning requirements on the relevant matrices, our theoretical analysis allows the identification of an optimal value for the regularization parameter from the standpoint of stability. We compare with the value derived by cross-validation for overfitting control and optimization of the generalization performance. We test our method for both regression and classification tasks. The proposed method is quite effective in terms of predictivity, often with some improvement on performance with respect to the reference cases considered. This approach, due to analytical determination of the regularization parameter, dramatically reduces the computational load required by many other techniques.
NASA Astrophysics Data System (ADS)
Takeda, Norio
We verified the generalization ability of the response surfaces of artificial neural networks (NNs), and that the surfaces could be applied to an engineering-design problem. A Bayesian framework to regularize NNs, which was proposed by Gull and Skilling, can be used to generate NN response surfaces with excellent generalization ability, i.e., to determine the regularizing constants in an objective function minimized during NN learning. This well-generalized NN might be useful to find an optimal solution in the process of response surface methodology (RSM). We, therefore, describe three rules based on the Bayesian framework to update the regularizing constants, utilizing these rules to generate NN response surfaces with noisy teacher data drawn from a typical unimodal or multimodal function. Good generalization ability was achieved with regularized NN response surfaces, even though an update rule including trace evaluation failed to determine the regularizing constants regardless of the response function. We, next, selected the most appropriate update rule, which included eigenvalue evaluation, and then the NN response surface regularized using the update rule was applied to finding the optimal solution to an illustrative engineering-design problem. The NN response surface did not fit the noise in the teacher data, and consequently, it could effectively be used to achieve a satisfactory solution. This may increase the opportunities for using NN in the process of RSM.
Songmuang, R; Giang, Le Thuy Thanh; Bleuse, J; Den Hertog, M; Niquet, Y M; Dang, Le Si; Mariette, H
2016-06-01
We present a set of experimental results showing a combination of various effects, that is, surface recombination velocity, surface charge traps, strain, and structural defects, that govern the carrier dynamics of self-catalyzed GaAs/AlGaAs core-shell nanowires (NWs) grown on a Si(111) substrate by molecular beam epitaxy. Time-resolved photoluminescence of NW ensemble and spatially resolved cathodoluminescence of single NWs reveal that emission intensity, decay time, and carrier diffusion length of the GaAs NW core strongly depend on the AlGaAs shell thickness but in a nonmonotonic fashion. Although 7 nm AlGaAs shell can efficiently suppress the surface recombination velocity of the GaAs NW core, the influence of the surface charge traps and the strain between the core and the shell that redshift the luminescence of the GaAs NW core remain observable in the whole range of the shell thickness. In addition, the band bending effect induced by the surface charge traps can alter the scattering of the excess carriers inside the GaAs NW core at the core/shell interface. If the AlGaAs shell thickness is larger than 50 nm, the luminescence efficiency of the GaAs NW cores deteriorates, ascribed to defect formation inside the AlGaAs shell evidenced by transmission electron microscopy.
Songmuang, R; Giang, Le Thuy Thanh; Bleuse, J; Den Hertog, M; Niquet, Y M; Dang, Le Si; Mariette, H
2016-06-01
We present a set of experimental results showing a combination of various effects, that is, surface recombination velocity, surface charge traps, strain, and structural defects, that govern the carrier dynamics of self-catalyzed GaAs/AlGaAs core-shell nanowires (NWs) grown on a Si(111) substrate by molecular beam epitaxy. Time-resolved photoluminescence of NW ensemble and spatially resolved cathodoluminescence of single NWs reveal that emission intensity, decay time, and carrier diffusion length of the GaAs NW core strongly depend on the AlGaAs shell thickness but in a nonmonotonic fashion. Although 7 nm AlGaAs shell can efficiently suppress the surface recombination velocity of the GaAs NW core, the influence of the surface charge traps and the strain between the core and the shell that redshift the luminescence of the GaAs NW core remain observable in the whole range of the shell thickness. In addition, the band bending effect induced by the surface charge traps can alter the scattering of the excess carriers inside the GaAs NW core at the core/shell interface. If the AlGaAs shell thickness is larger than 50 nm, the luminescence efficiency of the GaAs NW cores deteriorates, ascribed to defect formation inside the AlGaAs shell evidenced by transmission electron microscopy. PMID:27081785
Energy-efficient waveform shapes for neural stimulation revealed with a genetic algorithm
NASA Astrophysics Data System (ADS)
Wongsarnpigoon, Amorn; Grill, Warren M.
2010-08-01
The energy efficiency of stimulation is an important consideration for battery-powered implantable stimulators. We used a genetic algorithm (GA) to determine the energy-optimal waveform shape for neural stimulation. The GA was coupled to a computational model of extracellular stimulation of a mammalian myelinated axon. As the GA progressed, waveforms became increasingly energy efficient and converged upon an energy-optimal shape. The results of the GA were consistent across several trials, and resulting waveforms resembled truncated Gaussian curves. When constrained to monophasic cathodic waveforms, the GA produced waveforms that were symmetric about the peak, which occurred approximately during the middle of the pulse. However, when the cathodic waveforms were coupled to rectangular charge-balancing anodic pulses, the location and sharpness of the peak varied with the duration and timing (i.e., before or after the cathodic phase) of the anodic phase. In a model of a population of mammalian axons and in vivo experiments on a cat sciatic nerve, the GA-optimized waveforms were more energy efficient and charge efficient than several conventional waveform shapes used in neural stimulation. If used in implantable neural stimulators, GA-optimized waveforms could prolong battery life, thereby reducing the frequency of recharge intervals, the volume of implanted pulse generators, and the costs and risks of battery-replacement surgeries.
NASA Astrophysics Data System (ADS)
Ahmad, Nafis; Tanaka, Tomohisa; Saito, Yoshio
For efficient use of machine tools at optimum cutting condition, it is necessary to find a suitable optimization method, which can find optimum feasible solution rapidly and explain the constraints as well. As the actual turning process parameter optimization is highly constrained and nonlinear, a modified Genetic Algorithm with Self Organizing Adaptive Penalty (SOAP) strategy is used to find the optimum cutting condition and to get clear idea of constraints at the optimum condition. Unit production cost is the objective function while limits of the cutting force, power, surface finish, stability condition, tool-chip interface temperature and available rotational speed in the machine tool are considered as the constraints. The result shows that our approach of GA with SOAP converges quickly by focusing on the boundary of the feasible and infeasible solution space created by constraints and also identifies the critical and non-critical constraints at the optimum condition.
Neural network river forecasting through baseflow separation and binary-coded swarm optimization
NASA Astrophysics Data System (ADS)
Taormina, Riccardo; Chau, Kwok-Wing; Sivakumar, Bellie
2015-10-01
The inclusion of expert knowledge in data-driven streamflow modeling is expected to yield more accurate estimates of river quantities. Modular models (MMs) designed to work on different parts of the hydrograph are preferred ways to implement such approach. Previous studies have suggested that better predictions of total streamflow could be obtained via modular Artificial Neural Networks (ANNs) trained to perform an implicit baseflow separation. These MMs fit separately the baseflow and excess flow components as produced by a digital filter, and reconstruct the total flow by adding these two signals at the output. The optimization of the filter parameters and ANN architectures is carried out through global search techniques. Despite the favorable premises, the real effectiveness of such MMs has been tested only on a few case studies, and the quality of the baseflow separation they perform has never been thoroughly assessed. In this work, we compare the performance of MM against global models (GMs) for nine different gaging stations in the northern United States. Binary-coded swarm optimization is employed for the identification of filter parameters and model structure, while Extreme Learning Machines, instead of ANN, are used to drastically reduce the large computational times required to perform the experiments. The results show that there is no evidence that MM outperform global GM for predicting the total flow. In addition, the baseflow produced by the MM largely underestimates the actual baseflow component expected for most of the considered gages. This occurs because the values of the filter parameters maximizing overall accuracy do not reflect the geological characteristics of the river basins. The results indeed show that setting the filter parameters according to expert knowledge results in accurate baseflow separation but lower accuracy of total flow predictions, suggesting that these two objectives are intrinsically conflicting rather than compatible.
NASA Astrophysics Data System (ADS)
Berkman, Erkan A.; Lee, Soo Min; Ramos, Frank; Tucker, Eric; Arif, Ronald A.; Armour, Eric A.; Papasouliotis, George D.
2016-02-01
We report on green-emitting In0.18Ga0.82N/GaN multi-quantum well (MQW) structures over a variety of metalorganic chemical vapor deposition (MOCVD) growth conditions to examine the morphology, optical quality, and micron-scale emission properties. The MOCVD growth parameter space was analyzed utilizing two orthogonal metrics which allows comparing and optimizing growth conditions over a wide range of process parameters: effective gas speed, S*, and effective V/III ratio, V/III*. Optimized growth conditions with high V/III, low gas speed, and slow growth rates resulted in improved crystal quality, PL emission efficiency, and micron-scale wavelength uniformity. One of the main challenges in green MQWs with high Indium content is the formation of Indium inclusion type defects due to the large lattice mismatch combined with the miscibility gap between GaN and InN. An effective way of eliminating Indium inclusions was demonstrated by introducing a small fraction of H2 (2.7%) in the gas composition during the growth of high temperature GaN quantum barriers. In addition, the positive effects of employing an InGaN/GaN superlattice (SL) underlayer to crystal quality and micron-scale emission uniformity was demonstrated, which is of special interest for applications such as micro-LEDs.
Vilim, R.B.; Wegerich, S.W.
1995-12-31
A neural network originally proposed by Szu for performing pattern recognition has been modified for use in a noisy manufacturing environment. Signals from the factory floor are frequently affine transformed and, as a consequence, a signal may not be properly aligned with respect to the input node that corresponds to the signal leading edge or with respect to the number of nodes representing the time varying part. Rater than translate and scale the presented signal, an operation which because of noise can be prone to numerical error since the signal is not smoothly varying, the network in this paper has the capability to analytically translate and scale its internal representation of the signal so that it overlays the presented signal. A response surface in the neighborhood of the stored reference signal is built during, training, and covers the range of translate and scale parameter values expected. A genetic algorithm is used to search over this hilly terrain to find the optimal values of these parameters so that the reference signal overlays the presented signal. The procedure is repeated over all hypothesized pattern classes with the best fit identifying the class.
NASA Astrophysics Data System (ADS)
Xing, Y. F.; Wang, Y. S.; Shi, L.; Guo, H.; Chen, H.
2016-01-01
According to the human perceptional characteristics, a method combined by the optimal wavelet-packet transform and artificial neural network, so-called OWPT-ANN model, for psychoacoustical recognition is presented. Comparisons of time-frequency analysis methods are performed, and an OWPT with 21 critical bands is designed for feature extraction of a sound, as is a three-layer back-propagation ANN for sound quality (SQ) recognition. Focusing on the loudness and sharpness, the OWPT-ANN model is applied on vehicle noises under different working conditions. Experimental verifications show that the OWPT can effectively transfer a sound into a time-varying energy pattern as that in the human auditory system. The errors of loudness and sharpness of vehicle noise from the OWPT-ANN are all less than 5%, which suggest a good accuracy of the OWPT-ANN model in SQ recognition. The proposed methodology might be regarded as a promising technique for signal processing in the human-hearing related fields in engineering.
Optimal design of 2D digital filters based on neural networks
NASA Astrophysics Data System (ADS)
Wang, Xiao-hua; He, Yi-gang; Zheng, Zhe-zhao; Zhang, Xu-hong
2005-02-01
Two-dimensional (2-D) digital filters are widely useful in image processing and other 2-D digital signal processing fields,but designing 2-D filters is much more difficult than designing one-dimensional (1-D) ones.In this paper, a new design approach for designing linear-phase 2-D digital filters is described,which is based on a new neural networks algorithm (NNA).By using the symmetry of the given 2-D magnitude specification,a compact express for the magnitude response of a linear-phase 2-D finite impulse response (FIR) filter is derived.Consequently,the optimal problem of designing linear-phase 2-D FIR digital filters is turned to approximate the desired 2-D magnitude response by using the compact express.To solve the problem,a new NNA is presented based on minimizing the mean-squared error,and the convergence theorem is presented and proved to ensure the designed 2-D filter stable.Three design examples are also given to illustrate the effectiveness of the NNA-based design approach.
Wang, Zhongqi; Yang, Bo; Kang, Yonggang; Yang, Yuan
2016-01-01
Fixture plays an important part in constraining excessive sheet metal part deformation at machining, assembly, and measuring stages during the whole manufacturing process. However, it is still a difficult and nontrivial task to design and optimize sheet metal fixture locating layout at present because there is always no direct and explicit expression describing sheet metal fixture locating layout and responding deformation. To that end, an RBF neural network prediction model is proposed in this paper to assist design and optimization of sheet metal fixture locating layout. The RBF neural network model is constructed by training data set selected by uniform sampling and finite element simulation analysis. Finally, a case study is conducted to verify the proposed method. PMID:27127499
Rocksloh, K; Rapp, F R; Abu Abed, S; Müller, W; Reher, M; Gauglitz, G; Schmidt, P C
1999-09-01
Optimization of crushing strength and disintegration time of a high-dose plant extract tablet was reached after extensive experimentation. Effects of the processing parameters, like compression force and tooling, and also of the excipients were found to be significant. Best results for both disintegration time and crushing strength were obtained with a plant extract that was granulated by roller compaction before compression. To gain more information about the different effects, artificial neural networks (ANNs) and a conventional multivariate method (partial least squares [PLS]) were used for data analysis. The topologies of the neural networks of the feed-forward type were optimized manually and by pruning methods. All methods were tested for contemplated parameters, crushing strength, and disintegration time. In general, ANNs were found to be more successful in characterizing the effects that influence crushing strength and disintegration time than the conventional multivariate methods.
Wang, Zhongqi; Yang, Bo; Kang, Yonggang; Yang, Yuan
2016-01-01
Fixture plays an important part in constraining excessive sheet metal part deformation at machining, assembly, and measuring stages during the whole manufacturing process. However, it is still a difficult and nontrivial task to design and optimize sheet metal fixture locating layout at present because there is always no direct and explicit expression describing sheet metal fixture locating layout and responding deformation. To that end, an RBF neural network prediction model is proposed in this paper to assist design and optimization of sheet metal fixture locating layout. The RBF neural network model is constructed by training data set selected by uniform sampling and finite element simulation analysis. Finally, a case study is conducted to verify the proposed method. PMID:27127499
Liu, Derong; Wang, Ding; Wang, Fei-Yue; Li, Hongliang; Yang, Xiong
2014-12-01
In this paper, the infinite horizon optimal robust guaranteed cost control of continuous-time uncertain nonlinear systems is investigated using neural-network-based online solution of Hamilton-Jacobi-Bellman (HJB) equation. By establishing an appropriate bounded function and defining a modified cost function, the optimal robust guaranteed cost control problem is transformed into an optimal control problem. It can be observed that the optimal cost function of the nominal system is nothing but the optimal guaranteed cost of the original uncertain system. A critic neural network is constructed to facilitate the solution of the modified HJB equation corresponding to the nominal system. More importantly, an additional stabilizing term is introduced for helping to verify the stability, which reinforces the updating process of the weight vector and reduces the requirement of an initial stabilizing control. The uniform ultimate boundedness of the closed-loop system is analyzed by using the Lyapunov approach as well. Two simulation examples are provided to verify the effectiveness of the present control approach. PMID:25415951
Zhang, Peng; Liu, Keping; Zhao, Bo; Li, Yuanchun
2015-01-01
Optimal guidance is essential for the soft landing task. However, due to its high computational complexities, it is hardly applied to the autonomous guidance. In this paper, a computationally inexpensive optimal guidance algorithm based on the radial basis function neural network (RBFNN) is proposed. The optimization problem of the trajectory for soft landing on asteroids is formulated and transformed into a two-point boundary value problem (TPBVP). Combining the database of initial states with the relative initial co-states, an RBFNN is trained offline. The optimal trajectory of the soft landing is determined rapidly by applying the trained network in the online guidance. The Monte Carlo simulations of soft landing on the Eros433 are performed to demonstrate the effectiveness of the proposed guidance algorithm. PMID:26367382
Zhang, Peng; Liu, Keping; Zhao, Bo; Li, Yuanchun
2015-01-01
Optimal guidance is essential for the soft landing task. However, due to its high computational complexities, it is hardly applied to the autonomous guidance. In this paper, a computationally inexpensive optimal guidance algorithm based on the radial basis function neural network (RBFNN) is proposed. The optimization problem of the trajectory for soft landing on asteroids is formulated and transformed into a two-point boundary value problem (TPBVP). Combining the database of initial states with the relative initial co-states, an RBFNN is trained offline. The optimal trajectory of the soft landing is determined rapidly by applying the trained network in the online guidance. The Monte Carlo simulations of soft landing on the Eros433 are performed to demonstrate the effectiveness of the proposed guidance algorithm.
Zhang, Peng; Liu, Keping; Zhao, Bo; Li, Yuanchun
2015-01-01
Optimal guidance is essential for the soft landing task. However, due to its high computational complexities, it is hardly applied to the autonomous guidance. In this paper, a computationally inexpensive optimal guidance algorithm based on the radial basis function neural network (RBFNN) is proposed. The optimization problem of the trajectory for soft landing on asteroids is formulated and transformed into a two-point boundary value problem (TPBVP). Combining the database of initial states with the relative initial co-states, an RBFNN is trained offline. The optimal trajectory of the soft landing is determined rapidly by applying the trained network in the online guidance. The Monte Carlo simulations of soft landing on the Eros433 are performed to demonstrate the effectiveness of the proposed guidance algorithm. PMID:26367382
Chang, Chiao-Yun; Li, Heng; Shih, Yang-Ta; Lu, Tien-Chang
2015-03-02
We systematically investigated the influence of nanoscale V-pits on the internal quantum efficiency (IQE) of InGaN multiple quantum wells (MQWs) by adjusting the underlying superlattices (SLS). The analysis indicated that high barrier energy of sidewall MQWs on V-pits and long diffusion distance between the threading dislocation (TD) center and V-pit boundary were crucial to effectively passivate the non-radiative centers of TDs. For a larger V-pit, the thicker sidewall MQW on V-pit would decrease the barrier energy. On the contrary, a shorter distance between the TD center and V-pit boundary would be observed in a smaller V-pit, which could increase the carrier capturing capability of TDs. An optimized V-pit size of approximately 200–250 nm in our experiment could be concluded for MQWs with 15 pairs SLS, which exhibited an IQE value of 70%.
Terahertz harvesting with shape-optimized InAlAs/InGaAs self-switching nanodiodes
NASA Astrophysics Data System (ADS)
Cortes-Mestizo, Irving; Méndez-García, Victor H.; Briones, Joel; Perez-Caro, Manuel; Droopad, Ravi; McMurtry, Stefan; Hehn, Michel; Montaigne, François; Briones, Edgar
2015-11-01
In this letter, self-switching nanochannels have been proposed as an enabling technology for energy gathering in the terahertz (THz) regime. Such devices combine their diode-like behavior and high-speed of operation in order to generate DC electrical power from high-frequency signals. By using finite-element simulations, we have improved the sensitivity of L-shaped and V-shaped nanochannels based on InAlAs/InGaAs samples. Since those devices combine geometrical effects with their rectifying properties at zero-bias, we have improved their performance by optimizing their shape. Results show nominal sensitivities at zero-bias in the order of 40 V-1 and 20 V-1, attractive values for harvesting applications with square-law rectifiers.
Yoo, Sung-Hoon; Oh, Sung-Kwun; Pedrycz, Witold
2015-09-01
In this study, we propose a hybrid method of face recognition by using face region information extracted from the detected face region. In the preprocessing part, we develop a hybrid approach based on the Active Shape Model (ASM) and the Principal Component Analysis (PCA) algorithm. At this step, we use a CCD (Charge Coupled Device) camera to acquire a facial image by using AdaBoost and then Histogram Equalization (HE) is employed to improve the quality of the image. ASM extracts the face contour and image shape to produce a personal profile. Then we use a PCA method to reduce dimensionality of face images. In the recognition part, we consider the improved Radial Basis Function Neural Networks (RBF NNs) to identify a unique pattern associated with each person. The proposed RBF NN architecture consists of three functional modules realizing the condition phase, the conclusion phase, and the inference phase completed with the help of fuzzy rules coming in the standard 'if-then' format. In the formation of the condition part of the fuzzy rules, the input space is partitioned with the use of Fuzzy C-Means (FCM) clustering. In the conclusion part of the fuzzy rules, the connections (weights) of the RBF NNs are represented by four kinds of polynomials such as constant, linear, quadratic, and reduced quadratic. The values of the coefficients are determined by running a gradient descent method. The output of the RBF NNs model is obtained by running a fuzzy inference method. The essential design parameters of the network (including learning rate, momentum coefficient and fuzzification coefficient used by the FCM) are optimized by means of Differential Evolution (DE). The proposed P-RBF NNs (Polynomial based RBF NNs) are applied to facial recognition and its performance is quantified from the viewpoint of the output performance and recognition rate. PMID:26163042
Yoo, Sung-Hoon; Oh, Sung-Kwun; Pedrycz, Witold
2015-09-01
In this study, we propose a hybrid method of face recognition by using face region information extracted from the detected face region. In the preprocessing part, we develop a hybrid approach based on the Active Shape Model (ASM) and the Principal Component Analysis (PCA) algorithm. At this step, we use a CCD (Charge Coupled Device) camera to acquire a facial image by using AdaBoost and then Histogram Equalization (HE) is employed to improve the quality of the image. ASM extracts the face contour and image shape to produce a personal profile. Then we use a PCA method to reduce dimensionality of face images. In the recognition part, we consider the improved Radial Basis Function Neural Networks (RBF NNs) to identify a unique pattern associated with each person. The proposed RBF NN architecture consists of three functional modules realizing the condition phase, the conclusion phase, and the inference phase completed with the help of fuzzy rules coming in the standard 'if-then' format. In the formation of the condition part of the fuzzy rules, the input space is partitioned with the use of Fuzzy C-Means (FCM) clustering. In the conclusion part of the fuzzy rules, the connections (weights) of the RBF NNs are represented by four kinds of polynomials such as constant, linear, quadratic, and reduced quadratic. The values of the coefficients are determined by running a gradient descent method. The output of the RBF NNs model is obtained by running a fuzzy inference method. The essential design parameters of the network (including learning rate, momentum coefficient and fuzzification coefficient used by the FCM) are optimized by means of Differential Evolution (DE). The proposed P-RBF NNs (Polynomial based RBF NNs) are applied to facial recognition and its performance is quantified from the viewpoint of the output performance and recognition rate.
Antanasijević, Davor Z; Pocajt, Viktor V; Povrenović, Dragan S; Ristić, Mirjana Đ; Perić-Grujić, Aleksandra A
2013-01-15
This paper describes the development of an artificial neural network (ANN) model for the forecasting of annual PM(10) emissions at the national level, using widely available sustainability and economical/industrial parameters as inputs. The inputs for the model were selected and optimized using a genetic algorithm and the ANN was trained using the following variables: gross domestic product, gross inland energy consumption, incineration of wood, motorization rate, production of paper and paperboard, sawn wood production, production of refined copper, production of aluminum, production of pig iron and production of crude steel. The wide availability of the input parameters used in this model can overcome a lack of data and basic environmental indicators in many countries, which can prevent or seriously impede PM emission forecasting. The model was trained and validated with the data for 26 EU countries for the period from 1999 to 2006. PM(10) emission data, collected through the Convention on Long-range Transboundary Air Pollution - CLRTAP and the EMEP Programme or as emission estimations by the Regional Air Pollution Information and Simulation (RAINS) model, were obtained from Eurostat. The ANN model has shown very good performance and demonstrated that the forecast of PM(10) emission up to two years can be made successfully and accurately. The mean absolute error for two-year PM(10) emission prediction was only 10%, which is more than three times better than the predictions obtained from the conventional multi-linear regression and principal component regression models that were trained and tested using the same datasets and input variables.
Optimal design of neural networks for land-cover classification from multispectral imagery
NASA Astrophysics Data System (ADS)
Silvan-Cardenas, Jose L.
2004-02-01
It has long been shown the effectiveness of artificial neural networks to solve highly non-linear problems such as land-cover classification based on multispectral imagery. However, due to the large amount of data that is processed within this kind of applications, it is desirable to design networks with the lowest number of neurons that are capable to separate all of the given classes. At present, there are several methods intended to determine this optimal network. Most of them involve adjoining or pruning hidden neurons followed by further training in iterative fashion, which is generally a very slow process. As an alternative, the approach described in this paper is based on the computation of centroids of relevant clusters for each class samples through the well known clustering method ISODATA. A proper tessellation of the ISODATA centroids allows first the determination of the minimum number of neurons in the first hidden layer that are required to effectively separate all of the classes; and secondly, to compute weight and bias parameters for such neurons. Then, the minimum network required to perform the logic function that combines the halfspaces generated by the first layer into class-discriminant surfaces is determined via a logic function reduction method. This approach is much faster than that of current methods because it allows to determine the optimum network size and compute weight and bias parameters without further iterative adjustments. The procedure was tested with landsat 7 Enhanced Thematic Mapper Plus (ETM+) data. Results indicated that (1) the network exhibits good generalization behavior and (2) classification accuracies do not depend on the class boundary complexity but only on the class overlapping extent.
Fu, Xingang; Li, Shuhui; Fairbank, Michael; Wunsch, Donald C; Alonso, Eduardo
2015-09-01
This paper investigates how to train a recurrent neural network (RNN) using the Levenberg-Marquardt (LM) algorithm as well as how to implement optimal control of a grid-connected converter (GCC) using an RNN. To successfully and efficiently train an RNN using the LM algorithm, a new forward accumulation through time (FATT) algorithm is proposed to calculate the Jacobian matrix required by the LM algorithm. This paper explores how to incorporate FATT into the LM algorithm. The results show that the combination of the LM and FATT algorithms trains RNNs better than the conventional backpropagation through time algorithm. This paper presents an analytical study on the optimal control of GCCs, including theoretically ideal optimal and suboptimal controllers. To overcome the inapplicability of the optimal GCC controller under practical conditions, a new RNN controller with an improved input structure is proposed to approximate the ideal optimal controller. The performance of an ideal optimal controller and a well-trained RNN controller was compared in close to real-life power converter switching environments, demonstrating that the proposed RNN controller can achieve close to ideal optimal control performance even under low sampling rate conditions. The excellent performance of the proposed RNN controller under challenging and distorted system conditions further indicates the feasibility of using an RNN to approximate optimal control in practical applications.
NASA Astrophysics Data System (ADS)
Liu, H. Y.; Li, X.; Liu, S.; Ni, X.; Avrutin, V.; Izyumskaya, N.; Özgür, Ü.; Yankovich, A. B.; Kvit, A. V.; Voyles, P. M.; Reshchikov, M. A.; Morkoç, H.
2011-02-01
We report on the effects of substrate temperature and surface morphology of p-GaN templates on the properties of ZnO:Ga (GZO) layers grown by plasma-assisted molecular beam epitaxy. Substrate temperature varying from 200 °C to 450°C was found to have only a moderate effect on the electrical properties of GZO films but it greatly affects the surface morphology of the GZO films. The surface morphology and growth mode of GZO were also found to be considerably affected by the surface morphology of underlying p-GaN templates. On p-GaN templates with a smooth surface (RMS = 0.4 nm) featured by clear atomic steps, GZO layers grew in 2D growth mode and exhibited smooth surfaces with RMS roughness of 2 nm. In contrast, on p-GaN without clear atomic steps but having comparable surface roughness of 0.6 nm, GZO layers grew in 3D growth mode and exhibited rough surface (RMS roughness of ~17.0-20.0 nm). The results of surface roughness are consistent with those from TEM measurements. The lowest resistivity of ~2.3×10-4 Ω.cm for as-grown GZO layers has been achieved at substrate temperature of 350°C, while the data for 2D GZO layers was affected by a parallel conduction channel from underneath GaN and require further studies. Although the differences in electrical properties and surface morphology existed, the GZO layers grown on different p-GaN templates showed optical transparency higher than 90% in the visible spectral range. The performance of 3D GZO layers as p-electrode was tested in InGaN light emitting diodes.
NASA Astrophysics Data System (ADS)
Lahmiri, Salim
2016-02-01
Multiresolution analysis techniques including continuous wavelet transform, empirical mode decomposition, and variational mode decomposition are tested in the context of interest rate next-day variation prediction. In particular, multiresolution analysis techniques are used to decompose interest rate actual variation and feedforward neural network for training and prediction. Particle swarm optimization technique is adopted to optimize its initial weights. For comparison purpose, autoregressive moving average model, random walk process and the naive model are used as main reference models. In order to show the feasibility of the presented hybrid models that combine multiresolution analysis techniques and feedforward neural network optimized by particle swarm optimization, we used a set of six illustrative interest rates; including Moody's seasoned Aaa corporate bond yield, Moody's seasoned Baa corporate bond yield, 3-Month, 6-Month and 1-Year treasury bills, and effective federal fund rate. The forecasting results show that all multiresolution-based prediction systems outperform the conventional reference models on the criteria of mean absolute error, mean absolute deviation, and root mean-squared error. Therefore, it is advantageous to adopt hybrid multiresolution techniques and soft computing models to forecast interest rate daily variations as they provide good forecasting performance.
NASA Astrophysics Data System (ADS)
Vosoughifar, Hamid Reza; Sadat Shokouhi, Seyed Kazem; Dolatshah, Azam; Rahnavard, Yousef; Atapour, Hassan
2013-04-01
Blasts can produce, in a very short time, an overload much greater than the design load of a building. The blast explosion nearby or within structures causes catastrophic damage to the building both externally and internally. This study intends to model a Cold-Formed Steel (CFS) building using Finite Element Method (FEM) in which material properties of the model are defined according to results of performed laboratory tests. Then accelerograph record of a standard blast was applied to the Finite Element (FE) model. Furthermore, various Optimal Sensor Placement (OSP) algorithms were used and Genetic Algorithm (GA) was selected to act as the solution of the optimization formulation in selection of the best sensor placement according to the blast loading response of the system. In this research a novel numerical algorithm was proposed for OSP procedure which utilizes the exact value of the structural response under blast excitation. Results show that with a proper OSP method for Structural Health Monitoring (SHM) can detect the weak points of CFS structures in different parts efficiently.
Dierks, Travis; Thumati, Balaje T; Jagannathan, S
2009-01-01
The optimal control of linear systems accompanied by quadratic cost functions can be achieved by solving the well-known Riccati equation. However, the optimal control of nonlinear discrete-time systems is a much more challenging task that often requires solving the nonlinear Hamilton-Jacobi-Bellman (HJB) equation. In the recent literature, discrete-time approximate dynamic programming (ADP) techniques have been widely used to determine the optimal or near optimal control policies for affine nonlinear discrete-time systems. However, an inherent assumption of ADP requires the value of the controlled system one step ahead and at least partial knowledge of the system dynamics to be known. In this work, the need of the partial knowledge of the nonlinear system dynamics is relaxed in the development of a novel approach to ADP using a two part process: online system identification and offline optimal control training. First, in the system identification process, a neural network (NN) is tuned online using novel tuning laws to learn the complete plant dynamics so that a local asymptotic stability of the identification error can be shown. Then, using only the learned NN system model, offline ADP is attempted resulting in a novel optimal control law. The proposed scheme does not require explicit knowledge of the system dynamics as only the learned NN model is needed. The proof of convergence is demonstrated. Simulation results verify theoretical conjecture. PMID:19596551
The neural code for auditory space depends on sound frequency and head size in an optimal manner.
Harper, Nicol S; Scott, Brian H; Semple, Malcolm N; McAlpine, David
2014-01-01
A major cue to the location of a sound source is the interaural time difference (ITD)-the difference in sound arrival time at the two ears. The neural representation of this auditory cue is unresolved. The classic model of ITD coding, dominant for a half-century, posits that the distribution of best ITDs (the ITD evoking a neuron's maximal response) is unimodal and largely within the range of ITDs permitted by head-size. This is often interpreted as a place code for source location. An alternative model, based on neurophysiology in small mammals, posits a bimodal distribution of best ITDs with exquisite sensitivity to ITDs generated by means of relative firing rates between the distributions. Recently, an optimal-coding model was proposed, unifying the disparate features of these two models under the framework of efficient coding by neural populations. The optimal-coding model predicts that distributions of best ITDs depend on head size and sound frequency: for high frequencies and large heads it resembles the classic model, for low frequencies and small head sizes it resembles the bimodal model. The optimal-coding model makes key, yet unobserved, predictions: for many species, including humans, both forms of neural representation are employed, depending on sound frequency. Furthermore, novel representations are predicted for intermediate frequencies. Here, we examine these predictions in neurophysiological data from five mammalian species: macaque, guinea pig, cat, gerbil and kangaroo rat. We present the first evidence supporting these untested predictions, and demonstrate that different representations appear to be employed at different sound frequencies in the same species. PMID:25372405
The Neural Code for Auditory Space Depends on Sound Frequency and Head Size in an Optimal Manner
Harper, Nicol S.; Scott, Brian H.; Semple, Malcolm N.; McAlpine, David
2014-01-01
A major cue to the location of a sound source is the interaural time difference (ITD)–the difference in sound arrival time at the two ears. The neural representation of this auditory cue is unresolved. The classic model of ITD coding, dominant for a half-century, posits that the distribution of best ITDs (the ITD evoking a neuron’s maximal response) is unimodal and largely within the range of ITDs permitted by head-size. This is often interpreted as a place code for source location. An alternative model, based on neurophysiology in small mammals, posits a bimodal distribution of best ITDs with exquisite sensitivity to ITDs generated by means of relative firing rates between the distributions. Recently, an optimal-coding model was proposed, unifying the disparate features of these two models under the framework of efficient coding by neural populations. The optimal-coding model predicts that distributions of best ITDs depend on head size and sound frequency: for high frequencies and large heads it resembles the classic model, for low frequencies and small head sizes it resembles the bimodal model. The optimal-coding model makes key, yet unobserved, predictions: for many species, including humans, both forms of neural representation are employed, depending on sound frequency. Furthermore, novel representations are predicted for intermediate frequencies. Here, we examine these predictions in neurophysiological data from five mammalian species: macaque, guinea pig, cat, gerbil and kangaroo rat. We present the first evidence supporting these untested predictions, and demonstrate that different representations appear to be employed at different sound frequencies in the same species. PMID:25372405
The neural code for auditory space depends on sound frequency and head size in an optimal manner.
Harper, Nicol S; Scott, Brian H; Semple, Malcolm N; McAlpine, David
2014-01-01
A major cue to the location of a sound source is the interaural time difference (ITD)-the difference in sound arrival time at the two ears. The neural representation of this auditory cue is unresolved. The classic model of ITD coding, dominant for a half-century, posits that the distribution of best ITDs (the ITD evoking a neuron's maximal response) is unimodal and largely within the range of ITDs permitted by head-size. This is often interpreted as a place code for source location. An alternative model, based on neurophysiology in small mammals, posits a bimodal distribution of best ITDs with exquisite sensitivity to ITDs generated by means of relative firing rates between the distributions. Recently, an optimal-coding model was proposed, unifying the disparate features of these two models under the framework of efficient coding by neural populations. The optimal-coding model predicts that distributions of best ITDs depend on head size and sound frequency: for high frequencies and large heads it resembles the classic model, for low frequencies and small head sizes it resembles the bimodal model. The optimal-coding model makes key, yet unobserved, predictions: for many species, including humans, both forms of neural representation are employed, depending on sound frequency. Furthermore, novel representations are predicted for intermediate frequencies. Here, we examine these predictions in neurophysiological data from five mammalian species: macaque, guinea pig, cat, gerbil and kangaroo rat. We present the first evidence supporting these untested predictions, and demonstrate that different representations appear to be employed at different sound frequencies in the same species.
Deco, Gustavo; Hugues, Etienne
2012-01-01
Recent neurophysiological experiments have demonstrated a remarkable effect of attention on the underlying neural activity that suggests for the first time that information encoding is indeed actively influenced by attention. Single cell recordings show that attention reduces both the neural variability and correlations in the attended condition with respect to the non-attended one. This reduction of variability and redundancy enhances the information associated with the detection and further processing of the attended stimulus. Beyond the attentional paradigm, the local activity in a neural circuit can be modulated in a number of ways, leading to the general question of understanding how the activity of such circuits is sensitive to these relatively small modulations. Here, using an analytically tractable neural network model, we demonstrate how this enhancement of information emerges when excitatory and inhibitory synaptic currents are balanced. In particular, we show that the network encoding sensitivity -as measured by the Fisher information- is maximized at the exact balance. Furthermore, we find a similar result for a more realistic spiking neural network model. As the regime of balanced inputs has been experimentally observed, these results suggest that this regime is functionally important from an information encoding standpoint. PMID:22359550
Sikora, R.; Chady, T.; Baniukiewicz, P.; Caryk, M.; Piekarczyk, B.
2010-02-22
Nondestructive testing and evaluation are under continuous development. Currently researches are concentrated on three main topics: advancement of existing methods, introduction of novel methods and development of artificial intelligent systems for automatic defect recognition (ADR). Automatic defect classification algorithm comprises of two main tasks: creating a defect database and preparing a defect classifier. Here, the database was built using defect features that describe all geometrical and texture properties of the defect. Almost twenty carefully selected features calculated for flaws extracted from real radiograms were used. The radiograms were obtained from shipbuilding industry and they were verified by qualified operator. Two weld defect's classifiers based on artificial neural networks were proposed and compared. First model consisted of one neural network model, where each output neuron corresponded to different defect group. The second model contained five neural networks. Each neural network had one neuron on output and was responsible for detection of defects from one group. In order to evaluate the effectiveness of the neural networks classifiers, the mean square errors were calculated for test radiograms and compared.
Pruning Neural Networks with Distribution Estimation Algorithms
Cantu-Paz, E
2003-01-15
This paper describes the application of four evolutionary algorithms to the pruning of neural networks used in classification problems. Besides of a simple genetic algorithm (GA), the paper considers three distribution estimation algorithms (DEAs): a compact GA, an extended compact GA, and the Bayesian Optimization Algorithm. The objective is to determine if the DEAs present advantages over the simple GA in terms of accuracy or speed in this problem. The experiments used a feed forward neural network trained with standard back propagation and public-domain and artificial data sets. The pruned networks seemed to have better or equal accuracy than the original fully-connected networks. Only in a few cases, pruning resulted in less accurate networks. We found few differences in the accuracy of the networks pruned by the four EAs, but found important differences in the execution time. The results suggest that a simple GA with a small population might be the best algorithm for pruning networks on the data sets we tested.
Ansari, N; Hou, E H; Yu, Y
1995-01-01
Reports a new method for optimizing satellite broadcasting schedules based on the Hopfield neural model in combination with the mean field annealing theory. A clamping technique is used with an associative matrix, thus reducing the dimensions of the solution space. A formula for estimating the critical temperature for the mean field annealing procedure is derived, hence enabling the updating of the mean field theory equations to be more economical. Several factors on the numerical implementation of the mean field equations using a straightforward iteration method that may cause divergence are discussed; methods to avoid this kind of divergence are also proposed. Excellent results are consistently found for problems of various sizes.
NASA Astrophysics Data System (ADS)
Adineh, V. R.; Aghanajafi, C.; Dehghan, G. H.; Jelvani, S.
2008-11-01
This paper presents an artificial intelligence approach for optimization of the operational parameters such as gas pressure ratio and discharge current in a fast-axial-flow CW CO 2 laser by coupling artificial neural networks and genetic algorithm. First, a series of experiments were used as the learning data for artificial neural networks. The best-trained network was connected to genetic algorithm as a fitness function to find the optimum parameters. After the optimization, the calculated laser power increases by 33% and the measured value increases by 21% in an experiment as compared to a non-optimized case.
Mehrotra, Shakti; Prakash, O; Khan, Feroz; Kukreja, A K
2013-02-01
KEY MESSAGE : ANN-based combinatorial model is proposed and its efficiency is assessed for the prediction of optimal culture conditions to achieve maximum productivity in a bioprocess in terms of high biomass. A neural network approach is utilized in combination with Hidden Markov concept to assess the optimal values of different environmental factors that result in maximum biomass productivity of cultured tissues after definite culture duration. Five hidden Markov models (HMMs) were derived for five test culture conditions, i.e. pH of liquid growth medium, volume of medium per culture vessel, sucrose concentration (%w/v) in growth medium, nitrate concentration (g/l) in the medium and finally the density of initial inoculum (g fresh weight) per culture vessel and their corresponding fresh weight biomass. The artificial neural network (ANN) model was represented as the function of these five Markov models, and the overall simulation of fresh weight biomass was done with this combinatorial ANN-HMM. The empirical results of Rauwolfia serpentina hairy roots were taken as model and compared with simulated results obtained from pure ANN and ANN-HMMs. The stochastic testing and Cronbach's α-value of pure and combinatorial model revealed more internal consistency and skewed character (0.4635) in histogram of ANN-HMM compared to pure ANN (0.3804). The simulated results for optimal conditions of maximum fresh weight production obtained from ANN-HMM and ANN model closely resemble the experimentally optimized culture conditions based on which highest fresh weight was obtained. However, only 2.99 % deviation from the experimental values could be observed in the values obtained from combinatorial model when compared to the pure ANN model (5.44 %). This comparison showed 45 % better potential of combinatorial model for the prediction of optimal culture conditions for the best growth of hairy root cultures.
Pennings, Jeroen L A; Theunissen, Peter T; Piersma, Aldert H
2012-10-28
The murine neural embryonic stem cell test (ESTn) is an in vitro model for neurodevelopmental toxicity testing. Recent studies have shown that application of transcriptomics analyses in the ESTn is useful for obtaining more accurate predictions as well as mechanistic insights. Gene expression responses due to stem cell neural differentiation versus toxicant exposure could be distinguished using the Principal Component Analysis based differentiation track algorithm. In this study, we performed a de novo analysis on combined raw data (10 compounds, 19 exposures) from three previous transcriptomics studies to identify an optimized gene set for neurodevelopmental toxicity prediction in the ESTn. By evaluating predictions of 200,000 randomly selected gene sets, we identified genes which significantly contributed to the prediction reliability. A set of 100 genes was obtained, predominantly involved in (neural) development. Further stringency restrictions resulted in a set of 29 genes that allowed for 84% prediction accuracy (area under the curve 94%). We anticipate these gene sets will contribute to further improve ESTn transcriptomics studies aimed at compound risk assessment.
Li, Zhongwei; Sun, Beibei; Xin, Yuezhen; Wang, Xun
2016-01-01
Flavones, the secondary metabolites of Phellinus igniarius fungus, have the properties of antioxidation and anticancer. Because of the great medicinal value, there are large demands on flavones for medical use and research. Flavones abstracted from natural Phellinus can not meet the medical and research need, since Phellinus in the natural environment is very rare and is hard to be cultivated artificially. The production of flavones is mainly related to the fermentation culture of Phellinus, which made the optimization of culture conditions an important problem. Some researches were made to optimize the fermentation culture conditions, such as the method of response surface methodology, which claimed the optimal flavones production was 1532.83 μg/mL. In order to further optimize the fermentation culture conditions for flavones, in this work a hybrid intelligent algorithm with genetic algorithm and BP neural network is proposed. Our method has the intelligent learning ability and can overcome the limitation of large-scale biotic experiments. Through simulations, the optimal culture conditions are obtained and the flavones production is increased to 2200 μg/mL.
Li, Zhongwei; Sun, Beibei; Xin, Yuezhen; Wang, Xun; Zhu, Hu
2016-01-01
Flavones, the secondary metabolites of Phellinus igniarius fungus, have the properties of antioxidation and anticancer. Because of the great medicinal value, there are large demands on flavones for medical use and research. Flavones abstracted from natural Phellinus can not meet the medical and research need, since Phellinus in the natural environment is very rare and is hard to be cultivated artificially. The production of flavones is mainly related to the fermentation culture of Phellinus, which made the optimization of culture conditions an important problem. Some researches were made to optimize the fermentation culture conditions, such as the method of response surface methodology, which claimed the optimal flavones production was 1532.83 μg/mL. In order to further optimize the fermentation culture conditions for flavones, in this work a hybrid intelligent algorithm with genetic algorithm and BP neural network is proposed. Our method has the intelligent learning ability and can overcome the limitation of large-scale biotic experiments. Through simulations, the optimal culture conditions are obtained and the flavones production is increased to 2200 μg/mL. PMID:27595102
Li, Zhongwei; Sun, Beibei; Xin, Yuezhen; Wang, Xun
2016-01-01
Flavones, the secondary metabolites of Phellinus igniarius fungus, have the properties of antioxidation and anticancer. Because of the great medicinal value, there are large demands on flavones for medical use and research. Flavones abstracted from natural Phellinus can not meet the medical and research need, since Phellinus in the natural environment is very rare and is hard to be cultivated artificially. The production of flavones is mainly related to the fermentation culture of Phellinus, which made the optimization of culture conditions an important problem. Some researches were made to optimize the fermentation culture conditions, such as the method of response surface methodology, which claimed the optimal flavones production was 1532.83 μg/mL. In order to further optimize the fermentation culture conditions for flavones, in this work a hybrid intelligent algorithm with genetic algorithm and BP neural network is proposed. Our method has the intelligent learning ability and can overcome the limitation of large-scale biotic experiments. Through simulations, the optimal culture conditions are obtained and the flavones production is increased to 2200 μg/mL. PMID:27595102
Han, Zong-wei; Huang, Wei; Luo, Yun; Zhang, Chun-di; Qi, Da-cheng
2015-03-01
Taking the soil organic matter in eastern Zhongxiang County, Hubei Province, as a research object, thirteen sample sets from different regions were arranged surrounding the road network, the spatial configuration of which was optimized by the simulated annealing approach. The topographic factors of these thirteen sample sets, including slope, plane curvature, profile curvature, topographic wetness index, stream power index and sediment transport index, were extracted by the terrain analysis. Based on the results of optimization, a multiple linear regression model with topographic factors as independent variables was built. At the same time, a multilayer perception model on the basis of neural network approach was implemented. The comparison between these two models was carried out then. The results revealed that the proposed approach was practicable in optimizing soil sampling scheme. The optimal configuration was capable of gaining soil-landscape knowledge exactly, and the accuracy of optimal configuration was better than that of original samples. This study designed a sampling configuration to study the soil attribute distribution by referring to the spatial layout of road network, historical samples, and digital elevation data, which provided an effective means as well as a theoretical basis for determining the sampling configuration and displaying spatial distribution of soil organic matter with low cost and high efficiency. PMID:26211074
Luo, Biao; Wu, Huai-Ning
2012-12-01
This paper addresses the approximate optimal control problem for a class of parabolic partial differential equation (PDE) systems with nonlinear spatial differential operators. An approximate optimal control design method is proposed on the basis of the empirical eigenfunctions (EEFs) and neural network (NN). First, based on the data collected from the PDE system, the Karhunen-Loève decomposition is used to compute the EEFs. With those EEFs, the PDE system is formulated as a high-order ordinary differential equation (ODE) system. To further reduce its dimension, the singular perturbation (SP) technique is employed to derive a reduced-order model (ROM), which can accurately describe the dominant dynamics of the PDE system. Second, the Hamilton-Jacobi-Bellman (HJB) method is applied to synthesize an optimal controller based on the ROM, where the closed-loop asymptotic stability of the high-order ODE system can be guaranteed by the SP theory. By dividing the optimal control law into two parts, the linear part is obtained by solving an algebraic Riccati equation, and a new type of HJB-like equation is derived for designing the nonlinear part. Third, a control update strategy based on successive approximation is proposed to solve the HJB-like equation, and its convergence is proved. Furthermore, an NN approach is used to approximate the cost function. Finally, we apply the developed approximate optimal control method to a diffusion-reaction process with a nonlinear spatial operator, and the simulation results illustrate its effectiveness. PMID:22588610
Buyukada, Musa
2016-09-01
Co-combustion of coal and peanut hull (PH) were investigated using artificial neural networks (ANN), particle swarm optimization, and Monte Carlo simulation as a function of blend ratio, heating rate, and temperature. The best prediction was reached by ANN61 multi-layer perception model with a R(2) of 0.99994. Blend ratio of 90 to 10 (PH to coal, wt%), temperature of 305°C, and heating rate of 49°Cmin(-1) were determined as the optimum input values and yield of 87.4% was obtained under PSO optimized conditions. The validation experiments resulted in yields of 87.5%±0.2 after three replications. Monte Carlo simulations were used for the probabilistic assessments of stochastic variability and uncertainty associated with explanatory variables of co-combustion process. PMID:27243606
Buyukada, Musa
2016-09-01
Co-combustion of coal and peanut hull (PH) were investigated using artificial neural networks (ANN), particle swarm optimization, and Monte Carlo simulation as a function of blend ratio, heating rate, and temperature. The best prediction was reached by ANN61 multi-layer perception model with a R(2) of 0.99994. Blend ratio of 90 to 10 (PH to coal, wt%), temperature of 305°C, and heating rate of 49°Cmin(-1) were determined as the optimum input values and yield of 87.4% was obtained under PSO optimized conditions. The validation experiments resulted in yields of 87.5%±0.2 after three replications. Monte Carlo simulations were used for the probabilistic assessments of stochastic variability and uncertainty associated with explanatory variables of co-combustion process.
NASA Astrophysics Data System (ADS)
Chen, Ying; Liu, Teng; Wang, Wenyue; Zhu, Qiguang; Bi, Weihong
2015-04-01
According to the band gap and photon localization characteristics, the single-arm notching and the double-arm notching Mach-Zehnder interferometer (MZI) structures based on 2D triangular lattice air hole-typed photonic crystal waveguide are proposed. The back-propagation (BP) neural network is introduced to optimize the structural parameters of the photonic crystal MZI structure, which results in the normalized transmission peak increasing from 85.3% to 97.1%. The sensitivity performances of the two structures are compared and analyzed using the Salmonella solution samples with different concentrations in the numerical simulation. The results show that the sensitivity of the double-arm notching structure is 4583 nm/RIU, which is about 6.4 times of the single-arm notching structure, which can provide some references for the optimization of the photonic devices and the design of high-sensitivity biosensors.
Paegert, Martin; Stassun, Keivan G.; Burger, Dan M.
2014-08-01
We describe a new neural-net-based light curve classifier and provide it with documentation as a ready-to-use tool for the community. While optimized for identification and classification of eclipsing binary stars, the classifier is general purpose, and has been developed for speed in the context of upcoming massive surveys such as the Large Synoptic Survey Telescope. A challenge for classifiers in the context of neural-net training and massive data sets is to minimize the number of parameters required to describe each light curve. We show that a simple and fast geometric representation that encodes the overall light curve shape, together with a chi-square parameter to capture higher-order morphology information results in efficient yet robust light curve classification, especially for eclipsing binaries. Testing the classifier on the ASAS light curve database, we achieve a retrieval rate of 98% and a false-positive rate of 2% for eclipsing binaries. We achieve similarly high retrieval rates for most other periodic variable-star classes, including RR Lyrae, Mira, and delta Scuti. However, the classifier currently has difficulty discriminating between different sub-classes of eclipsing binaries, and suffers a relatively low (∼60%) retrieval rate for multi-mode delta Cepheid stars. We find that it is imperative to train the classifier's neural network with exemplars that include the full range of light curve quality to which the classifier will be expected to perform; the classifier performs well on noisy light curves only when trained with noisy exemplars. The classifier source code, ancillary programs, a trained neural net, and a guide for use, are provided.
NASA Astrophysics Data System (ADS)
Paegert, Martin; Stassun, Keivan G.; Burger, Dan M.
2014-08-01
We describe a new neural-net-based light curve classifier and provide it with documentation as a ready-to-use tool for the community. While optimized for identification and classification of eclipsing binary stars, the classifier is general purpose, and has been developed for speed in the context of upcoming massive surveys such as the Large Synoptic Survey Telescope. A challenge for classifiers in the context of neural-net training and massive data sets is to minimize the number of parameters required to describe each light curve. We show that a simple and fast geometric representation that encodes the overall light curve shape, together with a chi-square parameter to capture higher-order morphology information results in efficient yet robust light curve classification, especially for eclipsing binaries. Testing the classifier on the ASAS light curve database, we achieve a retrieval rate of 98% and a false-positive rate of 2% for eclipsing binaries. We achieve similarly high retrieval rates for most other periodic variable-star classes, including RR Lyrae, Mira, and delta Scuti. However, the classifier currently has difficulty discriminating between different sub-classes of eclipsing binaries, and suffers a relatively low (~60%) retrieval rate for multi-mode delta Cepheid stars. We find that it is imperative to train the classifier's neural network with exemplars that include the full range of light curve quality to which the classifier will be expected to perform; the classifier performs well on noisy light curves only when trained with noisy exemplars. The classifier source code, ancillary programs, a trained neural net, and a guide for use, are provided.
Optimizing growth and post treatment of diamond for high capacitance neural interfaces.
Tong, Wei; Fox, Kate; Zamani, Akram; Turnley, Ann M; Ganesan, Kumaravelu; Ahnood, Arman; Cicione, Rosemary; Meffin, Hamish; Prawer, Steven; Stacey, Alastair; Garrett, David J
2016-10-01
Electrochemical and biological properties are two crucial criteria in the selection of the materials to be used as electrodes for neural interfaces. For neural stimulation, materials are required to exhibit high capacitance and to form intimate contact with neurons for eliciting effective neural responses at acceptably low voltages. Here we report on a new high capacitance material fabricated using nitrogen included ultrananocrystalline diamond (N-UNCD). After exposure to oxygen plasma for 3 h, the activated N-UNCD exhibited extremely high electrochemical capacitance greater than 1 mF/cm(2), which originates from the special hybrid sp(2)/sp(3) structure of N-UNCD. The in vitro biocompatibility of the activated N-UNCD was then assessed using rat cortical neurons and surface roughness was found to be critical for healthy neuron growth, with best results observed on surfaces with a roughness of approximately 20 nm. Therefore, by using oxygen plasma activated N-UNCD with appropriate surface roughness, and considering the chemical and mechanical stability of diamond, the fabricated neural interfaces are expected to exhibit high efficacy, long-term stability and a healthy neuron/electrode interface. PMID:27424214
Optimizing growth and post treatment of diamond for high capacitance neural interfaces.
Tong, Wei; Fox, Kate; Zamani, Akram; Turnley, Ann M; Ganesan, Kumaravelu; Ahnood, Arman; Cicione, Rosemary; Meffin, Hamish; Prawer, Steven; Stacey, Alastair; Garrett, David J
2016-10-01
Electrochemical and biological properties are two crucial criteria in the selection of the materials to be used as electrodes for neural interfaces. For neural stimulation, materials are required to exhibit high capacitance and to form intimate contact with neurons for eliciting effective neural responses at acceptably low voltages. Here we report on a new high capacitance material fabricated using nitrogen included ultrananocrystalline diamond (N-UNCD). After exposure to oxygen plasma for 3 h, the activated N-UNCD exhibited extremely high electrochemical capacitance greater than 1 mF/cm(2), which originates from the special hybrid sp(2)/sp(3) structure of N-UNCD. The in vitro biocompatibility of the activated N-UNCD was then assessed using rat cortical neurons and surface roughness was found to be critical for healthy neuron growth, with best results observed on surfaces with a roughness of approximately 20 nm. Therefore, by using oxygen plasma activated N-UNCD with appropriate surface roughness, and considering the chemical and mechanical stability of diamond, the fabricated neural interfaces are expected to exhibit high efficacy, long-term stability and a healthy neuron/electrode interface.
[Optimization of pellet formulation with the help of artificial neural networks].
Kása, Péter; Sovány, Tamás; Hódi, Klára
2007-01-01
The authors demonstrate the essence and the application possibility of artificial neural networks in the formulation of pharmaceutical preparations. They draw attention to that the use of ANN the data processing will speed up and more accurate which will cause the decrease of the preliminary investigations and the amounts of the materials. PMID:17933271
Optimized laser patterning for high performance Cu(In,Ga)Se2 thin-film solar modules
NASA Astrophysics Data System (ADS)
Burn, Andreas; Muralt, Martin; Witte, Reiner; Buecheler, Stephan; Nishiwaki, Shiro; Krainer, Lukas; Spuehler, Gabriel J.; Romano, Valerio
2014-03-01
The thin-film solar cell market has seen a period of consolidation during the last years and many involved companies were forced to stop production due to increasing price pressure from competing cell technologies. Today, thin-film solar industry is gaining momentum again. Especially Cu(In,Ga)Se2 technology evolves at high pace fired by recently achieved record efficiencies of 20.4 percent on flexible polyimide substrate [1] and 20.8 percent on glass substrate [2]. Fresh companies are preparing market entry with matured products and manufacturing technology suitable for high-volume and high-throughput production. Among these key-enabling technologies is laser patterning for cell-to-cell interconnects. Several research groups worked on efficient and reliable laser processes that are now ready for the industrial assessment. Here we present a set of work-horse processes for P1, P2 and P3 scribing of CIGS cells on glass substrate. Optimized parameters are presented for 532 nm and 1064 nm using 50 ps pulses from an all-in-fiber laser system. We further demonstrate the successful realization of functional 8-cell modules with a reduced "dead-zone" width of 70±5 μm and high efficiencies. The certified efficiency of 16.6 percent for our low-dead-zone champion module confirms the observation that shrinking of interconnects has no adverse effects on their electrical quality.
Yarn Quality Prediction Based on Improved BP Neural Network
NASA Astrophysics Data System (ADS)
Yang, Jian-Guo; Xiong, Jing-Wei; Xun, Lan
Aiming at the key quality indexes xbt in spinning processing is caused by many complex and interactions factors. A xbt prediction model is put forward based on the PSO-BP neural network, which adjusts weights of BP neural network using particle swarm optimization (PSO) rather than the traditional gradient descent method, is used to improve the convergence speed of neural network and the ability of getting the global optimal solution. As the object of a large number of field detection data in a spinning workshop, the results show that, compared with the traditional BP algorithm and GA-BP algorithm, the PSO-BP neural network can obvious improve yarn quality prediction model precision and stability.
Šiljić Tomić, Aleksandra N; Antanasijević, Davor Z; Ristić, Mirjana Đ; Perić-Grujić, Aleksandra A; Pocajt, Viktor V
2016-05-01
This paper describes the application of artificial neural network models for the prediction of biological oxygen demand (BOD) levels in the Danube River. Eighteen regularly monitored water quality parameters at 17 stations on the river stretch passing through Serbia were used as input variables. The optimization of the model was performed in three consecutive steps: firstly, the spatial influence of a monitoring station was examined; secondly, the monitoring period necessary to reach satisfactory performance was determined; and lastly, correlation analysis was applied to evaluate the relationship among water quality parameters. Root-mean-square error (RMSE) was used to evaluate model performance in the first two steps, whereas in the last step, multiple statistical indicators of performance were utilized. As a result, two optimized models were developed, a general regression neural network model (labeled GRNN-1) that covers the monitoring stations from the Danube inflow to the city of Novi Sad and a GRNN model (labeled GRNN-2) that covers the stations from the city of Novi Sad to the border with Romania. Both models demonstrated good agreement between the predicted and actually observed BOD values.
Zhou, Fuqiang; Su, Zhen; Chai, Xinghua; Chen, Lipeng
2014-01-01
This paper proposes a new method to detect and identify foreign matter mixed in a plastic bottle filled with transfusion solution. A spin-stop mechanism and mixed illumination style are applied to obtain high contrast images between moving foreign matter and a static transfusion background. The Gaussian mixture model is used to model the complex background of the transfusion image and to extract moving objects. A set of features of moving objects are extracted and selected by the ReliefF algorithm, and optimal feature vectors are fed into the back propagation (BP) neural network to distinguish between foreign matter and bubbles. The mind evolutionary algorithm (MEA) is applied to optimize the connection weights and thresholds of the BP neural network to obtain a higher classification accuracy and faster convergence rate. Experimental results show that the proposed method can effectively detect visible foreign matter in 250-mL transfusion bottles. The misdetection rate and false alarm rate are low, and the detection accuracy and detection speed are satisfactory. PMID:25347581
NASA Astrophysics Data System (ADS)
Ai, Yuewei; Shao, Xinyu; Jiang, Ping; Li, Peigen; Liu, Yang; Yue, Chen
2015-11-01
The welded joints of dissimilar materials have been widely used in automotive, ship and space industries. The joint quality is often evaluated by weld seam geometry, microstructures and mechanical properties. To obtain the desired weld seam geometry and improve the quality of welded joints, this paper proposes a process modeling and parameter optimization method to obtain the weld seam with minimum width and desired depth of penetration for laser butt welding of dissimilar materials. During the process, Taguchi experiments are conducted on the laser welding of the low carbon steel (Q235) and stainless steel (SUS301L-HT). The experimental results are used to develop the radial basis function neural network model, and the process parameters are optimized by genetic algorithm. The proposed method is validated by a confirmation experiment. Simultaneously, the microstructures and mechanical properties of the weld seam generated from optimal process parameters are further studied by optical microscopy and tensile strength test. Compared with the unoptimized weld seam, the welding defects are eliminated in the optimized weld seam and the mechanical properties are improved. The results show that the proposed method is effective and reliable for improving the quality of welded joints in practical production.
NASA Technical Reports Server (NTRS)
Clancy, Daniel J.; Oezguener, Uemit; Graham, Ronald E.
1994-01-01
The potential for excessive plume impingement loads on Space Station Freedom solar arrays, caused by jet firings from an approaching Space Shuttle, is addressed. An artificial neural network is designed to determine commanded solar array beta gimbal angle for minimum plume loads. The commanded angle would be determined dynamically. The network design proposed involves radial basis functions as activation functions. Design, development, and simulation of this network design are discussed.
Zhang, Hao; Liu, Jia; Zhang, Qinglin
2014-01-01
Inventive conceptions amount to creative ideas for designing devices that are both original and useful. The generation of inventive conceptions is a key element of the inventive process. However, neural mechanisms of the inventive process remain poorly understood. Here we employed functional feature association tasks and event-related functional magnetic resonance imaging (MRI) to investigate neural substrates for the generation of inventive conceptions. The functional MRI (fMRI) data revealed significant activations at Brodmann area (BA) 47 in the left inferior frontal gyrus and at BA 18 in the left lingual gyrus, when participants performed biological functional feature association tasks compared with non-biological functional feature association tasks. Our results suggest that the left inferior frontal gyrus (BA 47) is associated with novelty-based representations formed by the generation and selection of semantic relatedness, and the left lingual gyrus (BA 18) is involved in relevant visual imagery in processing of semantic relatedness. The findings might shed light on neural mechanisms underlying the inventive process. PMID:23582377
Optimizing the De-Noise Neural Network Model for GPS Time-Series Monitoring of Structures.
Kaloop, Mosbeh R; Hu, Jong Wan
2015-09-22
The Global Positioning System (GPS) is recently used widely in structures and other applications. Notwithstanding, the GPS accuracy still suffers from the errors afflicting the measurements, particularly the short-period displacement of structural components. Previously, the multi filter method is utilized to remove the displacement errors. This paper aims at using a novel application for the neural network prediction models to improve the GPS monitoring time series data. Four prediction models for the learning algorithms are applied and used with neural network solutions: back-propagation, Cascade-forward back-propagation, adaptive filter and extended Kalman filter, to estimate which model can be recommended. The noise simulation and bridge's short-period GPS of the monitoring displacement component of one Hz sampling frequency are used to validate the four models and the previous method. The results show that the Adaptive neural networks filter is suggested for de-noising the observations, specifically for the GPS displacement components of structures. Also, this model is expected to have significant influence on the design of structures in the low frequency responses and measurements' contents.
Braunheim, B B; Schwartz, S D
2000-09-01
This paper presents a new approach to the discovery and design of bioactive compounds. The focus of this application will be on the analysis of enzymatic inhibitors. At present the discovery of enzymatic inhibitors for therapeutic use is often accomplished through random searches. The first phase of discovery is a random search through a large pre-fabricated chemical library. Many molecules are tested with refined enzyme for signs of inhibition. Once a group of lead compounds have been discovered the chemical intuition of biochemists is used to find structurally related compounds that are more effective. This step requires new molecules to be conceived and synthesized, and it is the most time-consuming and expensive step. The development of computational and theoretical methods for prediction of the molecular structure that would bind most tightly prior to synthesis and testing, would facilitate the design of novel inhibitors. In the past, our work has focused on solving the problem of predicting the bioactivity of a molecule prior to synthesis. We used a neural network trained with the bioactivity of known compounds to predict the bioactivity of unknown compounds. In our current work, we use a separate neural network in conjunction with a trained neural network in an attempt to gain insight as to how to modify existing compounds and increase their bioactivity.
Optimizing the De-Noise Neural Network Model for GPS Time-Series Monitoring of Structures.
Kaloop, Mosbeh R; Hu, Jong Wan
2015-01-01
The Global Positioning System (GPS) is recently used widely in structures and other applications. Notwithstanding, the GPS accuracy still suffers from the errors afflicting the measurements, particularly the short-period displacement of structural components. Previously, the multi filter method is utilized to remove the displacement errors. This paper aims at using a novel application for the neural network prediction models to improve the GPS monitoring time series data. Four prediction models for the learning algorithms are applied and used with neural network solutions: back-propagation, Cascade-forward back-propagation, adaptive filter and extended Kalman filter, to estimate which model can be recommended. The noise simulation and bridge's short-period GPS of the monitoring displacement component of one Hz sampling frequency are used to validate the four models and the previous method. The results show that the Adaptive neural networks filter is suggested for de-noising the observations, specifically for the GPS displacement components of structures. Also, this model is expected to have significant influence on the design of structures in the low frequency responses and measurements' contents. PMID:26402687
Braunheim, B B; Schwartz, S D
2000-09-01
This paper presents a new approach to the discovery and design of bioactive compounds. The focus of this application will be on the analysis of enzymatic inhibitors. At present the discovery of enzymatic inhibitors for therapeutic use is often accomplished through random searches. The first phase of discovery is a random search through a large pre-fabricated chemical library. Many molecules are tested with refined enzyme for signs of inhibition. Once a group of lead compounds have been discovered the chemical intuition of biochemists is used to find structurally related compounds that are more effective. This step requires new molecules to be conceived and synthesized, and it is the most time-consuming and expensive step. The development of computational and theoretical methods for prediction of the molecular structure that would bind most tightly prior to synthesis and testing, would facilitate the design of novel inhibitors. In the past, our work has focused on solving the problem of predicting the bioactivity of a molecule prior to synthesis. We used a neural network trained with the bioactivity of known compounds to predict the bioactivity of unknown compounds. In our current work, we use a separate neural network in conjunction with a trained neural network in an attempt to gain insight as to how to modify existing compounds and increase their bioactivity. PMID:10968935
Optimizing the De-Noise Neural Network Model for GPS Time-Series Monitoring of Structures
Kaloop, Mosbeh R.; Hu, Jong Wan
2015-01-01
The Global Positioning System (GPS) is recently used widely in structures and other applications. Notwithstanding, the GPS accuracy still suffers from the errors afflicting the measurements, particularly the short-period displacement of structural components. Previously, the multi filter method is utilized to remove the displacement errors. This paper aims at using a novel application for the neural network prediction models to improve the GPS monitoring time series data. Four prediction models for the learning algorithms are applied and used with neural network solutions: back-propagation, Cascade-forward back-propagation, adaptive filter and extended Kalman filter, to estimate which model can be recommended. The noise simulation and bridge’s short-period GPS of the monitoring displacement component of one Hz sampling frequency are used to validate the four models and the previous method. The results show that the Adaptive neural networks filter is suggested for de-noising the observations, specifically for the GPS displacement components of structures. Also, this model is expected to have significant influence on the design of structures in the low frequency responses and measurements’ contents. PMID:26402687
Dumidu Wijayasekara; Milos Manic; Piyush Sabharwall; Vivek Utgikar
2011-07-01
Artificial Neural Networks (ANN) have been used in the past to predict the performance of printed circuit heat exchangers (PCHE) with satisfactory accuracy. Typically published literature has focused on optimizing ANN using a training dataset to train the network and a testing dataset to evaluate it. Although this may produce outputs that agree with experimental results, there is a risk of over-training or overlearning the network rather than generalizing it, which should be the ultimate goal. An over-trained network is able to produce good results with the training dataset but fails when new datasets with subtle changes are introduced. In this paper we present EBaLM-OTR (error back propagation and Levenberg-Marquardt algorithms for over training resilience) technique, which is based on a previously discussed method of selecting neural network architecture that uses a separate validation set to evaluate different network architectures based on mean square error (MSE), and standard deviation of MSE. The method uses k-fold cross validation. Therefore in order to select the optimal architecture for the problem, the dataset is divided into three parts which are used to train, validate and test each network architecture. Then each architecture is evaluated according to their generalization capability and capability to conform to original data. The method proved to be a comprehensive tool in identifying the weaknesses and advantages of different network architectures. The method also highlighted the fact that the architecture with the lowest training error is not always the most generalized and therefore not the optimal. Using the method the testing error achieved was in the order of magnitude of within 10{sup -5} - 10{sup -3}. It was also show that the absolute error achieved by EBaLM-OTR was an order of magnitude better than the lowest error achieved by EBaLM-THP.
NASA Technical Reports Server (NTRS)
Leyland, Jane Anne
2001-01-01
Given the predicted growth in air transportation, the potential exists for significant market niches for rotary wing subsonic vehicles. Technological advances which optimise rotorcraft aeromechanical behaviour can contribute significantly to both their commercial and military development, acceptance, and sales. Examples of the optimisation of rotorcraft aeromechanical behaviour which are of interest include the minimisation of vibration and/or loads. The reduction of rotorcraft vibration and loads is an important means to extend the useful life of the vehicle and to improve its ride quality. Although vibration reduction can be accomplished by using passive dampers and/or tuned masses, active closed-loop control has the potential to reduce vibration and loads throughout a.wider flight regime whilst requiring less additional weight to the aircraft man that obtained by using passive methads. It is ernphasised that the analysis described herein is applicable to all those rotorcraft aeromechanical behaviour optimisation problems for which the relationship between the harmonic control vector and the measurement vector can be adequately described by a neural-network model.
Ansah-Antwi, KwaDwo Konadu Chua, Soo Jin; Soh, Chew Beng; Liu, Hongfei
2015-11-15
The four nearest Si(111) multifaceted sidewalls were exposed inside an array of 3 μm-wide square holes patterned on an Si(100) substrate, and this patterned Si(100) substrate was used as a substrate for the deposition of a gallium nitride (GaN) epilayer. Subsequently the effect that the growth pressure, the etched-hole profiles, and the etched-hole arrangement had upon the quality of the as-grown GaN was investigated. The coalescence of the as-grown GaN epilayer on the exposed Si(111) facets was observed to be enhanced with reduced growth pressure from 120 to 90 Torr. A larger Si(001) plane area at the bottom of the etched holes resulted in bidirectional GaN domains, which resulted in poor material quality. The bidirectional GaN domains were observed as two sets of six peaks via a high-resolution x-ray diffraction phi scan of the GaN(10-11) reflection. It was also shown that a triangular array of etched holes was more desirable than square arrays of etched holes for the growth high-quality and continuous GaN films.
Discriminant analysis and neural nets: Valuable tools to optimize completion practices
Nitters, G.; Davies, D.R.; Epping, W.J.M.
1995-06-01
This paper describes the application of multi-variate statistical techniques, discriminant analysis and neural networks in identifying drilling and other completion practices that impact on well productivity. Discriminant analysis determines whether a well can be assigned to a group of wells, on the basis of a number of common characteristics and using linear multivariate correlations. Neural nets enable the use of nonlinear correlations for such a classification. In this study, 47 gas wells from two fields were classified into three groups: Group 1 -- no production; Group 2 -- production below 5,900 std m{sup 3}/h (5 MMscf/D); Group 3 -- production over 5,900 std m{sup 3}/h (5 MMscf/D). The variables used in the discriminant analysis included parameters such as completion type, total height of the perforated interval, mud weight, drawdown during perforation, type of mud and perforation size. This study has identified and, to some extent, quantified those parameters that either adversely or favorable affect well productivity. The results can be used to adjust operational procedures to maximize well productivity. The parameters identified as increasing productivity reflect, for the most part, sound engineering practices. Application of neural nets enables further quantification of the effects of petroleum engineering parameters on well productivity and is being developed to make it possible for the most economical preventive and remedial measures to be selected. However, statistical techniques are applicable only when a sufficiently large data base is available, i.e., they are suitable for reasonably large and fairly mature fields and/or areas.
Stability of discrete time recurrent neural networks and nonlinear optimization problems.
Singh, Jayant; Barabanov, Nikita
2016-02-01
We consider the method of Reduction of Dissipativity Domain to prove global Lyapunov stability of Discrete Time Recurrent Neural Networks. The standard and advanced criteria for Absolute Stability of these essentially nonlinear systems produce rather weak results. The method mentioned above is proved to be more powerful. It involves a multi-step procedure with maximization of special nonconvex functions over polytopes on every step. We derive conditions which guarantee an existence of at most one point of local maximum for such functions over every hyperplane. This nontrivial result is valid for wide range of neuron transfer functions.
Silva, Leonardo W. T.; Barros, Vitor F.; Silva, Sandro G.
2014-01-01
In launching operations, Rocket Tracking Systems (RTS) process the trajectory data obtained by radar sensors. In order to improve functionality and maintenance, radars can be upgraded by replacing antennas with parabolic reflectors (PRs) with phased arrays (PAs). These arrays enable the electronic control of the radiation pattern by adjusting the signal supplied to each radiating element. However, in projects of phased array radars (PARs), the modeling of the problem is subject to various combinations of excitation signals producing a complex optimization problem. In this case, it is possible to calculate the problem solutions with optimization methods such as genetic algorithms (GAs). For this, the Genetic Algorithm with Maximum-Minimum Crossover (GA-MMC) method was developed to control the radiation pattern of PAs. The GA-MMC uses a reconfigurable algorithm with multiple objectives, differentiated coding and a new crossover genetic operator. This operator has a different approach from the conventional one, because it performs the crossover of the fittest individuals with the least fit individuals in order to enhance the genetic diversity. Thus, GA-MMC was successful in more than 90% of the tests for each application, increased the fitness of the final population by more than 20% and reduced the premature convergence. PMID:25196013
Silva, Leonardo W T; Barros, Vitor F; Silva, Sandro G
2014-08-18
In launching operations, Rocket Tracking Systems (RTS) process the trajectory data obtained by radar sensors. In order to improve functionality and maintenance, radars can be upgraded by replacing antennas with parabolic reflectors (PRs) with phased arrays (PAs). These arrays enable the electronic control of the radiation pattern by adjusting the signal supplied to each radiating element. However, in projects of phased array radars (PARs), the modeling of the problem is subject to various combinations of excitation signals producing a complex optimization problem. In this case, it is possible to calculate the problem solutions with optimization methods such as genetic algorithms (GAs). For this, the Genetic Algorithm with Maximum-Minimum Crossover (GA-MMC) method was developed to control the radiation pattern of PAs. The GA-MMC uses a reconfigurable algorithm with multiple objectives, differentiated coding and a new crossover genetic operator. This operator has a different approach from the conventional one, because it performs the crossover of the fittest individuals with the least fit individuals in order to enhance the genetic diversity. Thus, GA-MMC was successful in more than 90% of the tests for each application, increased the fitness of the final population by more than 20% and reduced the premature convergence.
Optimal fuel loading pattern design using an artificial neural network and a fuzzy rule-based system
Han Gon Kim; Soon Heung Chang; Byung Ho Lee )
1993-10-01
The Optimal Fuel Shuffling System (OFSS) was developed for the optimal design of pressurized water reactor (PWR) fuel loading patterns. An optimal loading pattern is defined in which the local power peaking factor is lower than a predetermined value during one cycle and the effective multiplication factor is maximized to extract the maximum energy. The OFSS is a hybrid system in which a rule-based system, fuzzy logic, and an artificial neural network (ANN) are connected with each other. The rule-based system classifies loading patterns into two types by using several heuristic rules and a fuzzy rule. The fuzzy rule is introduced to achieve a more effective and faster search. Its membership function is automatically updated in accordance with the prediction results. The ANN predicts core parameters for the patterns generated from the rule-based system. A back propagation network is used for fast prediction of the core parameters. The ANN and fuzzy logic can be used to improve the capabilities of existing algorithms. The OFSS was demonstrated and validated for cycle 1 of the Kori-1 PWR.
Barmpalexis, Panagiotis; Kachrimanis, Kyriakos; Georgarakis, Emanouil
2011-01-01
The present study investigates the use of nimodipine-polyethylene glycol solid dispersions for the development of effervescent controlled release floating tablet formulations. The physical state of the dispersed nimodipine in the polymer matrix was characterized by differential scanning calorimetry, powder X-ray diffraction, FT-IR spectroscopy and polarized light microscopy, and the mixture proportions of polyethylene glycol (PEG), polyvinyl-pyrrolidone (PVP), hydroxypropylmethylcellulose (HPMC), effervescent agents (EFF) and nimodipine were optimized in relation to drug release (% release at 60 min, and time at which the 90% of the drug was dissolved) and floating properties (tablet's floating strength and duration), employing a 25-run D-optimal mixture design combined with artificial neural networks (ANNs) and genetic programming (GP). It was found that nimodipine exists as mod I microcrystals in the solid dispersions and is stable for at least a three-month period. The tablets showed good floating properties and controlled release profiles, with drug release proceeding via the concomitant operation of swelling and erosion of the polymer matrix. ANNs and GP both proved to be efficient tools in the optimization of the tablet formulation, and the global optimum formulation suggested by the GP equations consisted of PEG=9%, PVP=30%, HPMC=36%, EFF=11%, nimodipine=14%.
Shan, Bonan; Wang, Jiang; Deng, Bin; Wei, Xile; Yu, Haitao; Zhang, Zhen; Li, Huiyan
2016-07-01
This paper proposes an epilepsy detection and closed-loop control strategy based on Particle Swarm Optimization (PSO) algorithm. The proposed strategy can effectively suppress the epileptic spikes in neural mass models, where the epileptiform spikes are recognized as the biomarkers of transitions from the normal (interictal) activity to the seizure (ictal) activity. In addition, the PSO algorithm shows capabilities of accurate estimation for the time evolution of key model parameters and practical detection for all the epileptic spikes. The estimation effects of unmeasurable parameters are improved significantly compared with unscented Kalman filter. When the estimated excitatory-inhibitory ratio exceeds a threshold value, the epileptiform spikes can be inhibited immediately by adopting the proportion-integration controller. Besides, numerical simulations are carried out to illustrate the effectiveness of the proposed method as well as the potential value for the model-based early seizure detection and closed-loop control treatment design. PMID:27475078
López-Caraballo, C H; Lazzús, J A; Salfate, I; Rojas, P; Rivera, M; Palma-Chilla, L
2015-01-01
An artificial neural network (ANN) based on particle swarm optimization (PSO) was developed for the time series prediction. The hybrid ANN+PSO algorithm was applied on Mackey-Glass chaotic time series in the short-term x(t + 6). The performance prediction was evaluated and compared with other studies available in the literature. Also, we presented properties of the dynamical system via the study of chaotic behaviour obtained from the predicted time series. Next, the hybrid ANN+PSO algorithm was complemented with a Gaussian stochastic procedure (called stochastic hybrid ANN+PSO) in order to obtain a new estimator of the predictions, which also allowed us to compute the uncertainties of predictions for noisy Mackey-Glass chaotic time series. Thus, we studied the impact of noise for several cases with a white noise level (σ(N)) from 0.01 to 0.1. PMID:26351449
López-Caraballo, C. H.; Lazzús, J. A.; Salfate, I.; Rojas, P.; Rivera, M.; Palma-Chilla, L.
2015-01-01
An artificial neural network (ANN) based on particle swarm optimization (PSO) was developed for the time series prediction. The hybrid ANN+PSO algorithm was applied on Mackey-Glass chaotic time series in the short-term x(t + 6). The performance prediction was evaluated and compared with other studies available in the literature. Also, we presented properties of the dynamical system via the study of chaotic behaviour obtained from the predicted time series. Next, the hybrid ANN+PSO algorithm was complemented with a Gaussian stochastic procedure (called stochastic hybrid ANN+PSO) in order to obtain a new estimator of the predictions, which also allowed us to compute the uncertainties of predictions for noisy Mackey-Glass chaotic time series. Thus, we studied the impact of noise for several cases with a white noise level (σN) from 0.01 to 0.1. PMID:26351449
NASA Astrophysics Data System (ADS)
Shan, Bonan; Wang, Jiang; Deng, Bin; Wei, Xile; Yu, Haitao; Zhang, Zhen; Li, Huiyan
2016-07-01
This paper proposes an epilepsy detection and closed-loop control strategy based on Particle Swarm Optimization (PSO) algorithm. The proposed strategy can effectively suppress the epileptic spikes in neural mass models, where the epileptiform spikes are recognized as the biomarkers of transitions from the normal (interictal) activity to the seizure (ictal) activity. In addition, the PSO algorithm shows capabilities of accurate estimation for the time evolution of key model parameters and practical detection for all the epileptic spikes. The estimation effects of unmeasurable parameters are improved significantly compared with unscented Kalman filter. When the estimated excitatory-inhibitory ratio exceeds a threshold value, the epileptiform spikes can be inhibited immediately by adopting the proportion-integration controller. Besides, numerical simulations are carried out to illustrate the effectiveness of the proposed method as well as the potential value for the model-based early seizure detection and closed-loop control treatment design.
Character Recognition Using Genetically Trained Neural Networks
Diniz, C.; Stantz, K.M.; Trahan, M.W.; Wagner, J.S.
1998-10-01
Computationally intelligent recognition of characters and symbols addresses a wide range of applications including foreign language translation and chemical formula identification. The combination of intelligent learning and optimization algorithms with layered neural structures offers powerful techniques for character recognition. These techniques were originally developed by Sandia National Laboratories for pattern and spectral analysis; however, their ability to optimize vast amounts of data make them ideal for character recognition. An adaptation of the Neural Network Designer soflsvare allows the user to create a neural network (NN_) trained by a genetic algorithm (GA) that correctly identifies multiple distinct characters. The initial successfid recognition of standard capital letters can be expanded to include chemical and mathematical symbols and alphabets of foreign languages, especially Arabic and Chinese. The FIN model constructed for this project uses a three layer feed-forward architecture. To facilitate the input of characters and symbols, a graphic user interface (GUI) has been developed to convert the traditional representation of each character or symbol to a bitmap. The 8 x 8 bitmap representations used for these tests are mapped onto the input nodes of the feed-forward neural network (FFNN) in a one-to-one correspondence. The input nodes feed forward into a hidden layer, and the hidden layer feeds into five output nodes correlated to possible character outcomes. During the training period the GA optimizes the weights of the NN until it can successfully recognize distinct characters. Systematic deviations from the base design test the network's range of applicability. Increasing capacity, the number of letters to be recognized, requires a nonlinear increase in the number of hidden layer neurodes. Optimal character recognition performance necessitates a minimum threshold for the number of cases when genetically training the net. And, the amount of
NASA Astrophysics Data System (ADS)
Swain, Basudev; Mishra, Chinmayee; Kang, Leeseung; Park, Kyung-Soo; Lee, Chan Gi; Hong, Hyun Seon; Park, Jeung-Jin
2015-05-01
Recovery of metal values from GaN, a metal-organic chemical vapor deposition (MOCVD) waste of GaN based power device and LED industry is investigated by acidic leaching. Leaching kinetics of gallium rich MOCVD waste is studied and the process is optimized. The gallium rich waste MOCVD dust is characterized by XRD and ICP-AES analysis followed by aqua regia digestion. Different mineral acids are used to find out the best lixiviant for selective leaching of the gallium and indium. Concentrated HCl is relatively better lixiviant having reasonably faster kinetic and better leaching efficiency. Various leaching process parameters like effect of acidity, pulp density, temperature and concentration of catalyst on the leaching efficiency of gallium and indium are investigated. Reasonably, 4 M HCl, a pulp density of 50 g/L, 100 °C and stirring rate of 400 rpm are the effective optimum condition for quantitative leaching of gallium and indium.
NASA Astrophysics Data System (ADS)
Simanowski, S.; Mermelstein, C.; Walther, M.; Herres, N.; Kiefer, R.; Rattunde, M.; Schmitz, J.; Wagner, J.; Weimann, G.
2001-07-01
The optimization of MBE growth conditions and layer structures for room temperature operation of 2.26 μm AlGaAsSb/GaInAsSb laser structures is investigated. Index guided triple quantum well large optical cavity diode lasers with 64 μm×1000 μm cavities and high reflection/antireflection coated facets reveal a cw output power of 350 mW at T=280 K. An internal quantum efficiency ηi of 69%, internal losses αi of 7.7 cm -1 and a threshold current density for infinite cavity length of j ∞=144 A/cm 2 are obtained for this structure.
NASA Astrophysics Data System (ADS)
Rossow, U.; Kruse, A.; Jönen, H.; Hoffmann, L.; Ketzer, F.; Langer, T.; Buss, R.; Bremers, H.; Hangleiter, A.; Mehrtens, T.; Schowalter, M.; Rosenauer, A.
2013-05-01
InxGaN/GaN quantum well (QW) structures grown on c-plane surfaces for long wavelength laser structures have been investigated. We found that temperature ramping in the barriers improves the layer structure in avoiding V-pit formation and improves the homogeneity of indium incorporation. In choosing proper temperature profiles degradation of the QWs can be avoided. We demonstrate optical gain for wavelengths larger than 500 nm using structures with an active zone grown in such way.
Wang, Jie-sheng; Han, Shuang; Shen, Na-na; Li, Shu-xia
2014-01-01
For meeting the forecasting target of key technology indicators in the flotation process, a BP neural network soft-sensor model based on features extraction of flotation froth images and optimized by shuffled cuckoo search algorithm is proposed. Based on the digital image processing technique, the color features in HSI color space, the visual features based on the gray level cooccurrence matrix, and the shape characteristics based on the geometric theory of flotation froth images are extracted, respectively, as the input variables of the proposed soft-sensor model. Then the isometric mapping method is used to reduce the input dimension, the network size, and learning time of BP neural network. Finally, a shuffled cuckoo search algorithm is adopted to optimize the BP neural network soft-sensor model. Simulation results show that the model has better generalization results and prediction accuracy. PMID:25133210
Wang, Jie-sheng; Han, Shuang; Shen, Na-na; Li, Shu-xia
2014-01-01
For meeting the forecasting target of key technology indicators in the flotation process, a BP neural network soft-sensor model based on features extraction of flotation froth images and optimized by shuffled cuckoo search algorithm is proposed. Based on the digital image processing technique, the color features in HSI color space, the visual features based on the gray level cooccurrence matrix, and the shape characteristics based on the geometric theory of flotation froth images are extracted, respectively, as the input variables of the proposed soft-sensor model. Then the isometric mapping method is used to reduce the input dimension, the network size, and learning time of BP neural network. Finally, a shuffled cuckoo search algorithm is adopted to optimize the BP neural network soft-sensor model. Simulation results show that the model has better generalization results and prediction accuracy.
Wang, Jie-sheng; Han, Shuang; Shen, Na-na; Li, Shu-xia
2014-01-01
For meeting the forecasting target of key technology indicators in the flotation process, a BP neural network soft-sensor model based on features extraction of flotation froth images and optimized by shuffled cuckoo search algorithm is proposed. Based on the digital image processing technique, the color features in HSI color space, the visual features based on the gray level cooccurrence matrix, and the shape characteristics based on the geometric theory of flotation froth images are extracted, respectively, as the input variables of the proposed soft-sensor model. Then the isometric mapping method is used to reduce the input dimension, the network size, and learning time of BP neural network. Finally, a shuffled cuckoo search algorithm is adopted to optimize the BP neural network soft-sensor model. Simulation results show that the model has better generalization results and prediction accuracy. PMID:25133210
Smart-Pixel Array Processors Based on Optimal Cellular Neural Networks for Space Sensor Applications
NASA Technical Reports Server (NTRS)
Fang, Wai-Chi; Sheu, Bing J.; Venus, Holger; Sandau, Rainer
1997-01-01
A smart-pixel cellular neural network (CNN) with hardware annealing capability, digitally programmable synaptic weights, and multisensor parallel interface has been under development for advanced space sensor applications. The smart-pixel CNN architecture is a programmable multi-dimensional array of optoelectronic neurons which are locally connected with their local neurons and associated active-pixel sensors. Integration of the neuroprocessor in each processor node of a scalable multiprocessor system offers orders-of-magnitude computing performance enhancements for on-board real-time intelligent multisensor processing and control tasks of advanced small satellites. The smart-pixel CNN operation theory, architecture, design and implementation, and system applications are investigated in detail. The VLSI (Very Large Scale Integration) implementation feasibility was illustrated by a prototype smart-pixel 5x5 neuroprocessor array chip of active dimensions 1380 micron x 746 micron in a 2-micron CMOS technology.
Simulation tests of the optimization method of Hopfield and Tank using neural networks
NASA Technical Reports Server (NTRS)
Paielli, Russell A.
1988-01-01
The method proposed by Hopfield and Tank for using the Hopfield neural network with continuous valued neurons to solve the traveling salesman problem is tested by simulation. Several researchers have apparently been unable to successfully repeat the numerical simulation documented by Hopfield and Tank. However, as suggested to the author by Adams, it appears that the reason for those difficulties is that a key parameter value is reported erroneously (by four orders of magnitude) in the original paper. When a reasonable value is used for that parameter, the network performs generally as claimed. Additionally, a new method of using feedback to control the input bias currents to the amplifiers is proposed and successfully tested. This eliminates the need to set the input currents by trial and error.
Optimal system size for complex dynamics in random neural networks near criticality
Wainrib, Gilles; García del Molino, Luis Carlos
2013-12-15
In this article, we consider a model of dynamical agents coupled through a random connectivity matrix, as introduced by Sompolinsky et al. [Phys. Rev. Lett. 61(3), 259–262 (1988)] in the context of random neural networks. When system size is infinite, it is known that increasing the disorder parameter induces a phase transition leading to chaotic dynamics. We observe and investigate here a novel phenomenon in the sub-critical regime for finite size systems: the probability of observing complex dynamics is maximal for an intermediate system size when the disorder is close enough to criticality. We give a more general explanation of this type of system size resonance in the framework of extreme values theory for eigenvalues of random matrices.
Paskiabi, Farnoush Asghari; Mirzaei, Esmaeil; Amani, Amir; Shokrgozar, Mohammad Ali; Saber, Reza; Faridi-Majidi, Reza
2015-11-01
This paper proposes an artificial neural networks approach to finding the effects of electrospinning parameters on alignment of poly(ɛ-caprolactone)/poly(glycolic acid) blend nanofibers. Four electrospinning parameters, namely total polymer concentration, working distance, drum speed and applied voltage were considered as input and the standard deviation of the angles of nanofibers, introducing fibers alignments, as the output of the model. The results demonstrated that drum speed and applied voltage are two critical factors influencing nanofibers alignment, however their effect are entirely interdependent. Their effects also are not independent of other electrospinning parameters. In obtaining aligned electrospun nanofibers, the concentration and working distance can also be effective. In vitro cell culture study on random and aligned nanofibers showed directional growth of cells on aligned fibers.
NASA Technical Reports Server (NTRS)
Thakoor, Anil
1990-01-01
Viewgraphs on electronic neural networks for space station are presented. Topics covered include: electronic neural networks; electronic implementations; VLSI/thin film hybrid hardware for neurocomputing; computations with analog parallel processing; features of neuroprocessors; applications of neuroprocessors; neural network hardware for terrain trafficability determination; a dedicated processor for path planning; neural network system interface; neural network for robotic control; error backpropagation algorithm for learning; resource allocation matrix; global optimization neuroprocessor; and electrically programmable read only thin-film synaptic array.
Han, Min; Fan, Jianchao; Wang, Jun
2011-09-01
A dynamic feedforward neural network (DFNN) is proposed for predictive control, whose adaptive parameters are adjusted by using Gaussian particle swarm optimization (GPSO) in the training process. Adaptive time-delay operators are added in the DFNN to improve its generalization for poorly known nonlinear dynamic systems with long time delays. Furthermore, GPSO adopts a chaotic map with Gaussian function to balance the exploration and exploitation capabilities of particles, which improves the computational efficiency without compromising the performance of the DFNN. The stability of the particle dynamics is analyzed, based on the robust stability theory, without any restrictive assumption. A stability condition for the GPSO+DFNN model is derived, which ensures a satisfactory global search and quick convergence, without the need for gradients. The particle velocity ranges could change adaptively during the optimization process. The results of a comparative study show that the performance of the proposed algorithm can compete with selected algorithms on benchmark problems. Additional simulation results demonstrate the effectiveness and accuracy of the proposed combination algorithm in identifying and controlling nonlinear systems with long time delays. PMID:21803682
Singh, Sagar; Lo, Meng-Chen; Damodaran, Vinod B; Kaplan, Hilton M; Kohn, Joachim; Zahn, Jeffrey D; Shreiber, David I
2016-01-01
Single-unit recording neural probes have significant advantages towards improving signal-to-noise ratio and specificity for signal acquisition in brain-to-computer interface devices. Long-term effectiveness is unfortunately limited by the chronic injury response, which has been linked to the mechanical mismatch between rigid probes and compliant brain tissue. Small, flexible microelectrodes may overcome this limitation, but insertion of these probes without buckling requires supporting elements such as a stiff coating with a biodegradable polymer. For these coated probes, there is a design trade-off between the potential for successful insertion into brain tissue and the degree of trauma generated by the insertion. The objective of this study was to develop and validate a finite element model (FEM) to simulate insertion of coated neural probes of varying dimensions and material properties into brain tissue. Simulations were performed to predict the buckling and insertion forces during insertion of coated probes into a tissue phantom with material properties of brain. The simulations were validated with parallel experimental studies where probes were inserted into agarose tissue phantom, ex vivo chick embryonic brain tissue, and ex vivo rat brain tissue. Experiments were performed with uncoated copper wire and both uncoated and coated SU-8 photoresist and Parylene C probes. Model predictions were found to strongly agree with experimental results (<10% error). The ratio of the predicted buckling force-to-predicted insertion force, where a value greater than one would ideally be expected to result in successful insertion, was plotted against the actual success rate from experiments. A sigmoidal relationship was observed, with a ratio of 1.35 corresponding to equal probability of insertion and failure, and a ratio of 3.5 corresponding to a 100% success rate. This ratio was dubbed the "safety factor", as it indicated the degree to which the coating should be over
Singh, Sagar; Lo, Meng-Chen; Damodaran, Vinod B.; Kaplan, Hilton M.; Kohn, Joachim; Zahn, Jeffrey D.; Shreiber, David I.
2016-01-01
Single-unit recording neural probes have significant advantages towards improving signal-to-noise ratio and specificity for signal acquisition in brain-to-computer interface devices. Long-term effectiveness is unfortunately limited by the chronic injury response, which has been linked to the mechanical mismatch between rigid probes and compliant brain tissue. Small, flexible microelectrodes may overcome this limitation, but insertion of these probes without buckling requires supporting elements such as a stiff coating with a biodegradable polymer. For these coated probes, there is a design trade-off between the potential for successful insertion into brain tissue and the degree of trauma generated by the insertion. The objective of this study was to develop and validate a finite element model (FEM) to simulate insertion of coated neural probes of varying dimensions and material properties into brain tissue. Simulations were performed to predict the buckling and insertion forces during insertion of coated probes into a tissue phantom with material properties of brain. The simulations were validated with parallel experimental studies where probes were inserted into agarose tissue phantom, ex vivo chick embryonic brain tissue, and ex vivo rat brain tissue. Experiments were performed with uncoated copper wire and both uncoated and coated SU-8 photoresist and Parylene C probes. Model predictions were found to strongly agree with experimental results (<10% error). The ratio of the predicted buckling force-to-predicted insertion force, where a value greater than one would ideally be expected to result in successful insertion, was plotted against the actual success rate from experiments. A sigmoidal relationship was observed, with a ratio of 1.35 corresponding to equal probability of insertion and failure, and a ratio of 3.5 corresponding to a 100% success rate. This ratio was dubbed the “safety factor”, as it indicated the degree to which the coating should be over
GPS receivers timing data processing using neural networks: optimal estimation and errors modeling.
Mosavi, M R
2007-10-01
The Global Positioning System (GPS) is a network of satellites, whose original purpose was to provide accurate navigation, guidance, and time transfer to military users. The past decade has also seen rapid concurrent growth in civilian GPS applications, including farming, mining, surveying, marine, and outdoor recreation. One of the most significant of these civilian applications is commercial aviation. A stand-alone civilian user enjoys an accuracy of 100 meters and 300 nanoseconds, 25 meters and 200 nanoseconds, before and after Selective Availability (SA) was turned off. In some applications, high accuracy is required. In this paper, five Neural Networks (NNs) are proposed for acceptable noise reduction of GPS receivers timing data. The paper uses from an actual data collection for evaluating the performance of the methods. An experimental test setup is designed and implemented for this purpose. The obtained experimental results from a Coarse Acquisition (C/A)-code single-frequency GPS receiver strongly support the potential of methods to give high accurate timing. Quality of the obtained results is very good, so that GPS timing RMS error reduce to less than 120 and 40 nanoseconds, with and without SA. PMID:18098370
GPS receivers timing data processing using neural networks: optimal estimation and errors modeling.
Mosavi, M R
2007-10-01
The Global Positioning System (GPS) is a network of satellites, whose original purpose was to provide accurate navigation, guidance, and time transfer to military users. The past decade has also seen rapid concurrent growth in civilian GPS applications, including farming, mining, surveying, marine, and outdoor recreation. One of the most significant of these civilian applications is commercial aviation. A stand-alone civilian user enjoys an accuracy of 100 meters and 300 nanoseconds, 25 meters and 200 nanoseconds, before and after Selective Availability (SA) was turned off. In some applications, high accuracy is required. In this paper, five Neural Networks (NNs) are proposed for acceptable noise reduction of GPS receivers timing data. The paper uses from an actual data collection for evaluating the performance of the methods. An experimental test setup is designed and implemented for this purpose. The obtained experimental results from a Coarse Acquisition (C/A)-code single-frequency GPS receiver strongly support the potential of methods to give high accurate timing. Quality of the obtained results is very good, so that GPS timing RMS error reduce to less than 120 and 40 nanoseconds, with and without SA.
Optimization of a GCaMP calcium indicator for neural activity imaging.
Akerboom, Jasper; Chen, Tsai-Wen; Wardill, Trevor J; Tian, Lin; Marvin, Jonathan S; Mutlu, Sevinç; Calderón, Nicole Carreras; Esposti, Federico; Borghuis, Bart G; Sun, Xiaonan Richard; Gordus, Andrew; Orger, Michael B; Portugues, Ruben; Engert, Florian; Macklin, John J; Filosa, Alessandro; Aggarwal, Aman; Kerr, Rex A; Takagi, Ryousuke; Kracun, Sebastian; Shigetomi, Eiji; Khakh, Baljit S; Baier, Herwig; Lagnado, Leon; Wang, Samuel S-H; Bargmann, Cornelia I; Kimmel, Bruce E; Jayaraman, Vivek; Svoboda, Karel; Kim, Douglas S; Schreiter, Eric R; Looger, Loren L
2012-10-01
Genetically encoded calcium indicators (GECIs) are powerful tools for systems neuroscience. Recent efforts in protein engineering have significantly increased the performance of GECIs. The state-of-the art single-wavelength GECI, GCaMP3, has been deployed in a number of model organisms and can reliably detect three or more action potentials in short bursts in several systems in vivo. Through protein structure determination, targeted mutagenesis, high-throughput screening, and a battery of in vitro assays, we have increased the dynamic range of GCaMP3 by severalfold, creating a family of "GCaMP5" sensors. We tested GCaMP5s in several systems: cultured neurons and astrocytes, mouse retina, and in vivo in Caenorhabditis chemosensory neurons, Drosophila larval neuromuscular junction and adult antennal lobe, zebrafish retina and tectum, and mouse visual cortex. Signal-to-noise ratio was improved by at least 2- to 3-fold. In the visual cortex, two GCaMP5 variants detected twice as many visual stimulus-responsive cells as GCaMP3. By combining in vivo imaging with electrophysiology we show that GCaMP5 fluorescence provides a more reliable measure of neuronal activity than its predecessor GCaMP3. GCaMP5 allows more sensitive detection of neural activity in vivo and may find widespread applications for cellular imaging in general.
Unsupervised neural networks for solving Troesch's problem
NASA Astrophysics Data System (ADS)
Muhammad, Asif Zahoor Raja
2014-01-01
In this study, stochastic computational intelligence techniques are presented for the solution of Troesch's boundary value problem. The proposed stochastic solvers use the competency of a feed-forward artificial neural network for mathematical modeling of the problem in an unsupervised manner, whereas the learning of unknown parameters is made with local and global optimization methods as well as their combinations. Genetic algorithm (GA) and pattern search (PS) techniques are used as the global search methods and the interior point method (IPM) is used for an efficient local search. The combination of techniques like GA hybridized with IPM (GA-IPM) and PS hybridized with IPM (PS-IPM) are also applied to solve different forms of the equation. A comparison of the proposed results obtained from GA, PS, IPM, PS-IPM and GA-IPM has been made with the standard solutions including well known analytic techniques of the Adomian decomposition method, the variational iterational method and the homotopy perturbation method. The reliability and effectiveness of the proposed schemes, in term of accuracy and convergence, are evaluated from the results of statistical analysis based on sufficiently large independent runs.
Kalderstam, Jonas; Edén, Patrik; Ohlsson, Mattias
2015-01-01
We investigate a new method to place patients into risk groups in censored survival data. Properties such as median survival time, and end survival rate, are implicitly improved by optimizing the area under the survival curve. Artificial neural networks (ANN) are trained to either maximize or minimize this area using a genetic algorithm, and combined into an ensemble to predict one of low, intermediate, or high risk groups. Estimated patient risk can influence treatment choices, and is important for study stratification. A common approach is to sort the patients according to a prognostic index and then group them along the quartile limits. The Cox proportional hazards model (Cox) is one example of this approach. Another method of doing risk grouping is recursive partitioning (Rpart), which constructs a decision tree where each branch point maximizes the statistical separation between the groups. ANN, Cox, and Rpart are compared on five publicly available data sets with varying properties. Cross-validation, as well as separate test sets, are used to validate the models. Results on the test sets show comparable performance, except for the smallest data set where Rpart’s predicted risk groups turn out to be inverted, an example of crossing survival curves. Cross-validation shows that all three models exhibit crossing of some survival curves on this small data set but that the ANN model manages the best separation of groups in terms of median survival time before such crossings. The conclusion is that optimizing the area under the survival curve is a viable approach to identify risk groups. Training ANNs to optimize this area combines two key strengths from both prognostic indices and Rpart. First, a desired minimum group size can be specified, as for a prognostic index. Second, the ability to utilize non-linear effects among the covariates, which Rpart is also able to do. PMID:26352405
Kalderstam, Jonas; Edén, Patrik; Ohlsson, Mattias
2015-01-01
We investigate a new method to place patients into risk groups in censored survival data. Properties such as median survival time, and end survival rate, are implicitly improved by optimizing the area under the survival curve. Artificial neural networks (ANN) are trained to either maximize or minimize this area using a genetic algorithm, and combined into an ensemble to predict one of low, intermediate, or high risk groups. Estimated patient risk can influence treatment choices, and is important for study stratification. A common approach is to sort the patients according to a prognostic index and then group them along the quartile limits. The Cox proportional hazards model (Cox) is one example of this approach. Another method of doing risk grouping is recursive partitioning (Rpart), which constructs a decision tree where each branch point maximizes the statistical separation between the groups. ANN, Cox, and Rpart are compared on five publicly available data sets with varying properties. Cross-validation, as well as separate test sets, are used to validate the models. Results on the test sets show comparable performance, except for the smallest data set where Rpart's predicted risk groups turn out to be inverted, an example of crossing survival curves. Cross-validation shows that all three models exhibit crossing of some survival curves on this small data set but that the ANN model manages the best separation of groups in terms of median survival time before such crossings. The conclusion is that optimizing the area under the survival curve is a viable approach to identify risk groups. Training ANNs to optimize this area combines two key strengths from both prognostic indices and Rpart. First, a desired minimum group size can be specified, as for a prognostic index. Second, the ability to utilize non-linear effects among the covariates, which Rpart is also able to do. PMID:26352405
Edupuganti, Sirisha; Sathish, Thadikamala
2014-01-01
Alpha-galactosidase production in submerged fermentation by Acinetobacter sp. was optimized using feed forward neural networks and genetic algorithm (FFNN-GA). Six different parameters, pH, temperature, agitation speed, carbon source (raffinose), nitrogen source (tryptone), and K2HPO4, were chosen and used to construct 6-10-1 topology of feed forward neural network to study interactions between fermentation parameters and enzyme yield. The predicted values were further optimized by genetic algorithm (GA). The predictability of neural networks was further analysed by using mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and R2-value for training and testing data. Using hybrid neural networks and genetic algorithm, alpha-galactosidase production was improved from 7.5 U/mL to 10.2 U/mL. PMID:25254205
Edupuganti, Sirisha; Potumarthi, Ravichandra; Sathish, Thadikamala; Mangamoori, Lakshmi Narasu
2014-01-01
Alpha-galactosidase production in submerged fermentation by Acinetobacter sp. was optimized using feed forward neural networks and genetic algorithm (FFNN-GA). Six different parameters, pH, temperature, agitation speed, carbon source (raffinose), nitrogen source (tryptone), and K2HPO4, were chosen and used to construct 6-10-1 topology of feed forward neural network to study interactions between fermentation parameters and enzyme yield. The predicted values were further optimized by genetic algorithm (GA). The predictability of neural networks was further analysed by using mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and R2-value for training and testing data. Using hybrid neural networks and genetic algorithm, alpha-galactosidase production was improved from 7.5 U/mL to 10.2 U/mL.
NASA Astrophysics Data System (ADS)
Nirmal, H. K.; Yadav, Nisha; Rahman, F.; Alvi, P. A.
2015-12-01
Most of the nano-heterostructures exhibiting lasing action in NIR (near infra-red) region that have been modeled and simulated are based on type-I category. The nano-scaled lasing heterostructures, however, of type-II category operating in SWIR (short wave infra-red) region have not been well studied. In this paper, for SWIR generation, an M-shaped type-II In0.70Ga0.30As/GaAs0.40Sb0.60 symmetric lasing nano-heterostructure has been designed. In order to simulate the optical gain, firstly the wave functions associated with conduction and valence sub-bands, carrier densities within the bands, energy band dispersion relations for the quantum well structure, optical matrix elements and finally optical gain have been studied by utilizing the six band k.p method. For the injected carrier concentration of 5 × 1012/cm2, the optimized optical gain within TE mode is as high as ∼9000/cm at the wavelength of ∼1.95 μm, thus providing a very important alternative material system for the generation of SWIR wavelength region.
Banerjee, Priya; Sau, Shubhra; Das, Papita; Mukhopadhayay, Aniruddha
2015-09-01
Azo dyes pose a major threat to current civilization by appearing in almost all streams of wastewater. The present investigation was carried out to examine the potential of Graphene oxide (GO) nanoplatelets as an efficient, cost-effective and non-toxic azo dye adsorbent for efficient wastewater treatment. The treatment process was optimized using Artificial Neural Network for maximum percentage dye removal and evaluated in terms of varying operational parameters, process kinetics and thermodynamics. A brief toxicity assay was also designed using fresh water snail Bellamya benghalensis to analyze the quality of the treated solution. 97.78% removal of safranin dye was obtained using GO as adsorbent. Characterization of GO nanoplatelets (using SEM, TEM, AFM and FTIR) reported the changes in its structure as well as surface morphology before and after use and explained its prospective as a good and environmentally benign adsorbent in very low quantities. The data recorded when subjected to different isotherms best fitted the Temkin isotherm. Further analysis revealed the process to be endothermic and chemisorption in nature. The verdict of the toxicity assay rendered the treated permeate as biologically safe for discharge or reuse in industrial and domestic purposes. PMID:25966335
Prediction of plasma processes using neural network and genetic algorithm
NASA Astrophysics Data System (ADS)
Kim, Byungwhan; Bae, Jungki
2005-10-01
Using genetic algorithm (GA) and backpropagation neural network (BPNN), computer models of plasma processes were constructed. The GA was applied to optimize five training factors simultaneously. The presented technique was evaluated with plasma etch data, characterized by a statistical experimental design. The etching was conducted in an inductively coupled plasma etch system. The etch outputs to model include aluminum (Al) etch rate, Al selectivity, silica profile angle, and DC bias. GA-BPNN models demonstrated improved predictions of more than 20% for all etch outputs but the DC bias. This indicates that a simultaneous optimization of training factors is more effective in improving the prediction performance of BPNN model than a sequential optimization of individual training factor. Compared to GA-BPNN models constructed in a previous training set, the presented models also yielded a much improved prediction of more than 35% for all etch outputs. The proven improvement indicates that the presented training set is more effective to improve GA-BPNN models.
Fedoryshyn, Yuriy; Ostinelli, Olivier; Alt, Andreas; Pallin, Angel; Bolognesi, Colombo R.
2014-01-28
The optimization of heavily strained Ga{sub 0.25}In{sub 0.75}As/Al{sub 0.48}In{sub 0.52}As high electron mobility transistor structures is discussed in detail. The growth parameters and the channel layer interfaces were optimized in order to maximize the mobility of the two-dimensional electron gas. Structures composed of an 11 nm thick channel layer and a 4 nm thick spacer layer exhibited electron mobilities as high as 15 100 cm{sup 2}/Vs and 70 000 cm{sup 2}/Vs at 300 and 77 K, respectively, for channels including InAs strained layers. The sheet carrier density was kept above 2.5 × 10{sup 12} cm{sup −2} throughout the entire study.
Zhao, Qiming; Xu, Hao; Jagannathan, Sarangapani
2015-03-01
In this paper, the finite-horizon optimal control design for nonlinear discrete-time systems in affine form is presented. In contrast with the traditional approximate dynamic programming methodology, which requires at least partial knowledge of the system dynamics, in this paper, the complete system dynamics are relaxed utilizing a neural network (NN)-based identifier to learn the control coefficient matrix. The identifier is then used together with the actor-critic-based scheme to learn the time-varying solution, referred to as the value function, of the Hamilton-Jacobi-Bellman (HJB) equation in an online and forward-in-time manner. Since the solution of HJB is time-varying, NNs with constant weights and time-varying activation functions are considered. To properly satisfy the terminal constraint, an additional error term is incorporated in the novel update law such that the terminal constraint error is also minimized over time. Policy and/or value iterations are not needed and the NN weights are updated once a sampling instant. The uniform ultimate boundedness of the closed-loop system is verified by standard Lyapunov stability theory under nonautonomous analysis. Numerical examples are provided to illustrate the effectiveness of the proposed method. PMID:25720005
Jin, Long; Zhang, Yunong
2015-07-01
In this brief, a discrete-time Zhang neural network (DTZNN) model is first proposed, developed, and investigated for online time-varying nonlinear optimization (OTVNO). Then, Newton iteration is shown to be derived from the proposed DTZNN model. In addition, to eliminate the explicit matrix-inversion operation, the quasi-Newton Broyden-Fletcher-Goldfarb-Shanno (BFGS) method is introduced, which can effectively approximate the inverse of Hessian matrix. A DTZNN-BFGS model is thus proposed and investigated for OTVNO, which is the combination of the DTZNN model and the quasi-Newton BFGS method. In addition, theoretical analyses show that, with step-size h=1 and/or with zero initial error, the maximal residual error of the DTZNN model has an O(τ(2)) pattern, whereas the maximal residual error of the Newton iteration has an O(τ) pattern, with τ denoting the sampling gap. Besides, when h ≠ 1 and h ∈ (0,2) , the maximal steady-state residual error of the DTZNN model has an O(τ(2)) pattern. Finally, an illustrative numerical experiment and an application example to manipulator motion generation are provided and analyzed to substantiate the efficacy of the proposed DTZNN and DTZNN-BFGS models for OTVNO.
Hong, Won S; Pezzi, Hannah M; Schuster, Andrea R; Berry, Scott M; Sung, Kyung E; Beebe, David J
2016-01-01
Botulinum neurotoxin (BoNT) is the most lethal naturally produced neurotoxin. Due to the extreme toxicity, BoNTs are implicated in bioterrorism, while the specific mechanism of action and long-lasting effect was found to be medically applicable in treating various neurological disorders. Therefore, for both public and patient safety, a highly sensitive, physiologic, and specific assay is needed. In this paper, we show a method for achieving a highly sensitive cell-based assay for BoNT/A detection using the motor neuron-like continuous cell line NG108-15. To achieve high sensitivity, we performed a media optimization study evaluating three commercially available neural supplements in combination with retinoic acid, purmorphamine, transforming growth factor β1 (TGFβ1), and ganglioside GT1b. We found nonlinear combinatorial effects on BoNT/A detection sensitivity, achieving an EC50 of 7.4 U ± 1.5 SD (or ~7.9 pM). The achieved detection sensitivity is comparable to that of assays that used primary and stem cell-derived neurons as well as the mouse lethality assay.
Shahsavari, Sh; Bagheri, G; Mahjub, R; Bagheri, R; Radmehr, M; Rafiee-Tehrani, M; Dorkoosh, F A
2014-03-01
The aim of this research was to develop an artificial neural network (ANN) in order to design a nanoparticulate oral drug delivery system for insulin. The pH of polymer solution (X1), concentration ratio of polymer/insulin (X2) and polymer type (X3) in 3 level including methylated N-(4-N,N- dimethyl aminobenzyl) chitosan, methylated N-(4-pyridinyl) chitosan, and methylated N-(benzyl) chitosan are considered as the input values and the particle size, zeta potential, PdI, and entrapment efficiency (EE %) as output data. ANNs are employed to generate the best model to determining the relationships between input and response values. In this research, a multi-layer percepteron with different topologies has been tested in order to define the one with the best accuracy and performance. The optimization was used by minimizing the error between the predicted and observed values. Three training algorithms (Levenberg-Marquardt (LM), Bayesian-Regularization (BR), and Gradient Descent (GD)) were employed to train ANNs with various numbers of nodes, hidden layers and transfer functions by random selection. The accuracy of prediction data were assayed by the mean squared error (MSE).The ability of all algorithms was in the order: BR>LM>GD. Thus, BR was selected as the best algorithm.
Corzo, Gerald; Solomatine, Dimitri
2007-05-01
Natural phenomena are multistationary and are composed of a number of interacting processes, so one single model handling all processes often suffers from inaccuracies. A solution is to partition data in relation to such processes using the available domain knowledge or expert judgment, to train separate models for each of the processes, and to merge them in a modular model (committee). In this paper a problem of water flow forecast in watershed hydrology is considered where the flow process can be presented as consisting of two subprocesses -- base flow and excess flow, so that these two processes can be separated. Several approaches to data separation techniques are studied. Two case studies with different forecast horizons are considered. Parameters of the algorithms responsible for data partitioning are optimized using genetic algorithms and global pattern search. It was found that modularization of ANN models using domain knowledge makes models more accurate, if compared with a global model trained on the whole data set, especially when forecast horizon (and hence the complexity of the modelled processes) is increased.
Substrate temperature optimization for Cu(In, Ga)Se2 solar cells on flexible stainless steels
NASA Astrophysics Data System (ADS)
Liang, X.; Zhu, H.; Chen, J.; Zhou, D.; Zhang, C.; Guo, Y.; Niu, X.; Li, Z.; Mai, Y.
2016-04-01
Cu(In, Ga)Se2 (CIGS) thin films are deposited on flexible stainless steel (SS) substrates using the so called 3-stage co-evaporation process at different substrate temperatures ranging from 440 °C to 640 °C during the 2nd stage and the 3rd stage (TS2). The effects of TS2 on the properties of CIGS thin films are systematically investigated. It is found by secondary ion mass spectrometry measurement that CIGS thin films deposited at different TS2 show different Ga/(Ga + In) ratio (GGI) profiles along the growth direction. High TS2 facilitates the grain growth and leads to larger grain size. However, high TS2 worsens the spectral response of CIGS solar cells in the long wavelength range, which is partly attributed to the too much iron atom diffusion from the SS substrates into the CIGS thin films. All CIGS thin films show (112) preferred orientations with a shift to higher angle due to variation of compositions. A shoulder-like two-peak structure of (112) and (220/204) peaks appears for CIGS thin films deposited at lower TS2. Conversion efficiency of 11.3% is obtained for CIGS thin film solar cells deposited at the TS2 of 500 °C.
Wang, Jie-Sheng; Han, Shuang
2015-01-01
For predicting the key technology indicators (concentrate grade and tailings recovery rate) of flotation process, a feed-forward neural network (FNN) based soft-sensor model optimized by the hybrid algorithm combining particle swarm optimization (PSO) algorithm and gravitational search algorithm (GSA) is proposed. Although GSA has better optimization capability, it has slow convergence velocity and is easy to fall into local optimum. So in this paper, the velocity vector and position vector of GSA are adjusted by PSO algorithm in order to improve its convergence speed and prediction accuracy. Finally, the proposed hybrid algorithm is adopted to optimize the parameters of FNN soft-sensor model. Simulation results show that the model has better generalization and prediction accuracy for the concentrate grade and tailings recovery rate to meet the online soft-sensor requirements of the real-time control in the flotation process.
Wang, Jie-Sheng; Han, Shuang
2015-01-01
For predicting the key technology indicators (concentrate grade and tailings recovery rate) of flotation process, a feed-forward neural network (FNN) based soft-sensor model optimized by the hybrid algorithm combining particle swarm optimization (PSO) algorithm and gravitational search algorithm (GSA) is proposed. Although GSA has better optimization capability, it has slow convergence velocity and is easy to fall into local optimum. So in this paper, the velocity vector and position vector of GSA are adjusted by PSO algorithm in order to improve its convergence speed and prediction accuracy. Finally, the proposed hybrid algorithm is adopted to optimize the parameters of FNN soft-sensor model. Simulation results show that the model has better generalization and prediction accuracy for the concentrate grade and tailings recovery rate to meet the online soft-sensor requirements of the real-time control in the flotation process. PMID:26583034
NASA Astrophysics Data System (ADS)
Lu, Qiheng; Feng, Xiaoyun
2013-03-01
After analyzing the working principle of the four-aspect fixed autoblock system, an energy-saving control model was created based on the dynamics equations of the trains in order to study the energy-saving optimal control strategy of trains in a following operation. Besides the safety and punctuality, the main aims of the model were the energy consumption and the time error. Based on this model, the static and dynamic speed restraints under a four-aspect fixed autoblock system were put forward. The multi-dimension parallel genetic algorithm (GA) and the external punishment function were adopted to solve this problem. By using the real number coding and the strategy of ramps divided into three parts, the convergence of GA was speeded up and the length of chromosomes was shortened. A vector of Gaussian random disturbance with zero mean was superposed to the mutation operator. The simulation result showed that the method could reduce the energy consumption effectively based on safety and punctuality.
Learning evasive maneuvers using evolutionary algorithms and neural networks
NASA Astrophysics Data System (ADS)
Kang, Moung Hung
In this research, evolutionary algorithms and recurrent neural networks are combined to evolve control knowledge to help pilots avoid being struck by a missile, based on a two-dimensional air combat simulation model. The recurrent neural network is used for representing the pilot's control knowledge and evolutionary algorithms (i.e., Genetic Algorithms, Evolution Strategies, and Evolutionary Programming) are used for optimizing the weights and/or topology of the recurrent neural network. The simulation model of the two-dimensional evasive maneuver problem evolved is used for evaluating the performance of the recurrent neural network. Five typical air combat conditions were selected to evaluate the performance of the recurrent neural networks evolved by the evolutionary algorithms. Analysis of Variance (ANOVA) tests and response graphs were used to analyze the results. Overall, there was little difference in the performance of the three evolutionary algorithms used to evolve the control knowledge. However, the number of generations of each algorithm required to obtain the best performance was significantly different. ES converges the fastest, followed by EP and then by GA. The recurrent neural networks evolved by the evolutionary algorithms provided better performance than the traditional recommendations for evasive maneuvers, maximum gravitational turn, for each air combat condition. Furthermore, the recommended actions of the recurrent neural networks are reasonable and can be used for pilot training.
Dietz, Roman J. B.; Globisch, Björn; Stanze, Dennis; Roehle, Helmut; Göbel, Thorsten; Schell, Martin; Gerhard, Marina; Velauthapillai, Ajanthkrishna; Koch, Martin
2013-08-05
We present results on optimized growth temperatures and layer structure design of high mobility photoconductive Terahertz (THz) emitters based on molecular beam epitaxy grown In{sub 0.53}Ga{sub 0.47}As/In{sub 0.52}Al{sub 0.48}As multilayer heterostructures (MLHS). The photoconductive antennas made of these MLHS are evaluated as THz emitters in a THz time domain spectrometer and with a Golay cell. We measured a THz bandwidth in excess of 4 THz and average THz powers of up to 64 μW corresponding to an optical power-to-THz power conversion efficiency of up to 2 × 10{sup −3}.
NASA Astrophysics Data System (ADS)
Zhu, D. L.; Wang, Q.; Han, S.; Cao, P. J.; Liu, W. J.; Jia, F.; Zeng, Y. X.; Ma, X. C.; Lu, Y. M.
2014-04-01
Ga-doped ZnO (GZO) transparent conductive thin films have been deposited on quartz substrates by r.f. magnetron sputtering. The optimization of four process parameters (i.e., vacuum annealing temperature, r.f. power, sputtering pressure, and Ar flow rate) based on Taguchi method has been systematically studied in order to obtain the minimum resistivity. Compared to the optimal parameter set selected from orthogonal array by Taguchi method, the optimal prediction design can receive an improvement of 22.3% in electrical resistivity, and the corresponding resistivity is 8.08 × 10-4 Ω cm. The analysis of variance shows that vacuum annealing temperature is the most significant influencing parameter on the electrical properties in GZO films. X-ray photoelectron spectroscopy and photoluminescence results exhibit that the enhancement in electrical conductivity after vacuum annealing is ascribed to the variation of the chemical states of oxygen in GZO films. With the increase in annealing temperature, the content of absorbed oxygen and interstitial oxygen as acceptors will decrease.
Liang, Zhengzhao; Gong, Bin; Tang, Chunan; Zhang, Yongbin; Ma, Tianhui
2014-01-01
The right bank high slope of the Dagangshan Hydroelectric Power Station is located in complicated geological conditions with deep fractures and unloading cracks. How to obtain the mechanical parameters and then evaluate the safety of the slope are the key problems. This paper presented a displacement back analysis for the slope using an artificial neural network model (ANN) and particle swarm optimization model (PSO). A numerical model was established to simulate the displacement increment results, acquiring training data for the artificial neural network model. The backpropagation ANN model was used to establish a mapping function between the mechanical parameters and the monitoring displacements. The PSO model was applied to initialize the weights and thresholds of the backpropagation (BP) network model and determine suitable values of the mechanical parameters. Then the elastic moduli of the rock masses were obtained according to the monitoring displacement data at different excavation stages, and the BP neural network model was proved to be valid by comparing the measured displacements, the displacements predicted by the BP neural network model, and the numerical simulation using the back-analyzed parameters. The proposed model is useful for rock mechanical parameters determination and instability investigation of rock slopes. PMID:25140345
Lütje, Susanne; Blex, Sebastian; Gomez, Benedikt; Schaarschmidt, Benedikt M.; Umutlu, Lale; Forsting, Michael; Jentzen, Walter; Bockisch, Andreas; Poeppel, Thorsten D.; Wetter, Axel
2016-01-01
Objective The aim of this optimization study was to minimize the acquisition time of 68Ga-HBED-CC-PSMA positron emission tomography/magnetic resonance imaging (PET/MRI) in patients with local and metastatic prostate cancer (PCa) to obtain a sufficient image quality and quantification accuracy without any appreciable loss. Methods Twenty patients with PCa were administered intravenously with the 68Ga-HBED-CC-PSMA ligand (mean activity 99 MBq/patient, range 76–148 MBq) and subsequently underwent PET/MRI at, on average, 168 min (range 77–320 min) after injection. PET and MR imaging data were acquired simultaneously. PET acquisition was performed in list mode and PET images were reconstructed at different time intervals (1, 2, 4, 6, 8, and 10 min). Data were analyzed regarding radiotracer uptake in tumors and muscle tissue and PET image quality. Tumor uptake was quantified in terms of the maximum and mean standardized uptake value (SUVmax, SUVmean) within a spherical volume of interest (VOI). Reference VOIs were drawn in the gluteus maximus muscle on the right side. PET image quality was evaluated by experienced nuclear physicians/radiologists using a five-point ordinal scale from 5–1 (excellent—insufficient). Results Lesion detectability linearly increased with increasing acquisition times, reaching its maximum at PET acquisition times of 4 min. At this image acquisition time, tumor lesions in 19/20 (95%) patients were detected. PET image quality showed a positive correlation with increasing acquisition time, reaching a plateau at 4–6 min image acquisition. Both SUVmax and SUVmean correlated inversely with acquisition time and reached a plateau at acquisition times after 4 min. Conclusion In the applied image acquisition settings, the optimal acquisition time of 68Ga-PSMA-ligand PET/MRI in patients with local and metastatic PCa was identified to be 4 min per bed position. At this acquisition time, PET image quality and lesion detectability reach a maximum
Dahlan, Irvan; Ahmad, Zainal; Fadly, Muhammad; Lee, Keat Teong; Kamaruddin, Azlina Harun; Mohamed, Abdul Rahman
2010-06-15
In this work, the application of response surface and neural network models in predicting and optimizing the preparation variables of RHA/CaO/CeO(2) sorbent towards SO(2)/NO sorption capacity was investigated. The sorbents were prepared according to central composite design (CCD) with four independent variables (i.e. hydration period, RHA/CaO ratio, CeO(2) loading and the use of RHA(raw) or pretreated RHA(600 degrees C) as the starting material). Among all the variables studied, the amount of CeO(2) loading had the largest effect. The response surface models developed from CCD was effective in providing a highly accurate prediction for SO(2) and NO sorption capacities within the range of the sorbent preparation variables studied. The prediction of CCD experiment was verified by neural network models which gave almost similar results to those determined by response surface models. The response surface models together with neural network models were then successfully used to locate and validate the optimum hydration process variables for maximizing the SO(2)/NO sorption capacities. Through this optimization process, it was found that maximum SO(2) and NO sorption capacities of 44.34 and 3.51 mg/g, respectively could be obtained by using RHA/CaO/CeO(2) sorbents prepared from RHA(raw) with hydration period of 12h, RHA/CaO ratio of 2.33 and CeO(2) loading of 8.95%.
Khajeh, Mostafa; Golzary, Ali Reza
2014-10-15
In this work, zinc nanoparticles-chitosan based solid phase extraction has been developed for separation and preconcentration of trace amount of methyl orange from water samples. Artificial neural network-cuckoo optimization algorithm has been employed to develop the model for simulation and optimization of this method. The pH, volume of elution solvent, mass of zinc oxide nanoparticles-chitosan, flow rate of sample and elution solvent were the input variables, while recovery of methyl orange was the output. The optimum conditions were obtained by cuckoo optimization algorithm. At the optimum conditions, the limit of detections of 0.7μgL(-1)was obtained for the methyl orange. The developed procedure was then applied to the separation and preconcentration of methyl orange from water samples. PMID:24835725
Khajeh, Mostafa; Golzary, Ali Reza
2014-10-15
In this work, zinc nanoparticles-chitosan based solid phase extraction has been developed for separation and preconcentration of trace amount of methyl orange from water samples. Artificial neural network-cuckoo optimization algorithm has been employed to develop the model for simulation and optimization of this method. The pH, volume of elution solvent, mass of zinc oxide nanoparticles-chitosan, flow rate of sample and elution solvent were the input variables, while recovery of methyl orange was the output. The optimum conditions were obtained by cuckoo optimization algorithm. At the optimum conditions, the limit of detections of 0.7μgL(-1)was obtained for the methyl orange. The developed procedure was then applied to the separation and preconcentration of methyl orange from water samples.
NASA Astrophysics Data System (ADS)
Khajeh, Mostafa; Golzary, Ali Reza
2014-10-01
In this work, zinc nanoparticles-chitosan based solid phase extraction has been developed for separation and preconcentration of trace amount of methyl orange from water samples. Artificial neural network-cuckoo optimization algorithm has been employed to develop the model for simulation and optimization of this method. The pH, volume of elution solvent, mass of zinc oxide nanoparticles-chitosan, flow rate of sample and elution solvent were the input variables, while recovery of methyl orange was the output. The optimum conditions were obtained by cuckoo optimization algorithm. At the optimum conditions, the limit of detections of 0.7 μg L-1was obtained for the methyl orange. The developed procedure was then applied to the separation and preconcentration of methyl orange from water samples.
NASA Astrophysics Data System (ADS)
Wang, Youhua; Wang, Junhua; Ho, S. L.; Pang, Lingling; Fu, W. N.
2011-04-01
In this paper, neural networks with a finite element method (FEM) were introduced to predict eddy current distributions on the continuously moving thin conducting strips in traveling wave induction heating (TWIH) equipments. A method that combines a neural network with a finite element method (FEM) is proposed to optimize eddy current distributions of TWIH heater. The trained network used for tested examples shows quite good accuracy of the prediction. The results have then been used with reference to a double-side TWIH in order to analyze the distributions of the magnetic field and eddy current intensity, which accelerates the iterative solution process for the nonlinear coupled electromagnetic matters. The FEM computation of temperature converged conspicuously faster using the prediction results as initial values than using the zero values, and the number of iterations is reduced dramatically. Simulation results demonstrate the effectiveness and characteristics of the proposed method.
Wajsenzon, Inês Júlia Ribas; de Carvalho, Litia Alves; Biancalana, Adriano; da Silva, Wagner Antönio Barbosa; Dos Santos Mermelstein, Claudia; de Araujo, Elizabeth Giestal; Allodi, Silvana
2016-10-01
Although there is a considerable demand for cell culture protocols from invertebrates for both basic and applied research, few attempts have been made to culture neural cells of crustaceans. We describe an in vitro method that permits the proliferation, growth and characterization of neural cells from the visual system of an adult decapod crustacean. We explain the coating of the culture plates with different adhesive substrates, and the adaptation of the medium to maintain viable neural cells for up to 7 days. Scanning electron microscopy allowed us to monitor the conditioned culture medium to assess cell morphology and cell damage. We quantified cells in the different substrates and performed statistical analyses. Of the most commonly used substrates, poly-L-ornithine was found to be the best for maintaining neural cells for 7 days. We characterized glial cells and neurons, and observed cell proliferation using immunocytochemical reactions with specific markers. This protocol was designed to aid in conducting investigations of adult crustacean neural cells in culture. We believe that an advantage of this method is the potential for adaptation to neural cells from other arthropods and even other groups of invertebrates.
NASA Astrophysics Data System (ADS)
Froio, A.; Bonifetto, R.; Carli, S.; Quartararo, A.; Savoldi, L.; Zanino, R.
2016-09-01
In superconducting tokamaks, the cryoplant provides the helium needed to cool different clients, among which by far the most important one is the superconducting magnet system. The evaluation of the transient heat load from the magnets to the cryoplant is fundamental for the design of the latter and the assessment of suitable strategies to smooth the heat load pulses, induced by the intrinsically pulsed plasma scenarios characteristic of today's tokamaks, is crucial for both suitable sizing and stable operation of the cryoplant. For that evaluation, accurate but expensive system-level models, as implemented in e.g. the validated state-of-the-art 4C code, were developed in the past, including both the magnets and the respective external cryogenic cooling circuits. Here we show how these models can be successfully substituted with cheaper ones, where the magnets are described by suitably trained Artificial Neural Networks (ANNs) for the evaluation of the heat load to the cryoplant. First, two simplified thermal-hydraulic models for an ITER Toroidal Field (TF) magnet and for the ITER Central Solenoid (CS) are developed, based on ANNs, and a detailed analysis of the chosen networks' topology and parameters is presented and discussed. The ANNs are then inserted into the 4C model of the ITER TF and CS cooling circuits, which also includes active controls to achieve a smoothing of the variation of the heat load to the cryoplant. The training of the ANNs is achieved using the results of full 4C simulations (including detailed models of the magnets) for conventional sigmoid-like waveforms of the drivers and the predictive capabilities of the ANN-based models in the case of actual ITER operating scenarios are demonstrated by comparison with the results of full 4C runs, both with and without active smoothing, in terms of both accuracy and computational time. Exploiting the low computational effort requested by the ANN-based models, a demonstrative optimization study has been
NASA Astrophysics Data System (ADS)
Tien Bui, Dieu; Pradhan, Biswajeet; Nampak, Haleh; Bui, Quang-Thanh; Tran, Quynh-An; Nguyen, Quoc-Phi
2016-09-01
This paper proposes a new artificial intelligence approach based on neural fuzzy inference system and metaheuristic optimization for flood susceptibility modeling, namely MONF. In the new approach, the neural fuzzy inference system was used to create an initial flood susceptibility model and then the model was optimized using two metaheuristic algorithms, Evolutionary Genetic and Particle Swarm Optimization. A high-frequency tropical cyclone area of the Tuong Duong district in Central Vietnam was used as a case study. First, a GIS database for the study area was constructed. The database that includes 76 historical flood inundated areas and ten flood influencing factors was used to develop and validate the proposed model. Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Receiver Operating Characteristic (ROC) curve, and area under the ROC curve (AUC) were used to assess the model performance and its prediction capability. Experimental results showed that the proposed model has high performance on both the training (RMSE = 0.306, MAE = 0.094, AUC = 0.962) and validation dataset (RMSE = 0.362, MAE = 0.130, AUC = 0.911). The usability of the proposed model was evaluated by comparing with those obtained from state-of-the art benchmark soft computing techniques such as J48 Decision Tree, Random Forest, Multi-layer Perceptron Neural Network, Support Vector Machine, and Adaptive Neuro Fuzzy Inference System. The results show that the proposed MONF model outperforms the above benchmark models; we conclude that the MONF model is a new alternative tool that should be used in flood susceptibility mapping. The result in this study is useful for planners and decision makers for sustainable management of flood-prone areas.
Neural networks for aircraft control
NASA Technical Reports Server (NTRS)
Linse, Dennis
1990-01-01
Current research in Artificial Neural Networks indicates that networks offer some potential advantages in adaptation and fault tolerance. This research is directed at determining the possible applicability of neural networks to aircraft control. The first application will be to aircraft trim. Neural network node characteristics, network topology and operation, neural network learning and example histories using neighboring optimal control with a neural net are discussed.
Tumuluru, J.S.; Sokhansanj, Shahabaddine
2008-12-01
Abstract In the present study, response surface method (RSM) and genetic algorithm (GA) were used to study the effects of process variables like screw speed, rpm (x1), L/D ratio (x2), barrel temperature ( C; x3), and feed mix moisture content (%; x4), on flow rate of biomass during single-screw extrusion cooking. A second-order regression equation was developed for flow rate in terms of the process variables. The significance of the process variables based on Pareto chart indicated that screw speed and feed mix moisture content had the most influence followed by L/D ratio and barrel temperature on the flow rate. RSM analysis indicated that a screw speed>80 rpm, L/D ratio> 12, barrel temperature>80 C, and feed mix moisture content>20% resulted in maximum flow rate. Increase in screw speed and L/D ratio increased the drag flow and also the path of traverse of the feed mix inside the extruder resulting in more shear. The presence of lipids of about 35% in the biomass feed mix might have induced a lubrication effect and has significantly influenced the flow rate. The second-order regression equations were further used as the objective function for optimization using genetic algorithm. A population of 100 and iterations of 100 have successfully led to convergence the optimum. The maximum and minimum flow rates obtained using GA were 13.19 10 7 m3/s (x1=139.08 rpm, x2=15.90, x3=99.56 C, and x4=59.72%) and 0.53 10 7 m3/s (x1=59.65 rpm, x2= 11.93, x3=68.98 C, and x4=20.04%).
Dideriksen, Jakob L.; Negro, Francesco
2015-01-01
Increasing joint stiffness by cocontraction of antagonist muscles and compensatory reflexes are neural strategies to minimize the impact of unexpected perturbations on movement. Combining these strategies, however, may compromise steadiness, as elements of the afferent input to motor pools innervating antagonist muscles are inherently negatively correlated. Consequently, a high afferent gain and active contractions of both muscles may imply negatively correlated neural drives to the muscles and thus an unstable limb position. This hypothesis was systematically explored with a novel computational model of the peripheral nervous system and the mechanics of one limb. Two populations of motor neurons received synaptic input from descending drive, spinal interneurons, and afferent feedback. Muscle force, simulated based on motor unit activity, determined limb movement that gave rise to afferent feedback from muscle spindles and Golgi tendon organs. The results indicated that optimal steadiness was achieved with low synaptic gain of the afferent feedback. High afferent gains during cocontraction implied increased levels of common drive in the motor neuron outputs, which were negatively correlated across the two populations, constraining instability of the limb. Increasing the force acting on the joint and the afferent gain both effectively minimized the impact of an external perturbation, and suboptimal adjustment of the afferent gain could be compensated by muscle cocontraction. These observations show that selection of the strategy for a given contraction implies a compromise between steadiness and effectiveness of compensations to perturbations. This indicates that a task-dependent selection of neural strategy for steadiness is necessary when acting in different environments. PMID:26203102
Dideriksen, Jakob L; Negro, Francesco; Farina, Dario
2015-09-01
Increasing joint stiffness by cocontraction of antagonist muscles and compensatory reflexes are neural strategies to minimize the impact of unexpected perturbations on movement. Combining these strategies, however, may compromise steadiness, as elements of the afferent input to motor pools innervating antagonist muscles are inherently negatively correlated. Consequently, a high afferent gain and active contractions of both muscles may imply negatively correlated neural drives to the muscles and thus an unstable limb position. This hypothesis was systematically explored with a novel computational model of the peripheral nervous system and the mechanics of one limb. Two populations of motor neurons received synaptic input from descending drive, spinal interneurons, and afferent feedback. Muscle force, simulated based on motor unit activity, determined limb movement that gave rise to afferent feedback from muscle spindles and Golgi tendon organs. The results indicated that optimal steadiness was achieved with low synaptic gain of the afferent feedback. High afferent gains during cocontraction implied increased levels of common drive in the motor neuron outputs, which were negatively correlated across the two populations, constraining instability of the limb. Increasing the force acting on the joint and the afferent gain both effectively minimized the impact of an external perturbation, and suboptimal adjustment of the afferent gain could be compensated by muscle cocontraction. These observations show that selection of the strategy for a given contraction implies a compromise between steadiness and effectiveness of compensations to perturbations. This indicates that a task-dependent selection of neural strategy for steadiness is necessary when acting in different environments.
Han, Qing-Long; Liu, Yurong; Yang, Fuwen
2016-02-01
This paper is concerned with optimal communication network-based H∞ quantized control for a discrete-time neural network with distributed time delay. Control of the neural network (plant) is implemented via a communication network. Both quantization and communication network-induced data packet dropouts are considered simultaneously. It is assumed that the plant state signal is quantized by a logarithmic quantizer before transmission, and communication network-induced packet dropouts can be described by a Bernoulli distributed white sequence. A new approach is developed such that controller design can be reduced to the feasibility of linear matrix inequalities, and a desired optimal control gain can be derived in an explicit expression. It is worth pointing out that some new techniques based on a new sector-like expression of quantization errors, and the singular value decomposition of a matrix are developed and employed in the derivation of main results. An illustrative example is presented to show the effectiveness of the obtained results.
NASA Astrophysics Data System (ADS)
Das, Chandan; Das, Arijit; Kumar Golder, Animes
2016-07-01
The present work illustrates the Microwave-Assisted Drying (MWAD) characteristic of aloe vera gel combined with process optimization and artificial neural network modeling. The influence of microwave power (160-480 W), gel quantity (4-8 g) and drying time (1-9 min) on the moisture ratio was investigated. The drying of aloe gel exhibited typical diffusion-controlled characteristics with a predominant interaction between input power and drying time. Falling rate period was observed for the entire MWAD of aloe gel. Face-centered Central Composite Design (FCCD) developed a regression model to evaluate their effects on moisture ratio. The optimal MWAD conditions were established as microwave power of 227.9 W, sample amount of 4.47 g and 5.78 min drying time corresponding to the moisture ratio of 0.15. A computer-stimulated Artificial Neural Network (ANN) model was generated for mapping between process variables and the desired response. `Levenberg-Marquardt Back Propagation' algorithm with 3-5-1 architect gave the best prediction, and it showed a clear superiority over FCCD.
Dead-space corrected GaInP/GaAs composite collector double heterojunction bipolar transistors
NASA Astrophysics Data System (ADS)
Poh, Z. S.; Yow, H. K.; Ong, D. S.; Houston, P. A.; Krysa, A. B.
2007-04-01
GaInP/GaAs/GaInP double heterojunction bipolar transistors incorporating dead-space corrected composite collectors were investigated experimentally. The optimized DHBT with a 10-nm lowly doped GaAs spacer and a 5-nm highly doped GaInP spacer has extended the operating range of the collector-emitter voltage, VCE, by maximizing the collector-emitter voltage at the onset of the multiplication, VCE ,onset, to 20 V, while minimizing the saturation voltage, VCE ,sat (<1 V), and maintaining the nominal breakdown voltage, BVCEO, of the GaInP collector at 25 V. The design incorporating an Al0.11Ga0.89As spacer rather than a GaInP spacer within the lowly doped GaAs-GaInP composite collector demonstrated similar breakdown behavior.
Du, Gang; Jiang, Zhibin; Diao, Xiaodi; Yao, Yang
2013-07-01
Takagi-Sugeno (T-S) fuzzy neural networks (FNNs) can be used to handle complex, fuzzy, uncertain clinical pathway (CP) variances. However, there are many drawbacks, such as slow training rate, propensity to become trapped in a local minimum and poor ability to perform a global search. In order to improve overall performance of variance handling by T-S FNNs, a new CP variance handling method is proposed in this study. It is based on random cooperative decomposing particle swarm optimization with double mutation mechanism (RCDPSO_DM) for T-S FNNs. Moreover, the proposed integrated learning algorithm, combining the RCDPSO_DM algorithm with a Kalman filtering algorithm, is applied to optimize antecedent and consequent parameters of constructed T-S FNNs. Then, a multi-swarm cooperative immigrating particle swarm algorithm ensemble method is used for intelligent ensemble T-S FNNs with RCDPSO_DM optimization to further improve stability and accuracy of CP variance handling. Finally, two case studies on liver and kidney poisoning variances in osteosarcoma preoperative chemotherapy are used to validate the proposed method. The result demonstrates that intelligent ensemble T-S FNNs based on the RCDPSO_DM achieves superior performances, in terms of stability, efficiency, precision and generalizability, over PSO ensemble of all T-S FNNs with RCDPSO_DM optimization, single T-S FNNs with RCDPSO_DM optimization, standard T-S FNNs, standard Mamdani FNNs and T-S FNNs based on other algorithms (cooperative particle swarm optimization and particle swarm optimization) for CP variance handling. Therefore, it makes CP variance handling more effective.
Du, Gang; Jiang, Zhibin; Diao, Xiaodi; Yao, Yang
2013-07-01
Takagi-Sugeno (T-S) fuzzy neural networks (FNNs) can be used to handle complex, fuzzy, uncertain clinical pathway (CP) variances. However, there are many drawbacks, such as slow training rate, propensity to become trapped in a local minimum and poor ability to perform a global search. In order to improve overall performance of variance handling by T-S FNNs, a new CP variance handling method is proposed in this study. It is based on random cooperative decomposing particle swarm optimization with double mutation mechanism (RCDPSO_DM) for T-S FNNs. Moreover, the proposed integrated learning algorithm, combining the RCDPSO_DM algorithm with a Kalman filtering algorithm, is applied to optimize antecedent and consequent parameters of constructed T-S FNNs. Then, a multi-swarm cooperative immigrating particle swarm algorithm ensemble method is used for intelligent ensemble T-S FNNs with RCDPSO_DM optimization to further improve stability and accuracy of CP variance handling. Finally, two case studies on liver and kidney poisoning variances in osteosarcoma preoperative chemotherapy are used to validate the proposed method. The result demonstrates that intelligent ensemble T-S FNNs based on the RCDPSO_DM achieves superior performances, in terms of stability, efficiency, precision and generalizability, over PSO ensemble of all T-S FNNs with RCDPSO_DM optimization, single T-S FNNs with RCDPSO_DM optimization, standard T-S FNNs, standard Mamdani FNNs and T-S FNNs based on other algorithms (cooperative particle swarm optimization and particle swarm optimization) for CP variance handling. Therefore, it makes CP variance handling more effective. PMID:23668338
Optimization of the ZnS Buffer Layer by Chemical Bath Deposition for Cu(In,Ga)Se2 Solar Cells.
Jeon, Dong-Hwan; Hwang, Dae-Kue; Kim, Dae-Hwan; Kang, Jin-Kyu; Lee, Chang-Seop
2016-05-01
We evaluated a ZnS buffer layer prepared using a chemical bath deposition (CBD) process for application in cadmium-free Cu(In,Ga)Se2 (CIGS) solar cells. The ZnS buffer layer showed good transmittance (above 90%) in the spectral range from 300 to 800 nm and was non-toxic compared with the CdS buffer layers normally used in CIGS solar cells. The CBD process was affected by several deposition conditions. The deposition rate was dependent on the ammonia concentration (complexing agent). When the ammonia concentration was either too high or low, a decrease in the deposition rate was observed. In addition, post heat treatments at high temperatures had detrimental influences on the ZnS buffer layers because portions of the ZnS thin films were transformed into ZnO. With optimized deposition conditions, a CIGS solar cell with a ZnS buffer layer showed an efficiency of 14.18% with a 0.23 cm2 active area under 100 mW/cm2 illumination.
Optimization of the ZnS Buffer Layer by Chemical Bath Deposition for Cu(In,Ga)Se2 Solar Cells.
Jeon, Dong-Hwan; Hwang, Dae-Kue; Kim, Dae-Hwan; Kang, Jin-Kyu; Lee, Chang-Seop
2016-05-01
We evaluated a ZnS buffer layer prepared using a chemical bath deposition (CBD) process for application in cadmium-free Cu(In,Ga)Se2 (CIGS) solar cells. The ZnS buffer layer showed good transmittance (above 90%) in the spectral range from 300 to 800 nm and was non-toxic compared with the CdS buffer layers normally used in CIGS solar cells. The CBD process was affected by several deposition conditions. The deposition rate was dependent on the ammonia concentration (complexing agent). When the ammonia concentration was either too high or low, a decrease in the deposition rate was observed. In addition, post heat treatments at high temperatures had detrimental influences on the ZnS buffer layers because portions of the ZnS thin films were transformed into ZnO. With optimized deposition conditions, a CIGS solar cell with a ZnS buffer layer showed an efficiency of 14.18% with a 0.23 cm2 active area under 100 mW/cm2 illumination. PMID:27483938
Moshayedi, Pouria; Nih, Lina R; Llorente, Irene L; Berg, Andrew R; Cinkornpumin, Jessica; Lowry, William E; Segura, Tatiana; Carmichael, S Thomas
2016-10-01
Stem cell therapies have shown promise in promoting recovery in stroke but have been limited by poor cell survival and differentiation. We have developed a hyaluronic acid (HA)-based self-polymerizing hydrogel that serves as a platform for adhesion of structural motifs and a depot release for growth factors to promote transplant stem cell survival and differentiation. We took an iterative approach in optimizing the complex combination of mechanical, biochemical and biological properties of an HA cell scaffold. First, we optimized stiffness for a minimal reaction of adjacent brain to the transplant. Next hydrogel crosslinkers sensitive to matrix metalloproteinases (MMP) were incorporated as they promoted vascularization. Finally, candidate adhesion motifs and growth factors were systemically changed in vitro using a design of experiment approach to optimize stem cell survival or proliferation. The optimized HA hydrogel, tested in vivo, promoted survival of encapsulated human neural progenitor cells (iPS-NPCs) after transplantation into the stroke core and differentially tuned transplanted cell fate through the promotion of glial, neuronal or immature/progenitor states. This HA hydrogel can be tracked in vivo with MRI. A hydrogel can serve as a therapeutic adjunct in a stem cell therapy through selective control of stem cell survival and differentiation in vivo. PMID:27521617
Choi, D J; Park, H
2001-11-01
For control and automation of biological treatment processes, lack of reliable on-line sensors to measure water quality parameters is one of the most important problems to overcome. Many parameters cannot be measured directly with on-line sensors. The accuracy of existing hardware sensors is also not sufficient and maintenance problems such as electrode fouling often cause trouble. This paper deals with the development of software sensor techniques that estimate the target water quality parameter from other parameters using the correlation between water quality parameters. We focus our attention on the preprocessing of noisy data and the selection of the best model feasible to the situation. Problems of existing approaches are also discussed. We propose a hybrid neural network as a software sensor inferring wastewater quality parameter. Multivariate regression, artificial neural networks (ANN), and a hybrid technique that combines principal component analysis as a preprocessing stage are applied to data from industrial wastewater processes. The hybrid ANN technique shows an enhancement of prediction capability and reduces the overfitting problem of neural networks. The result shows that the hybrid ANN technique can be used to extract information from noisy data and to describe the nonlinearity of complex wastewater treatment processes.
Franco, Paula G.; Pasquini, Juana M.; Silvestroff, Lucas
2015-01-01
Neural Stem and Progenitor Cells (NSC/NPC) are gathering tangible recognition for their uses in cell therapy and cell replacement therapies for human disease, as well as a model system to continue research on overall neural developmental processes in vitro. The Subventricular Zone is one of the largest NSC/NPC niches in the developing mammalian Central Nervous System, and persists through to adulthood. Oligodendrocyte progenitor cell (OPC) enriched cultures are usefull tools for in vitro studies as well as for cell replacement therapies for treating demyelination diseases. We used Subventricular Zone-derived NSC/NPC primary cultures from newborn mice and compared the effects of different growth factor combinations on cell proliferation and OPC yield. The Platelet Derived Growth Factor-AA and BB homodimers had a positive and significant impact on OPC generation. Furthermore, heparin addition to the culture media contributed to further increase overall culture yields. The OPC generated by this protocol were able to mature into Myelin Basic Protein-expressing cells and to interact with neurons in an in vitro co-culture system. As a whole, we describe an optimized in vitro method for increasing OPC. PMID:25837625
Carver, Charles S.; Scheier, Michael F.; Segerstrom, Suzanne C.
2010-01-01
Optimism is an individual difference variable that reflects the extent to which people hold generalized favorable expectancies for their future. Higher levels of optimism have been related prospectively to better subjective well-being in times of adversity or difficulty (i.e., controlling for previous well-being). Consistent with such findings, optimism has been linked to higher levels of engagement coping and lower levels of avoidance, or disengagement, coping. There is evidence that optimism is associated with taking proactive steps to protect one's health, whereas pessimism is associated with health-damaging behaviors. Consistent with such findings, optimism is also related to indicators of better physical health. The energetic, task-focused approach that optimists take to goals also relates to benefits in the socioeconomic world. Some evidence suggests that optimism relates to more persistence in educational efforts and to higher later income. Optimists also appear to fare better than pessimists in relationships. Although there are instances in which optimism fails to convey an advantage, and instances in which it may convey a disadvantage, those instances are relatively rare. In sum, the behavioral patterns of optimists appear to provide models of living for others to learn from. PMID:20170998
Han, Chao; Zhao, Shengzhi; Li, Dechun; Li, Guiqiu; Yang, Kejian; Zhang, Gang; Cheng, Kang
2011-11-01
By considering the single-photon absorption and two-photon absorption processes in the GaAs saturable absorber, the coupled rate equations for a diode-pumped passively Q-switched and mode-locked (QML) laser with GaAs coupler under Gaussian approximation are given. These rate equations are solved numerically. The key parameters of an optimally coupled passively QML laser with the shortest pulse-width envelope are determined. These key parameters include the parameters of the gain medium, the saturable absorber, and the resonator, which can minimize the pulse-width of a singly Q-switched envelope. Sample calculations for a diode-pumped passively Q-switched mode-locked c-cut Nd:GdVO(4) laser with a GaAs coupler are presented to demonstrate that the shortest pulse-width envelope can be obtained by selecting the optimal small-signal transmission of the saturable absorber and the reflectivity of the output mirror.
NASA Astrophysics Data System (ADS)
Rahimi, Masoud; Beigzadeh, Reza; Parvizi, Mehdi; Eiamsa-ard, Smith
2016-08-01
The group method of data handling (GMDH) technique was used to predict heat transfer and friction characteristics in heat exchanger tubes equipped with wire-rod bundles. Nusselt number and friction factor were determined as functions of wire-rod bundle geometric parameters and Reynolds number. The performance of the developed GMDH-type neural networks was found to be superior in comparison with the proposed empirical correlations. For optimization, the genetic algorithm-based multi-objective optimization was applied.
AlGaAs-GaAs cascade solar cell
NASA Technical Reports Server (NTRS)
Lamorte, M. F.; Abbott, D. H.
1980-01-01
Computer modeling studies are reported for a monolithic, two junction, cascade solar cell using the AlGaAs GaAs materials combination. An optimum design was obtained through a serial optimization procedure by which conversion efficiency is maximized for operation at 300 K, AM 0, and unity solar concentration. Under these conditions the upper limit on efficiency was shown to be in excess of 29 percent, provided surface recombination velocity did not exceed 10,000 cm/sec.
NASA Astrophysics Data System (ADS)
Hu, Dawei; Liu, Hong; Yang, Chenliang; Hu, Enzhu
As a subsystem of the bioregenerative life support system (BLSS), light-algae bioreactor (LABR) has properties of high reaction rate, efficiently synthesizing microalgal biomass, absorbing CO2 and releasing O2, so it is significant for BLSS to provide food and maintain gas balance. In order to manipulate the LABR properly, it has been designed as a closed-loop control system, and technology of Artificial Neural Network-Model Predictive Control (ANN-MPC) is applied to design the controller for LABR in which green microalgae, Spirulina platensis is cultivated continuously. The conclusion is drawn by computer simulation that ANN-MPC controller can intelligently learn the complicated dynamic performances of LABR, and automatically, robustly and self-adaptively regulate the light intensity illuminating on the LABR, hence make the growth of microalgae in the LABR be changed in line with the references, meanwhile provide appropriate damping to improve markedly the transient response performance of LABR.
Nelofer, Rubina; Ramanan, Ramakrishnan Nagasundara; Rahman, Raja Noor Zaliha Raja Abd; Basri, Mahiran; Ariff, Arbakariya B
2012-02-01
Response surface methodology (RSM) and artificial neural network (ANN) were used to optimize the effect of four independent variables, viz. glucose, sodium chloride (NaCl), temperature and induction time, on lipase production by a recombinant Escherichia coli BL21. The optimization and prediction capabilities of RSM and ANN were then compared. RSM predicted the dependent variable with a good coefficient of correlation determination (R² and adjusted R² values for the model. Although the R (2) value showed a good fit, absolute average deviation (AAD) and root mean square error (RMSE) values did not support the accuracy of the model and this was due to the inferiority in predicting the values towards the edges of the design points. On the other hand, ANN-predicted values were closer to the observed values with better R², adjusted R², AAD and RMSE values and this was due to the capability of predicting the values throughout the selected range of the design points. Similar to RSM, ANN could also be used to rank the effect of variables. However, ANN could not predict the interactive effect between the variables as performed by RSM. The optimum levels for glucose, NaCl, temperature and induction time predicted by RSM are 32 g/L, 5 g/L, 32°C and 2.12 h, and those by ANN are 25 g/L, 3 g/L, 30°C and 2 h, respectively. The ANN-predicted optimal levels gave higher lipase activity (55.8 IU/mL) as compared to RSM-predicted levels (50.2 IU/mL) and the predicted lipase activity was also closer to the observed data at these levels, suggesting that ANN is a better optimization method than RSM for lipase production by the recombinant strain. PMID:21833714
NASA Astrophysics Data System (ADS)
Majdabadi-Farahani, V.; Hanif, M.; Gholaminezhad, I.; Jamali, A.; Nariman-Zadeh, N.
2014-10-01
In this paper, model predictive control (MPC) is used for optimal selection of proportional-integral-derivative (PID) controller gains. In conventional tuning methods a history of response error of the system under control in the passed time is measured and used to adjust PID parameters in order to improve the performance of the system in proceeding time. But MPC obviates this characteristic of classic PID. In fact MPC tries to tune the controller by predicting the system's behaviour some time steps ahead. In this way, PID parameters are adjusted before any real error occurs in the system's response. For this purpose, polynomial meta-models based on the evolved group method of data handling neural networks are obtained to simply simulate the time response of the dynamic system. Moreover, a non-dominated sorting genetic algorithm has been used in a multi-objective Pareto optimisation to select the parameters of the MPC which are prediction horizon, control horizon and relation of weight of Δ u and error, to minimise simultaneously two objective functions that are control effort and integral time absolute error of the system response. The results mentioned at the end obviously declare that the proposed method surpasses conventional tuning methods for PID controllers, and Pareto optimal selection of predictive parameters also improves the performance of the introduced method.
Aghajani, Mohamad Hosein; Pashazadeh, Ali Mahmoud; Mostafavi, Seyed Hossein; Abbasi, Shayan; Hajibagheri-Fard, Mohammad-Javad; Assadi, Majid; Aghajani, Mahdi
2015-10-01
In this study, nanosuspension of stable iodine ((127)I) was prepared by nanoprecipitation process in microfluidic devices. Then, size of particles was optimized using artificial neural networks (ANNs) modeling. The size of prepared particles was evaluated by dynamic light scattering. The response surfaces obtained from ANNs model illustrated the determining effect of input variables (solvent and antisolvent flow rate, surfactant concentration, and solvent temperature) on the output variable (nanoparticle size). Comparing the 3D graphs revealed that solvent and antisolvent flow rate had reverse relation with size of nanoparticles. Also, those graphs indicated that the solvent temperature at low values had an indirect relation with size of stable iodine ((127)I) nanoparticles, while at the high values, a direct relation was observed. In addition, it was found that the effect of surfactant concentration on particle size in the nanosuspension of stable iodine ((127)I) was depended on the solvent temperature. Nanoprecipitation process of stable iodine (127I) and optimization of particle size using ANNs modeling.
Aghajani, Mahdi; Shahverdi, Ahmad Reza; Amani, Amir
2012-12-01
Artificial neural networks (ANNs) were used in this study to determine factors that control the polydispersity index (PDI) in an acetaminophen nanosuspension which was prepared using nanoprecipitation in microfluidic devices. The PDI of prepared formulations was measured by dynamic light scattering. Afterwards, the ANNs were applied to model the data. Four independent variables, namely, surfactant concentration, solvent temperature, and flow rate of solvent and antisolvent were considered as input variables, and the PDI of acetaminophen nanosuspension was taken as the output variable. The response surfaces, generated as 3D graphs after modeling, were used to survey the interactions happening between the input variables and the output variable. Comparison of the response surfaces indicated that the antisolvent flow rate and the solvent temperature have reverse effect on the PDI, whereas solvent flow rate has direct relation with PDI. Also, the effect of the concentration of the surfactant on the PDI was found to be indirect and less influential. Overall, it was found that minimum PDI may be obtained at high values of antisolvent flow rate and solvent temperature, while the solvent flow rate should be kept to a minimum.
Paulus, Martin P.; Flagan, Taru; Simmons, Alan N.; Gillis, Kristine; Kotturi, Sante; Thom, Nathaniel; Johnson, Douglas C.; Van Orden, Karl F.; Davenport, Paul W.; Swain, Judith L.
2012-01-01
Background It is unclear whether and how elite athletes process physiological or psychological challenges differently than healthy comparison subjects. In general, individuals optimize exercise level as it relates to differences between expected and experienced exertion, which can be conceptualized as a body prediction error. The process of computing a body prediction error involves the insular cortex, which is important for interoception, i.e. the sense of the physiological condition of the body. Thus, optimal performance may be related to efficient minimization of the body prediction error. We examined the hypothesis that elite athletes, compared to control subjects, show attenuated insular cortex activation during an aversive interoceptive challenge. Methodology/Principal Findings Elite adventure racers (n = 10) and healthy volunteers (n = 11) performed a continuous performance task with varying degrees of a non-hypercapnic breathing load while undergoing functional magnetic resonance imaging. The results indicate that (1) non-hypercapnic inspiratory breathing load is an aversive experience associated with a profound activation of a distributed set of brain areas including bilateral insula, dorsolateral prefrontal cortex and anterior cingulated; (2) adventure racers relative to comparison subjects show greater accuracy on the continuous performance task during the aversive interoceptive condition; and (3) adventure racers show an attenuated right insula cortex response during and following the aversive interoceptive condition of non-hypercapnic inspiratory breathing load. Conclusions/Significance These findings support the hypothesis that elite athletes during an aversive interoceptive condition show better performance and an attenuated insular cortex activation during the aversive experience. Interestingly, differential modulation of the right insular cortex has been found previously in elite military personnel and appears to be emerging as an important
Mathematically Reduced Chemical Reaction Mechanism Using Neural Networks
Ziaul Huque
2007-08-31
This is the final technical report for the project titled 'Mathematically Reduced Chemical Reaction Mechanism Using Neural Networks'. The aim of the project was to develop an efficient chemistry model for combustion simulations. The reduced chemistry model was developed mathematically without the need of having extensive knowledge of the chemistry involved. To aid in the development of the model, Neural Networks (NN) was used via a new network topology known as Non-linear Principal Components Analysis (NPCA). A commonly used Multilayer Perceptron Neural Network (MLP-NN) was modified to implement NPCA-NN. The training rate of NPCA-NN was improved with the GEneralized Regression Neural Network (GRNN) based on kernel smoothing techniques. Kernel smoothing provides a simple way of finding structure in data set without the imposition of a parametric model. The trajectory data of the reaction mechanism was generated based on the optimization techniques of genetic algorithm (GA). The NPCA-NN algorithm was then used for the reduction of Dimethyl Ether (DME) mechanism. DME is a recently discovered fuel made from natural gas, (and other feedstock such as coal, biomass, and urban wastes) which can be used in compression ignition engines as a substitute for diesel. An in-house two-dimensional Computational Fluid Dynamics (CFD) code was developed based on Meshfree technique and time marching solution algorithm. The project also provided valuable research experience to two graduate students.
Shibata, M; Shimura, M; Shibata, S; Wakamura, T; Moritani, T
1997-01-01
The purpose of this study was to determine the walking speed which has the greatest influence on neural relaxation in healthy elderly women as determined by electromyogram (EMG) and electroencephalogram (EEG) analyses. Seven elderly female volunteers [mean age 68.5 (SD 3.95) years] served as subjects for this study. The EMG signals were recorded from the gastrocnemius (MG), soleus (SL) and tibialis anterior (TA) muscles while walking on a treadmill, starting at 40 m.min-1 and increasing 6 m.min-1 incrementally for 10 min. The turning point of muscle activities (by integrated EMG. iEMGtp) was determined as the walking speed at the point at which the mean rate of change of iEMG (MG + SL + TA) abruptly increased. After the determination of iEMGtp. the treadmill was set at three constant speeds, one corresponding to the speed for the iEMGtp and two others 20% higher or lower than that for the iEMGtp. The subjects then walked for 20 min at each of these speeds on 3 separate days and their EEG power spectrum data were obtained for frequencies from the 8 to 13 Hz (z-wave component, AWC). The mean of iEMGtp for our subjects was at a mean walking speed of 64.7 (SD 7.9) m.min-1. Considering the subjects' age and height, iEMGtp was somewhat faster than their expected self-paced normal walking speed. There were no differences between the mean AWC values of the subjects prior to exercising at each of the three speeds. The mean AWC values after exercise were significantly (P < 0.01) greater than before. The extent of the increase in AWC at iEMGtp was greater than those at slower speeds. Our data would suggest that walking exercise at the speed which corresponds with EMG evidence of iEMGtp may induce the most significant relaxing effects in elderly women.
NASA Astrophysics Data System (ADS)
Chu, Pao-Shin; Holloway, Chris
2015-04-01
Understanding long-term changes of rainfall is important for water resources planning and development. General Circulation Models (GCMs) such as those used in CMIP5 have undergone significant improvements since the early development of Numerical Weather Prediction. CMIP5's RCP8.5 experiment was comprised of over 20 different GCM configurations using various parameterization schemes and initial conditions to project the future climate in response to anthropogenic warming. However due to coarse spatial resolution and simple parameterization schemes of GCMs, current rainfall estimates and future rainfall projections are often unrealistic, especially for small islands with complex terrains such as the Hawaiian Islands. Recent advancements in mesoscale meteorology have helped develop limited area Regional Climate Models (RCMs) such as WRF-ARW that have the ability to estimate and project high-resolution rainfall at smaller scales, in our case down to 1.1km. RCMs often use GCM output for their initial lateral boundary conditions and prescribed land surface conditions. In the original WRF system, there is a land surface model but small Hawaiian Islands such as Oahu is not well represented in the land surface datasets of the official WRF model release. Therefore, we made effort to improve land surface characteristics (e.g., albedo, green vegetation fraction) suitable for 1.1 km domain over Oahu. Since high-resolution RCM output is forced by the lateral boundary conditions, we see significant variations in estimated and future projected rainfall depending on which GCM was chosen to force the RCM. To combat this issue we implement an Artificial Neural Network using a simple Sequential Learning Algorithm (SLA) to evaluate the GCM's ability to simulate the current climate, allowing us to choose the optimum lateral boundary conditions that drive the RCM. In our study we use CMIP5's monthly means output from several different models that included both the Historical and RCP8
Rasouli, Zolaikha; Hassanzadeh, Zeinabe; Ghavami, Raouf
2016-11-01
The current study for the first time is devoted to the application of whole space genetic algorithm-radial basis function network (wsGA-RBFN) method to determine the content micro minerals of Zn(2+), Fe(2+), Co(2+) and Cu(2+) based on their complexes formation with methylthymol blue (MTB) spectrophotometrically in various pharmaceutical products and vegetable samples. Advantage of wsGA-RBFN compared to GA-RBFN is that centers can be located in any point of the samples spaces. Initially, the parameters controlling behavior of the system were investigated and optimum conditions were selected. Then, an exploratory analysis of complex systems was carried out by chemometrics approaches such as SVD, EFA, MCR-ALS and RAFA. The optimal parameters and conditions for constructing the proposed model of wsGA-RBFN were obtained from processing the data set of synthetic samples. Finally, wsGA-RBFN was successfully applied to the simultaneous determination of Zn(2+), Fe(2+), Co(2+) and Cu(2+) in tomato, white cabbage, red cabbage and lettuce and pharmaceutical products included iron, zinc, multi complete and B12 ampoule. PMID:27591591
NASA Astrophysics Data System (ADS)
Veal, T. D.; Lowe, M. J.; McConville, C. F.
2002-03-01
High-resolution electron-energy-loss spectroscopy (HREELS) and synchrotron-radiation photoemission spectroscopy (SRPES) have been used to study the Sb-stabilised GaSb(1 0 0)-(1×3) surface prepared by a two-stage low-temperature atomic hydrogen cleaning (AHC) procedure. The use of a maximum annealing temperature of 300 °C avoids the degradation of surface stoichiometry associated with higher annealing temperatures. After AHC at a sample temperature of 100 °C, SRPES results show that all Sb oxides have been removed and only a small amount of Ga oxide remains. Further AHC treatment at 300 °C results in a clean surface with a sharp (1×3) low energy electron diffraction pattern. SRPES results indicate that the surface stoichiometry is identical to that previously found for GaSb(1 0 0)-(1×3) prepared by in situ molecular beam epitaxy. Electron energy-dependent HREEL spectra exhibit a coupled plasmon-phonon mode which has been used to study the electronic structure of the near-surface region. Semi-classical dielectric theory simulations of the HREEL spectra of the clean GaSb(1 0 0)-(1×3) surface indicate no detectable electronic damage or dopant passivation results from the AHC treatment. Valence band SRPES indicates that the surface Fermi level is close to the valence band maximum, suggesting the presence of an inversion layer at the surface.
Zhao, Guo; Wang, Hui; Liu, Gang; Wang, Zhiqiang
2016-01-01
An easy, but effective, method has been proposed to detect and quantify the Pb(II) in the presence of Cd(II) based on a Bi/glassy carbon electrode (Bi/GCE) with the combination of a back propagation artificial neural network (BP-ANN) and square wave anodic stripping voltammetry (SWASV) without further electrode modification. The effects of Cd(II) in different concentrations on stripping responses of Pb(II) was studied. The results indicate that the presence of Cd(II) will reduce the prediction precision of a direct calibration model. Therefore, a two-input and one-output BP-ANN was built for the optimization of a stripping voltammetric sensor, which considering the combined effects of Cd(II) and Pb(II) on the SWASV detection of Pb(II) and establishing the nonlinear relationship between the stripping peak currents of Pb(II) and Cd(II) and the concentration of Pb(II). The key parameters of the BP-ANN and the factors affecting the SWASV detection of Pb(II) were optimized. The prediction performance of direct calibration model and BP-ANN model were tested with regard to the mean absolute error (MAE), root mean square error (RMSE), average relative error (ARE), and correlation coefficient. The results proved that the BP-ANN model exhibited higher prediction accuracy than the direct calibration model. Finally, a real samples analysis was performed to determine trace Pb(II) in some soil specimens with satisfactory results. PMID:27657083
Ghaedi, M; Shojaeipour, E; Ghaedi, A M; Sahraei, Reza
2015-05-01
In this study, copper nanowires loaded on activated carbon (Cu-NWs-AC) was used as novel efficient adsorbent for the removal of malachite green (MG) from aqueous solution. This new material was synthesized through simple protocol and its surface properties such as surface area, pore volume and functional groups were characterized with different techniques such XRD, BET and FESEM analysis. The relation between removal percentages with variables such as solution pH, adsorbent dosage (0.005, 0.01, 0.015, 0.02 and 0.1g), contact time (1-40min) and initial MG concentration (5, 10, 20, 70 and 100mg/L) was investigated and optimized. A three-layer artificial neural network (ANN) model was utilized to predict the malachite green dye removal (%) by Cu-NWs-AC following conduction of 248 experiments. When the training of the ANN was performed, the parameters of ANN model were as follows: linear transfer function (purelin) at output layer, Levenberg-Marquardt algorithm (LMA), and a tangent sigmoid transfer function (tansig) at the hidden layer with 11 neurons. The minimum mean squared error (MSE) of 0.0017 and coefficient of determination (R(2)) of 0.9658 were found for prediction and modeling of dye removal using testing data set. A good agreement between experimental data and predicted data using the ANN model was obtained. Fitting the experimental data on previously optimized condition confirm the suitability of Langmuir isotherm models for their explanation with maximum adsorption capacity of 434.8mg/g at 25°C. Kinetic studies at various adsorbent mass and initial MG concentration show that the MG maximum removal percentage was achieved within 20min. The adsorption of MG follows the pseudo-second-order with a combination of intraparticle diffusion model. PMID:25699703
Ghaedi, M; Shojaeipour, E; Ghaedi, A M; Sahraei, Reza
2015-05-01
In this study, copper nanowires loaded on activated carbon (Cu-NWs-AC) was used as novel efficient adsorbent for the removal of malachite green (MG) from aqueous solution. This new material was synthesized through simple protocol and its surface properties such as surface area, pore volume and functional groups were characterized with different techniques such XRD, BET and FESEM analysis. The relation between removal percentages with variables such as solution pH, adsorbent dosage (0.005, 0.01, 0.015, 0.02 and 0.1g), contact time (1-40min) and initial MG concentration (5, 10, 20, 70 and 100mg/L) was investigated and optimized. A three-layer artificial neural network (ANN) model was utilized to predict the malachite green dye removal (%) by Cu-NWs-AC following conduction of 248 experiments. When the training of the ANN was performed, the parameters of ANN model were as follows: linear transfer function (purelin) at output layer, Levenberg-Marquardt algorithm (LMA), and a tangent sigmoid transfer function (tansig) at the hidden layer with 11 neurons. The minimum mean squared error (MSE) of 0.0017 and coefficient of determination (R(2)) of 0.9658 were found for prediction and modeling of dye removal using testing data set. A good agreement between experimental data and predicted data using the ANN model was obtained. Fitting the experimental data on previously optimized condition confirm the suitability of Langmuir isotherm models for their explanation with maximum adsorption capacity of 434.8mg/g at 25°C. Kinetic studies at various adsorbent mass and initial MG concentration show that the MG maximum removal percentage was achieved within 20min. The adsorption of MG follows the pseudo-second-order with a combination of intraparticle diffusion model.
NASA Astrophysics Data System (ADS)
Ghaedi, M.; Shojaeipour, E.; Ghaedi, A. M.; Sahraei, Reza
2015-05-01
In this study, copper nanowires loaded on activated carbon (Cu-NWs-AC) was used as novel efficient adsorbent for the removal of malachite green (MG) from aqueous solution. This new material was synthesized through simple protocol and its surface properties such as surface area, pore volume and functional groups were characterized with different techniques such XRD, BET and FESEM analysis. The relation between removal percentages with variables such as solution pH, adsorbent dosage (0.005, 0.01, 0.015, 0.02 and 0.1 g), contact time (1-40 min) and initial MG concentration (5, 10, 20, 70 and 100 mg/L) was investigated and optimized. A three-layer artificial neural network (ANN) model was utilized to predict the malachite green dye removal (%) by Cu-NWs-AC following conduction of 248 experiments. When the training of the ANN was performed, the parameters of ANN model were as follows: linear transfer function (purelin) at output layer, Levenberg-Marquardt algorithm (LMA), and a tangent sigmoid transfer function (tansig) at the hidden layer with 11 neurons. The minimum mean squared error (MSE) of 0.0017 and coefficient of determination (R2) of 0.9658 were found for prediction and modeling of dye removal using testing data set. A good agreement between experimental data and predicted data using the ANN model was obtained. Fitting the experimental data on previously optimized condition confirm the suitability of Langmuir isotherm models for their explanation with maximum adsorption capacity of 434.8 mg/g at 25 °C. Kinetic studies at various adsorbent mass and initial MG concentration show that the MG maximum removal percentage was achieved within 20 min. The adsorption of MG follows the pseudo-second-order with a combination of intraparticle diffusion model.
Hashad, Rania A; Ishak, Rania A H; Fahmy, Sherif; Mansour, Samar; Geneidi, Ahmed S
2016-05-01
At a novel pH value of the polymeric solution (6.2), variable chitosan (Cs) and sodium tripolyphosphate (TPP) concentrations and mass ratios were optimized to improve the process yield without undesirable particle flocculation. Prepared formulations were characterized in terms of particle size (PS), zeta potential (ZP) and percentage yield (% yield). Artificial neural networks (ANN) were built up and used to identify the parameters that control nanoparticle (NP) size and yield, in addition to being tested for their ability to predict these two experimental outputs. Using these networks, it was found that TPP concentration has the greatest effect on PS and% yield. The most optimum formulation was characterized by a notable process yield reaching 91.5%, a mean hydrodynamic PS 227 nm, ZP+24.13 mv and spherical compact morphology. Successful Cs-TPP interaction in NP formation was confirmed by both Fourier transform-infrared spectroscopy (FT-IR) and differential scanning calorimetry (DSC). This study demonstrated the ability of ANN to predict not only PS of the formed particles but also NP% yield. This may have a great impact on Cs-TPP NPs preparation and can be used to customize the required target formulations. PMID:26783636
Xu, Hao; Zhao, Qiming; Jagannathan, Sarangapani
2015-08-01
The output feedback-based near-optimal regulation of uncertain and quantized nonlinear discrete-time systems in affine form with control constraint over finite horizon is addressed in this paper. First, the effect of input constraint is handled using a nonquadratic cost functional. Next, a neural network (NN)-based Luenberger observer is proposed to reconstruct both the system states and the control coefficient matrix so that a separate identifier is not needed. Then, approximate dynamic programming-based actor-critic framework is utilized to approximate the time-varying solution of the Hamilton-Jacobi-Bellman using NNs with constant weights and time-dependent activation functions. A new error term is defined and incorporated in the NN update law so that the terminal constraint error is also minimized over time. Finally, a novel dynamic quantizer for the control inputs with adaptive step size is designed to eliminate the quantization error overtime, thus overcoming the drawback of the traditional uniform quantizer. The proposed scheme functions in a forward-in-time manner without offline training phase. Lyapunov analysis is used to investigate the stability. Simulation results are given to show the effectiveness and feasibility of the proposed method. PMID:25794403
Xu, Hao; Zhao, Qiming; Jagannathan, Sarangapani
2015-08-01
The output feedback-based near-optimal regulation of uncertain and quantized nonlinear discrete-time systems in affine form with control constraint over finite horizon is addressed in this paper. First, the effect of input constraint is handled using a nonquadratic cost functional. Next, a neural network (NN)-based Luenberger observer is proposed to reconstruct both the system states and the control coefficient matrix so that a separate identifier is not needed. Then, approximate dynamic programming-based actor-critic framework is utilized to approximate the time-varying solution of the Hamilton-Jacobi-Bellman using NNs with constant weights and time-dependent activation functions. A new error term is defined and incorporated in the NN update law so that the terminal constraint error is also minimized over time. Finally, a novel dynamic quantizer for the control inputs with adaptive step size is designed to eliminate the quantization error overtime, thus overcoming the drawback of the traditional uniform quantizer. The proposed scheme functions in a forward-in-time manner without offline training phase. Lyapunov analysis is used to investigate the stability. Simulation results are given to show the effectiveness and feasibility of the proposed method.
Adam-Rebeles, R; Van den Winkel, P; De Vis, L
2007-09-01
Optimization of the production parameters (incident and exit proton energy, thickness of the (68)Zn target layer, decay time to start chemical processing of an irradiated target after the end of bombardment) and of the thickness of the lead shield of the processing hotcell for the cyclotron production of (67)Ga by the (68)Zn(p,2n) threshold reaction are accomplished by powerful divide et impera and binary search algorithms with the Pharmacopoeia radionuclidic purity of the (67)Ga-citrate radiopharmaceutical at a reference time and the locally accepted dose rate level for the controlled area as boundary conditions. Two sets of equations are presented (one associated with the maximum production rate, the other with the use of a minimum target layer thickness) that allow the expression of the optimized production parameters, the radionuclide yields satisfying the Pharmacopoeia requirements at the start of distribution and the necessary shielding as a function of the required activity at the start of distribution and of the maximum allowable beam current on target.
NASA Astrophysics Data System (ADS)
Zhou, Shu-Xing; Qi, Ming; Ai, Li-Kun; Xu, An-Huai
2016-09-01
The structure of InP-based InxGa1-xAs/In0.52Al0.48As pseudomorphic high electron mobility transistor (PHEMT) was optimized in detail. Effects of growth temperature, growth interruption time, Si δ-doping condition, channel thickness and In content, and inserted AlAs monolayer (ML) on the two-dimensional electron gas (2DEG) performance were investigated carefully. It was found that the use of the inserted AlAs monolayer has an enhancement effect on the mobility due to the reduction of interface roughness and the suppression of Si movement. With optimization of the growth parameters, the structures composed of a 10 nm thick In0.75Ga0.25As channel layer and a 3 nm thick AlAs/In0.52Al0.48As superlattices spacer layer exhibited electron mobilities as high as 12500 cm2·V-1·s-1 (300 K) and 53500 cm2·V-1·s-1 (77 K) and the corresponding sheet carrier concentrations (Ns) of 2.8 × 1012 cm-2 and 2.9 × 1012 cm-2, respectively. To the best of the authors’ knowledge, this is the highest reported room temperature mobility for InP-based HEMTs with a spacer of 3 nm to date. Project supported by the National Natural Science Foundation of China (Grant No. 61434006).
NASA Astrophysics Data System (ADS)
Zhou, Shu-Xing; Qi, Ming; Ai, Li-Kun; Xu, An-Huai
2016-09-01
The structure of InP-based InxGa1‑xAs/In0.52Al0.48As pseudomorphic high electron mobility transistor (PHEMT) was optimized in detail. Effects of growth temperature, growth interruption time, Si δ-doping condition, channel thickness and In content, and inserted AlAs monolayer (ML) on the two-dimensional electron gas (2DEG) performance were investigated carefully. It was found that the use of the inserted AlAs monolayer has an enhancement effect on the mobility due to the reduction of interface roughness and the suppression of Si movement. With optimization of the growth parameters, the structures composed of a 10 nm thick In0.75Ga0.25As channel layer and a 3 nm thick AlAs/In0.52Al0.48As superlattices spacer layer exhibited electron mobilities as high as 12500 cm2·V‑1·s‑1 (300 K) and 53500 cm2·V‑1·s‑1 (77 K) and the corresponding sheet carrier concentrations (Ns) of 2.8 × 1012 cm‑2 and 2.9 × 1012 cm‑2, respectively. To the best of the authors’ knowledge, this is the highest reported room temperature mobility for InP-based HEMTs with a spacer of 3 nm to date. Project supported by the National Natural Science Foundation of China (Grant No. 61434006).
Crystal growth of device quality GaAs in space
NASA Technical Reports Server (NTRS)
Gatos, H. C.; Lagowski, J.
1979-01-01
The optimization of space processing of GaAs is described. The detailed compositional, structural, and electronic characterization of GaAs on a macro- and microscale and the relationships between growth parameters and the properties of GaAs are among the factors discussed. The key parameters limiting device performance are assessed.
GaAs Optoelectronic Integrated-Circuit Neurons
NASA Technical Reports Server (NTRS)
Lin, Steven H.; Kim, Jae H.; Psaltis, Demetri
1992-01-01
Monolithic GaAs optoelectronic integrated circuits developed for use as artificial neurons. Neural-network computer contains planar arrays of optoelectronic neurons, and variable synaptic connections between neurons effected by diffraction of light from volume hologram in photorefractive material. Basic principles of neural-network computers explained more fully in "Optoelectronic Integrated Circuits For Neural Networks" (NPO-17652). In present circuits, devices replaced by metal/semiconductor field effect transistors (MESFET's), which consume less power.
NASA Astrophysics Data System (ADS)
Basumatary, Bikramjit; Maity, Santanu; Muchahary, Deboraj
2016-09-01
For the high-power application, high breakdown voltage of the HEMT device is required that is free from the negative effects such as current collapse and NDC. In this paper, it is found that without any substrate for the higher dimension, the breakdown is larger than the lower dimension. However, it is not free from NDC, which can be eliminated by the shorter channel. Back barrier layer is used to increase this breakdown voltage for the shorter dimension which has given very efficient result, doubling the voltage from 6 V to 13 V for the 'x' composition of 0.3 of AlxGa1-xN and 16 V for AlN back barrier. At the end, the relation of capacitance to the breakdown voltage for the shorter channel is derived, which shows that the breakdown voltage is mostly dependent on the carrier concentration in the lower layer.
Optimized support vector regression for drilling rate of penetration estimation
NASA Astrophysics Data System (ADS)
Bodaghi, Asadollah; Ansari, Hamid Reza; Gholami, Mahsa
2015-12-01
In the petroleum industry, drilling optimization involves the selection of operating conditions for achieving the desired depth with the minimum expenditure while requirements of personal safety, environment protection, adequate information of penetrated formations and productivity are fulfilled. Since drilling optimization is highly dependent on the rate of penetration (ROP), estimation of this parameter is of great importance during well planning. In this research, a novel approach called `optimized support vector regression' is employed for making a formulation between input variables and ROP. Algorithms used for optimizing the support vector regression are the genetic algorithm (GA) and the cuckoo search algorithm (CS). Optimization implementation improved the support vector regression performance by virtue of selecting proper values for its parameters. In order to evaluate the ability of optimization algorithms in enhancing SVR performance, their results were compared to the hybrid of pattern search and grid search (HPG) which is conventionally employed for optimizing SVR. The results demonstrated that the CS algorithm achieved further improvement on prediction accuracy of SVR compared to the GA and HPG as well. Moreover, the predictive model derived from back propagation neural network (BPNN), which is the traditional approach for estimating ROP, is selected for comparisons with CSSVR. The comparative results revealed the superiority of CSSVR. This study inferred that CSSVR is a viable option for precise estimation of ROP.
Juang, Bor-Chau Laghumavarapu, Ramesh B.; Foggo, Brandon J.; Lin, Andrew; Simmonds, Paul J.; Liang, Baolai; Huffaker, Diana L.
2015-03-16
There exists a long-term need for foreign substrates on which to grow GaSb-based optoelectronic devices. We address this need by using interfacial misfit arrays to grow GaSb-based thermophotovoltaic cells directly on GaAs (001) substrates and demonstrate promising performance. We compare these cells to control devices grown on GaSb substrates to assess device properties and material quality. The room temperature dark current densities show similar characteristics for both cells on GaAs and on GaSb. Under solar simulation the cells on GaAs exhibit an open-circuit voltage of 0.121 V and a short-circuit current density of 15.5 mA/cm{sup 2}. In addition, the cells on GaAs substrates maintain 10% difference in spectral response to those of the control cells over a large range of wavelengths. While the cells on GaSb substrates in general offer better performance than the cells on GaAs substrates, the cost-savings and scalability offered by GaAs substrates could potentially outweigh the reduction in performance. By further optimizing GaSb buffer growth on GaAs substrates, Sb-based compound semiconductors grown on GaAs substrates with similar performance to devices grown directly on GaSb substrates could be realized.
Pathak, Lakshmi; Singh, Vineeta; Niwas, Ram; Osama, Khwaja; Khan, Saif; Haque, Shafiul; Tripathi, C K M; Mishra, B N
2015-01-01
Cholesterol oxidase (COD) is a bi-functional FAD-containing oxidoreductase which catalyzes the oxidation of cholesterol into 4-cholesten-3-one. The wider biological functions and clinical applications of COD have urged the screening, isolation and characterization of newer microbes from diverse habitats as a source of COD and optimization and over-production of COD for various uses. The practicability of statistical/ artificial intelligence techniques, such as response surface methodology (RSM), artificial neural network (ANN) and genetic algorithm (GA) have been tested to optimize the medium composition for the production of COD from novel strain Streptomyces sp. NCIM 5500. All experiments were performed according to the five factor central composite design (CCD) and the generated data was analysed using RSM and ANN. GA was employed to optimize the models generated by RSM and ANN. Based upon the predicted COD concentration, the model developed with ANN was found to be superior to the model developed with RSM. The RSM-GA approach predicted maximum of 6.283 U/mL COD production, whereas the ANN-GA approach predicted a maximum of 9.93 U/mL COD concentration. The optimum concentrations of the medium variables predicted through ANN-GA approach were: 1.431 g/50 mL soybean, 1.389 g/50 mL maltose, 0.029 g/50 mL MgSO4, 0.45 g/50 mL NaCl and 2.235 ml/50 mL glycerol. The experimental COD concentration was concurrent with the GA predicted yield and led to 9.75 U/mL COD production, which was nearly two times higher than the yield (4.2 U/mL) obtained with the un-optimized medium. This is the very first time we are reporting the statistical versus artificial intelligence based modeling and optimization of COD production by Streptomyces sp. NCIM 5500.
Niwas, Ram; Osama, Khwaja; Khan, Saif; Haque, Shafiul; Tripathi, C. K. M.; Mishra, B. N.
2015-01-01
Cholesterol oxidase (COD) is a bi-functional FAD-containing oxidoreductase which catalyzes the oxidation of cholesterol into 4-cholesten-3-one. The wider biological functions and clinical applications of COD have urged the screening, isolation and characterization of newer microbes from diverse habitats as a source of COD and optimization and over-production of COD for various uses. The practicability of statistical/ artificial intelligence techniques, such as response surface methodology (RSM), artificial neural network (ANN) and genetic algorithm (GA) have been tested to optimize the medium composition for the production of COD from novel strain Streptomyces sp. NCIM 5500. All experiments were performed according to the five factor central composite design (CCD) and the generated data was analysed using RSM and ANN. GA was employed to optimize the models generated by RSM and ANN. Based upon the predicted COD concentration, the model developed with ANN was found to be superior to the model developed with RSM. The RSM-GA approach predicted maximum of 6.283 U/mL COD production, whereas the ANN-GA approach predicted a maximum of 9.93 U/mL COD concentration. The optimum concentrations of the medium variables predicted through ANN-GA approach were: 1.431 g/50 mL soybean, 1.389 g/50 mL maltose, 0.029 g/50 mL MgSO4, 0.45 g/50 mL NaCl and 2.235 ml/50 mL glycerol. The experimental COD concentration was concurrent with the GA predicted yield and led to 9.75 U/mL COD production, which was nearly two times higher than the yield (4.2 U/mL) obtained with the un-optimized medium. This is the very first time we are reporting the statistical versus artificial intelligence based modeling and optimization of COD production by Streptomyces sp. NCIM 5500. PMID:26368924
Song, Xianzhi; Peng, Chi; Li, Gensheng
2016-01-01
Sand production and blockage are common during the drilling and production of horizontal oil and gas wells as a result of formation breakdown. The use of high-pressure rotating jets and annular helical flow is an effective way to enhance horizontal wellbore cleanout. In this paper, we propose the idea of using supercritical CO2 (SC-CO2) as washing fluid in water-sensitive formation. SC-CO2 is manifested to be effective in preventing formation damage and enhancing production rate as drilling fluid, which justifies tis potential in wellbore cleanout. In order to investigate the effectiveness of SC-CO2 helical flow cleanout, we perform the numerical study on the annular flow field, which significantly affects sand cleanout efficiency, of SC-CO2 jets in horizontal wellbore. Based on the field data, the geometry model and mathematical models were built. Then a numerical simulation of the annular helical flow field by SC-CO2 jets was accomplished. The influences of several key parameters were investigated, and SC-CO2 jets were compared to conventional water jets. The results show that flow rate, ambient temperature, jet temperature, and nozzle assemblies play the most important roles on wellbore flow field. Once the difference between ambient temperatures and jet temperatures is kept constant, the wellbore velocity distributions will not change. With increasing lateral nozzle size or decreasing rear/forward nozzle size, suspending ability of SC-CO2 flow improves obviously. A back-propagation artificial neural network (BP-ANN) was successfully employed to match the operation parameters and SC-CO2 flow velocities. A comprehensive model was achieved to optimize the operation parameters according to two strategies: cost-saving strategy and local optimal strategy. This paper can help to understand the distinct characteristics of SC-CO2 flow. And it is the first time that the BP-ANN is introduced to analyze the flow field during wellbore cleanout in horizontal wells. PMID
Song, Xianzhi; Peng, Chi; Li, Gensheng; He, Zhenguo; Wang, Haizhu
2016-01-01
Sand production and blockage are common during the drilling and production of horizontal oil and gas wells as a result of formation breakdown. The use of high-pressure rotating jets and annular helical flow is an effective way to enhance horizontal wellbore cleanout. In this paper, we propose the idea of using supercritical CO2 (SC-CO2) as washing fluid in water-sensitive formation. SC-CO2 is manifested to be effective in preventing formation damage and enhancing production rate as drilling fluid, which justifies tis potential in wellbore cleanout. In order to investigate the effectiveness of SC-CO2 helical flow cleanout, we perform the numerical study on the annular flow field, which significantly affects sand cleanout efficiency, of SC-CO2 jets in horizontal wellbore. Based on the field data, the geometry model and mathematical models were built. Then a numerical simulation of the annular helical flow field by SC-CO2 jets was accomplished. The influences of several key parameters were investigated, and SC-CO2 jets were compared to conventional water jets. The results show that flow rate, ambient temperature, jet temperature, and nozzle assemblies play the most important roles on wellbore flow field. Once the difference between ambient temperatures and jet temperatures is kept constant, the wellbore velocity distributions will not change. With increasing lateral nozzle size or decreasing rear/forward nozzle size, suspending ability of SC-CO2 flow improves obviously. A back-propagation artificial neural network (BP-ANN) was successfully employed to match the operation parameters and SC-CO2 flow velocities. A comprehensive model was achieved to optimize the operation parameters according to two strategies: cost-saving strategy and local optimal strategy. This paper can help to understand the distinct characteristics of SC-CO2 flow. And it is the first time that the BP-ANN is introduced to analyze the flow field during wellbore cleanout in horizontal wells. PMID
Working toward high-power GaN/InGaN heterojunction bipolar transistors
NASA Astrophysics Data System (ADS)
Shen, Shyh-Chiang; Dupuis, Russell D.; Lochner, Zachery; Lee, Yi-Che; Kao, Tsung-Ting; Zhang, Yun; Kim, Hee-Jin; Ryou, Jae-Hyun
2013-07-01
III-nitride (III-N) heterojunction bipolar transistors (HBTs) are a less-explored electronic device technology due to the myriad research issues in material growth, device design and fabrication associated with these devices. For III-N HBTs, npn-GaN/InGaN heterostructures provide the benefits of mitigating the poor base electrical conductivity of p-type GaN and the problematic magnesium incorporation issues. Consequently, InGaN-base III-N HBTs are promising for next-generation high-power RF III-N systems. This paper will describe the current development status of npn GaN/InGaN HBTs grown either on sapphire or free-standing (FS) GaN substrates using optimized metalorganic chemical vapor deposition (MOCVD) and refined HBT processing techniques. Recombination current paths in GaN/InGaN HBTs are studied and small-signal equivalent circuits are developed. The extracted device model indicates that, with further device fabrication technique development, Johnson's figure of merit (JFOM) of GaN/InGaN HBTs can be as high as 5 THz V.
XROUTE: A knowledge-based routing system using neural networks and genetic algorithms
Kadaba, N.
1990-01-01
This dissertation is concerned with applying alternative methods of artificial intelligence (AI) in conjunction with mathematical methods to Vehicle Routing Problems. The combination of good mathematical models, knowledge-based systems, artificial neural networks, and adaptive genetic algorithms (GA) - which are shown to be synergistic - produces near-optimal results, which none of the individual methods can produce on its own. A significant problem associated with application of the Back Propagation learning paradigm for pattern classification with neural networks is the lack of high accuracy in generalization when the domain is large. In this work, a multiple neural network system is employed, using two self-organizing neural networks that work as feature extractors, producing information that is used to train a generalization neural network. The technique was successfully applied to the selection of control rules for a Traveling Salesman Problem heuristic, thus making it adaptive to the input problem instance. XROUTE provides an interactive visualization system, using state-of-the-art vehicle routing models and AI tools, yet allows an interactive environment for human expertise to be utilized in powerful ways. XROUTE provides an experimental, exploratory framework that allows many variations, and alternatives to problems with different characteristics. XROUTE is dynamic, expandable, and adaptive, and typically outperforms alternative methods in computer-aided vehicle routing.
Evolving neural networks for detecting breast cancer.
Fogel, D B; Wasson, E C; Boughton, E M
1995-09-01
Artificial neural networks are applied to the problem of detecting breast cancer from histologic data. Evolutionary programming is used to train the networks. This stochastic optimization method reduces the chance of becoming trapped in locally optimal weight sets. Preliminary results indicate that very parsimonious neural nets can outperform other methods reported in the literature on the same data. The results are statistically significant.
Neural Architectures for Control
NASA Technical Reports Server (NTRS)
Peterson, James K.
1991-01-01
The cerebellar model articulated controller (CMAC) neural architectures are shown to be viable for the purposes of real-time learning and control. Software tools for the exploration of CMAC performance are developed for three hardware platforms, the MacIntosh, the IBM PC, and the SUN workstation. All algorithm development was done using the C programming language. These software tools were then used to implement an adaptive critic neuro-control design that learns in real-time how to back up a trailer truck. The truck backer-upper experiment is a standard performance measure in the neural network literature, but previously the training of the controllers was done off-line. With the CMAC neural architectures, it was possible to train the neuro-controllers on-line in real-time on a MS-DOS PC 386. CMAC neural architectures are also used in conjunction with a hierarchical planning approach to find collision-free paths over 2-D analog valued obstacle fields. The method constructs a coarse resolution version of the original problem and then finds the corresponding coarse optimal path using multipass dynamic programming. CMAC artificial neural architectures are used to estimate the analog transition costs that dynamic programming requires. The CMAC architectures are trained in real-time for each obstacle field presented. The coarse optimal path is then used as a baseline for the construction of a fine scale optimal path through the original obstacle array. These results are a very good indication of the potential power of the neural architectures in control design. In order to reach as wide an audience as possible, we have run a seminar on neuro-control that has met once per week since 20 May 1991. This seminar has thoroughly discussed the CMAC architecture, relevant portions of classical control, back propagation through time, and adaptive critic designs.
Modeling and simulation of InGaN/GaN quantum dots solar cell
NASA Astrophysics Data System (ADS)
Aissat, A.; Benyettou, F.; Vilcot, J. P.
2016-07-01
Currently, quantum dots have attracted attention in the field of optoelectronics, and are used to overcome the limits of a conventional solar cell. Here, an In0.25Ga0.75N/GaN Quantum Dots Solar Cell has been modeled and simulated using Silvaco Atlas. Our results show that the short circuit current increases with the insertion of the InGaN quantum dots inside the intrinsic region of a GaN pin solar cell. In contrary, the open circuit voltage decreases. A relative optimization of the conversion efficiency of 54.77% was achieved comparing a 5-layers In0.25Ga0.75N/GaN quantum dots with pin solar cell. The conversion efficiency begins to decline beyond 5-layers quantum dots introduced. Indium composition of 10 % improves relatively the efficiency about 42.58% and a temperature of 285 K gives better conversion efficiency of 13.14%.
Ligand "Brackets" for Ga-Ga Bond.
Fedushkin, Igor L; Skatova, Alexandra A; Dodonov, Vladimir A; Yang, Xiao-Juan; Chudakova, Valentina A; Piskunov, Alexander V; Demeshko, Serhiy; Baranov, Evgeny V
2016-09-01
The reactivity of digallane (dpp-Bian)Ga-Ga(dpp-Bian) (1) (dpp-Bian = 1,2-bis[(2,6-diisopropylphenyl)imino]acenaphthene) toward acenaphthenequinone (AcQ), sulfur dioxide, and azobenzene was investigated. The reaction of 1 with AcQ in 1:1 molar ratio proceeds via two-electron reduction of AcQ to give (dpp-Bian)Ga(μ2-AcQ)Ga(dpp-Bian) (2), in which diolate [AcQ](2-) acts as "bracket" for the Ga-Ga bond. The interaction of 1 with AcQ in 1:2 molar ratio proceeds with an oxidation of the both dpp-Bian ligands as well as of the Ga-Ga bond to give (dpp-Bian)Ga(μ2-AcQ)2Ga(dpp-Bian) (3). At 330 K in toluene complex 2 decomposes to give compounds 3 and 1. The reaction of complex 2 with atmospheric oxygen results in oxidation of a Ga-Ga bond and affords (dpp-Bian)Ga(μ2-AcQ)(μ2-O)Ga(dpp-Bian) (4). The reaction of digallane 1 with SO2 produces, depending on the ratio (1:2 or 1:4), dithionites (dpp-Bian)Ga(μ2-O2S-SO2)Ga(dpp-Bian) (5) and (dpp-Bian)Ga(μ2-O2S-SO2)2Ga(dpp-Bian) (6). In compound 5 the Ga-Ga bond is preserved and supported by dithionite dianionic bracket. In compound 6 the gallium centers are bridged by two dithionite ligands. Both 5 and 6 consist of dpp-Bian radical anionic ligands. Four-electron reduction of azobenzene with 1 mol equiv of digallane 1 leads to complex (dpp-Bian)Ga(μ2-NPh)2Ga(dpp-Bian) (7). Paramagnetic compounds 2-7 were characterized by electron spin resonance spectroscopy, and their molecular structures were established by single-crystal X-ray analysis. Magnetic behavior of compounds 2, 5, and 6 was investigated by superconducting quantum interference device technique in the range of 2-295 K. PMID:27548713
Optimizing white light luminescence in Dy{sup 3+}-doped Lu{sub 3}Ga{sub 5}O{sub 12} nano-garnets
Haritha, P.; Linganna, K.; Venkatramu, V.; Martín, I. R.; Monteseguro, V.; Rodríguez-Mendoza, U. R.; Babu, P.; León-Luis, S. F.; Jayasankar, C. K.; Lavín, V.
2014-11-07
Trivalent dysprosium-doped Lu{sub 3}Ga{sub 5}O{sub 12} nano-garnets have been prepared by sol-gel method and characterized by X-ray powder diffraction, high-resolution transmission electron microscopy, dynamic light scattering, and laser excited spectroscopy. Under a cw 457 nm laser excitation, the white luminescence properties of Lu{sub 3}Ga{sub 5}O{sub 12} nano-garnets have been studied as a function of the optically active Dy{sup 3+} ion concentration and at low temperature. Decay curves for the {sup 4}F{sub 9/2} level of Dy{sup 3+} ion exhibit non-exponential nature for all the Dy{sup 3+} concentrations, which have been well-fitted to a generalized energy transfer model for a quadrupole-quadrupole interaction between Dy{sup 3+} ions without diffusion. From these data, a simple rate-equations model can be applied to predict that intense white luminescence could be obtained from 1.8 mol% Dy{sup 3+} ions-doped nano-garnets, which is in good agreement with experimental results. Chromaticity color coordinates and correlated color temperatures have been determined as a function of temperature and are found to be within the white light region for all Dy{sup 3+} concentrations. These results indicate that 2.0 mol% Dy{sup 3+} ions doped nano-garnet could be useful for white light emitting device applications.
High efficiency, low cost thin GaAs solar cells
NASA Technical Reports Server (NTRS)
Fan, J. C. C.
1982-01-01
The feasibility of fabricating space-resistant, high efficiency, light-weight, low-cost GaAs shallow-homojunction solar cells for space application is demonstrated. This program addressed the optimal preparation of ultrathin GaAs single-crystal layers by AsCl3-GaAs-H2 and OMCVD process. Considerable progress has been made in both areas. Detailed studies on the AsCl3 process showed high-quality GaAs thin layers can be routinely grown. Later overgrowth of GaAs by OMCVD has been also observed and thin FaAs films were obtained from this process.
Applications of artificial neural nets in structural mechanics
NASA Technical Reports Server (NTRS)
Berke, Laszlo; Hajela, Prabhat
1990-01-01
A brief introduction to the fundamental of Neural Nets is given, followed by two applications in structural optimization. In the first case, the feasibility of simulating with neural nets the many structural analyses performed during optimization iterations was studied. In the second case, the concept of using neural nets to capture design expertise was studied.
Applications of artificial neural nets in structural mechanics
NASA Technical Reports Server (NTRS)
Berke, L.; Hajela, P.
1992-01-01
A brief introduction to the fundamental of Neural Nets is given, followed by two applications in structural optimization. In the first case, the feasibility of simulating with neural nets the many structural analyses performed during optimization iterations was studied. In the second case, the concept of using neural nets to capture design expertise was studied.
Near-surface depletion of antimony during the growth of GaAsSb and GaAs/GaAsSb nanowires
Kauko, H.; Helvoort, A. T. J. van; Fimland, B. O.; Munshi, A. M.; Grieb, T.; Müller, K.; Rosenauer, A.
2014-10-14
The near-surface reduction of the Sb mole fraction during the growth of GaAsSb nanowires (NWs) and GaAs NWs with GaAsSb inserts has been studied using quantitative high-angle annular dark field scanning transmission electron microscopy (STEM). A model for diffusion of Sb in the hexagonal NWs was developed and employed in combination with the quantitative STEM analysis. GaAsSb NWs grown by Ga-assisted molecular beam epitaxy (MBE) and GaAs/GaAsSb NWs grown by Ga- and Au-assisted MBE were investigated. At the high temperatures employed in the NW growth, As-Sb exchange at and outward diffusion of Sb towards the surface take place, resulting in reduction of the Sb concentration at and near the surface in the GaAsSb NWs and the GaAsSb inserts. In GaAsSb NWs, an increasing near-surface depletion of Sb was observed towards the bottom of the NW due to longer exposure to the As beam flux. In GaAsSb inserts, an increasing change in the Sb concentration profile was observed with increasing post-insert axial GaAs growth time, resulting from a combined effect of radial GaAs overgrowth and diffusion of Sb. The effect of growth temperature on the diffusion of Sb in the GaAsSb inserts was identified. The consequences of these findings for growth optimization and the optoelectronic properties of GaAsSb are discussed.
Process dependency on threshold voltage of GaN MOSFET on AlGaN/GaN heterostructure
NASA Astrophysics Data System (ADS)
Wang, Qingpeng; Jiang, Ying; Miyashita, Takahiro; Motoyama, Shin-ichi; Li, Liuan; Wang, Dejun; Ohno, Yasuo; Ao, Jin-Ping
2014-09-01
GaN metal-oxide-semiconductor field-effect transistors (MOSFETs) with recessed gate on AlGaN/GaN heterostructure are reported in which the drain and source ohmic contacts were fabricated on the AlGaN/GaN heterostructure and the electron channel was formed on the GaN buffer layer by removing the AlGaN barrier layer. Negative threshold voltages were commonly observed in all devices. To investigate the reasons of the negative threshold voltages, different oxide thickness, etching gas and bias power of inductively-coupled plasma (ICP) system were utilized in the fabrication process of the GaN MOSFETs. It is found that positive charges of around 1 × 1012 q/cm2 exist near the interface at the just threshold condition in both silane- and tetraethylorthosilicate (TEOS)-based devices. It is also found that the threshold voltages do not obviously change with the different etching gas (SiCl4, BCl3 and two-step etching of SiCl4/Cl2) at the same ICP bias power level (20-25 W) and will become deeper when higher bias power is used in the dry recess process which may be related to the much serious ion bombardment damage. Furthermore, X-ray photoelectron spectroscopy (XPS) experiments were done to investigate the surface conditions. It is found that N 1s peaks become lower with higher bias power of the dry etching process. Also, silicon contamination was found and could be removed by HNO3/HF solution. It indicates that the nitrogen vacancies are mainly responsible for the negative threshold voltages rather than the silicon contamination. It demonstrates that optimization of the ICP recess conditions and improvement of the surface condition are still necessary to realize enhancement-mode GaN MOSFETs on AlGaN/GaN heterostructure.
Production of Engineered Fabrics Using Artificial Neural Network-Genetic Algorithm Hybrid Model
NASA Astrophysics Data System (ADS)
Mitra, Ashis; Majumdar, Prabal Kumar; Banerjee, Debamalya
2015-10-01
The process of fabric engineering which is generally practised in most of the textile mills is very complicated, repetitive, tedious and time consuming. To eliminate this trial and error approach, a new approach of fabric engineering has been attempted in this work. Data sets of construction parameters [comprising of ends per inch, picks per inch, warp count and weft count] and three fabric properties (namely drape coefficient, air permeability and thermal resistance) of 25 handloom cotton fabrics have been used. The weights and biases of three artificial neural network (ANN) models developed for the prediction of drape coefficient, air permeability and thermal resistance were used to formulate the fitness or objective function and constraints of the optimization problem. The optimization problem was solved using genetic algorithm (GA). In both the fabrics which were attempted for engineering, the target and simulated fabric properties were very close. The GA was able to search the optimum set of fabric construction parameters with reasonably good accuracy except in case of EPI. However, the overall result is encouraging and can be improved further by using larger data sets of handloom fabrics by hybrid ANN-GA model.
NASA Astrophysics Data System (ADS)
Hsiao, Feng-Hsiag
2016-10-01
In this study, a novel approach via improved genetic algorithm (IGA)-based fuzzy observer is proposed to realise exponential optimal H∞ synchronisation and secure communication in multiple time-delay chaotic (MTDC) systems. First, an original message is inserted into the MTDC system. Then, a neural-network (NN) model is employed to approximate the MTDC system. Next, a linear differential inclusion (LDI) state-space representation is established for the dynamics of the NN model. Based on this LDI state-space representation, this study proposes a delay-dependent exponential stability criterion derived in terms of Lyapunov's direct method, thus ensuring that the trajectories of the slave system approach those of the master system. Subsequently, the stability condition of this criterion is reformulated into a linear matrix inequality (LMI). Due to GA's random global optimisation search capabilities, the lower and upper bounds of the search space can be set so that the GA will seek better fuzzy observer feedback gains, accelerating feedback gain-based synchronisation via the LMI-based approach. IGA, which exhibits better performance than traditional GA, is used to synthesise a fuzzy observer to not only realise the exponential synchronisation, but also achieve optimal H∞ performance by minimizing the disturbance attenuation level and recovering the transmitted message. Finally, a numerical example with simulations is given in order to demonstrate the effectiveness of our approach.
NASA Astrophysics Data System (ADS)
Krishna, Hemanth; Kumar, Hemantha; Gangadharan, Kalluvalappil
2016-06-01
A magneto rheological (MR) fluid damper offers cost effective solution for semiactive vibration control in an automobile suspension. The performance of MR damper is significantly depends on the electromagnetic circuit incorporated into it. The force developed by MR fluid damper is highly influenced by the magnetic flux density induced in the fluid flow gap. In the present work, optimization of electromagnetic circuit of an MR damper is discussed in order to maximize the magnetic flux density. The optimization procedure was proposed by genetic algorithm and design of experiments techniques. The result shows that the fluid flow gap size less than 1.12 mm cause significant increase of magnetic flux density.
Growth of InGaN/GaN quantum wells with graded InGaN buffer for green-to-yellow light emitters
NASA Astrophysics Data System (ADS)
Hu, Chia-Hsuan; Lo, Ikai; Hsu, Yu-Chi; Shih, Cheng-Hung; Pang, Wen-Yuan; Wang, Ying-Chieh; Lin, Yu-Chiao; Yang, Chen-Chi; Tsai, Cheng-Da; Hsu, Gary Z. L.
2016-08-01
We have studied the growth of high-indium-content In x Ga1‑ x N/GaN double quantum wells (QWs) for yellow and green light emitters by plasma-assisted molecular beam epitaxy at a low substrate temperature (570 °C). By introducing a graded In y Ga1‑ y N buffer layer, the PL intensity of QWs can be increased sixfold compared with that of the original structure. In addition, the indium content in InGaN QWs was increased owing the prolonged growth time of the graded In y Ga1‑ y N buffer layer. After adjusting to optimal growth conditions, we achieved In x Ga1‑ x N/GaN QWs with x = 0.32. Photoluminescence measurements showed that the emission wavelength from In x Ga1‑ x N/GaN QWs was 560 nm (2.20 eV). The optimal condition for the gradient In y Ga1‑ y N buffer layer was obtained for light emission from green to yellow.
Tuning range and output power optimization of an external-cavity GaN diode laser at 455 nm.
Chi, Mingjun; Jensen, Ole Bjarlin; Petersen, Paul Michael
2016-03-20
In this paper we discuss how different feedback gratings affect the tuning range and the output power of external feedback diode laser systems. A tunable high-power narrow-spectrum external-cavity diode laser system around 455 nm is investigated. The laser system is based on a high-power GaN diode laser in a Littrow external-cavity. Both a holographic diffraction grating and a ruled diffraction grating are used as feedback elements in the external cavity. The output power, spectral bandwidth, and tunable range of the external cavity diode laser system are measured and compared with the two gratings at different injected currents. When the holographic grating is used, the laser system can be tuned over a range of 1.4 nm with an output power around 530 mW. When the ruled grating is used, the laser system can be tuned over a range of 6.0 nm with an output power around 80 mW. The results can be used as a guide for selecting gratings for external-cavity diode lasers for different requirements. PMID:27140561
Tuning range and output power optimization of an external-cavity GaN diode laser at 455 nm.
Chi, Mingjun; Jensen, Ole Bjarlin; Petersen, Paul Michael
2016-03-20
In this paper we discuss how different feedback gratings affect the tuning range and the output power of external feedback diode laser systems. A tunable high-power narrow-spectrum external-cavity diode laser system around 455 nm is investigated. The laser system is based on a high-power GaN diode laser in a Littrow external-cavity. Both a holographic diffraction grating and a ruled diffraction grating are used as feedback elements in the external cavity. The output power, spectral bandwidth, and tunable range of the external cavity diode laser system are measured and compared with the two gratings at different injected currents. When the holographic grating is used, the laser system can be tuned over a range of 1.4 nm with an output power around 530 mW. When the ruled grating is used, the laser system can be tuned over a range of 6.0 nm with an output power around 80 mW. The results can be used as a guide for selecting gratings for external-cavity diode lasers for different requirements.
Power Disturbances Classification Using S-Transform Based GA-PNN
NASA Astrophysics Data System (ADS)
Manimala, K.; Selvi, K.
2015-09-01
The significance of detection and classification of power quality events that disturb the voltage and/or current waveforms in the electrical power distribution networks is well known. Consequently, in spite of a large number of research reports in this area, a research on the selection of proper parameter for specific classifiers was so far not explored. The parameter selection is very important for successful modelling of input-output relationship in a function approximation model. In this study, probabilistic neural network (PNN) has been used as a function approximation tool for power disturbance classification and genetic algorithm (GA) is utilised for optimisation of the smoothing parameter of the PNN. The important features extracted from raw power disturbance signal using S-Transform are given to the PNN for effective classification. The choice of smoothing parameter for PNN classifier will significantly impact the classification accuracy. Hence, GA based parameter optimization is done to ensure good classification accuracy by selecting suitable parameter of the PNN classifier. Testing results show that the proposed S-Transform based GA-PNN model has better classification ability than classifiers based on conventional grid search method for parameter selection. The noisy and practical signals are considered for the classification process to show the effectiveness of the proposed method in comparison with existing methods.
Uhrig, R.E.; Emrich, M.L.
1990-01-01
The topics covered in this report are: Learning, Memory, and Artificial Neural Systems; Emerging Neural Network Technology; Neural Networks; Digital Signal Processing and Neural Networks; Application of Neural Networks to In-Core Fuel Management; Neural Networks in Process Control; Neural Network Applications in Image Processing; Neural Networks for Multi-Sensor Information Fusion; Neural Network Research in Instruments Controls Division; Neural Networks Research in the ORNL Engineering Physics and Mathematics Division; Neural Network Applications for Linear Programming; Neural Network Applications to Signal Processing and Diagnostics; Neural Networks in Filtering and Control; Neural Network Research at Tennessee Technological University; and Global Minima within the Hopfield Hypercube.
Particle Swarm Optimization Toolbox
NASA Technical Reports Server (NTRS)
Grant, Michael J.
2010-01-01
The Particle Swarm Optimization Toolbox is a library of evolutionary optimization tools developed in the MATLAB environment. The algorithms contained in the library include a genetic algorithm (GA), a single-objective particle swarm optimizer (SOPSO), and a multi-objective particle swarm optimizer (MOPSO). Development focused on both the SOPSO and MOPSO. A GA was included mainly for comparison purposes, and the particle swarm optimizers appeared to perform better for a wide variety of optimization problems. All algorithms are capable of performing unconstrained and constrained optimization. The particle swarm optimizers are capable of performing single and multi-objective optimization. The SOPSO and MOPSO algorithms are based on swarming theory and bird-flocking patterns to search the trade space for the optimal solution or optimal trade in competing objectives. The MOPSO generates Pareto fronts for objectives that are in competition. A GA, based on Darwin evolutionary theory, is also included in the library. The GA consists of individuals that form a population in the design space. The population mates to form offspring at new locations in the design space. These offspring contain traits from both of the parents. The algorithm is based on this combination of traits from parents to hopefully provide an improved solution than either of the original parents. As the algorithm progresses, individuals that hold these optimal traits will emerge as the optimal solutions. Due to the generic design of all optimization algorithms, each algorithm interfaces with a user-supplied objective function. This function serves as a "black-box" to the optimizers in which the only purpose of this function is to evaluate solutions provided by the optimizers. Hence, the user-supplied function can be numerical simulations, analytical functions, etc., since the specific detail of this function is of no concern to the optimizer. These algorithms were originally developed to support entry
Koblmueller, G.; Chu, R. M.; Raman, A.; Mishra, U. K.; Speck, J. S.
2010-02-15
We present combined in situ thermal cleaning and intentional doping strategies near the substrate regrowth interface to produce high-quality AlGaN/GaN high electron mobility transistors on semi-insulating (0001) GaN templates with low interfacial impurity concentrations and low buffer leakage. By exposing the GaN templates to an optimized thermal dissociation step in the plasma-assisted molecular beam epitaxy environment, oxygen, carbon, and, to lesser extent, Si impurities were effectively removed from the regrowth interface under preservation of good interface quality. Residual Si was further compensated by C-doped GaN via CBr{sub 4} to yield highly resistive GaN buffer layers. Improved N-rich growth conditions at high growth temperatures were then utilized for subsequent growth of the AlGaN/GaN device structure, yielding smooth surface morphologies and low residual oxygen concentration with large insensitivity to the (Al+Ga)N flux ratio. Room temperature electron mobilities of the two-dimensional electron gas at the AlGaN/GaN interface exceeded >1750 cm{sup 2}/V s and the dc drain current reached {approx}1.1 A/mm at a +1 V bias, demonstrating the effectiveness of the applied methods.
Nonlinear programming with feedforward neural networks.
Reifman, J.
1999-06-02
We provide a practical and effective method for solving constrained optimization problems by successively training a multilayer feedforward neural network in a coupled neural-network/objective-function representation. Nonlinear programming problems are easily mapped into this representation which has a simpler and more transparent method of solution than optimization performed with Hopfield-like networks and poses very mild requirements on the functions appearing in the problem. Simulation results are illustrated and compared with an off-the-shelf optimization tool.
GA-ANFIS Expert System Prototype for Prediction of Dermatological Diseases.
Begic Fazlic, Lejla; Avdagic, Korana; Omanovic, Samir
2015-01-01
This paper presents novel GA-ANFIS expert system prototype for dermatological disease detection by using dermatological features and diagnoses collected in real conditions. Nine dermatological features are used as inputs to classifiers that are based on Adaptive Neuro-Fuzzy Inference Systems (ANFIS) for the first level of fuzzy model optimization. After that, they are used as inputs in Genetic Algorithm (GA) for the second level of fuzzy model optimization within GA-ANFIS system. GA-ANFIS system performs optimization in two steps. Modelling and validation of the novel GA-ANFIS system approach is performed in MATLAB environment by using validation set of data. Some conclusions concerning the impacts of features on the detection of dermatological diseases were obtained through analysis of the GA-ANFIS. We compared GA-ANFIS and ANFIS results. The results confirmed that the proposed GA-ANFIS model achieved accuracy rates which are higher than the ones we got by ANFIS model. PMID:25991223
Naresh-Kumar, G. Trager-Cowan, C.; Vilalta-Clemente, A.; Morales, M.; Ruterana, P.; Pandey, S.; Cavallini, A.; Cavalcoli, D.; Skuridina, D.; Vogt, P.; Kneissl, M.; Behmenburg, H.; Giesen, C.; Heuken, M.; Gamarra, P.; Di Forte-Poisson, M. A.; Patriarche, G.; Vickridge, I.
2014-12-15
We report on our multi–pronged approach to understand the structural and electrical properties of an InAl(Ga)N(33nm barrier)/Al(Ga)N(1nm interlayer)/GaN(3μm)/ AlN(100nm)/Al{sub 2}O{sub 3} high electron mobility transistor (HEMT) heterostructure grown by metal organic vapor phase epitaxy (MOVPE). In particular we reveal and discuss the role of unintentional Ga incorporation in the barrier and also in the interlayer. The observation of unintentional Ga incorporation by using energy dispersive X–ray spectroscopy analysis in a scanning transmission electron microscope is supported with results obtained for samples with a range of AlN interlayer thicknesses grown under both the showerhead as well as the horizontal type MOVPE reactors. Poisson–Schrödinger simulations show that for high Ga incorporation in the Al(Ga)N interlayer, an additional triangular well with very small depth may be exhibited in parallel to the main 2–DEG channel. The presence of this additional channel may cause parasitic conduction and severe issues in device characteristics and processing. Producing a HEMT structure with InAlGaN as the barrier and AlGaN as the interlayer with appropriate alloy composition may be a possible route to optimization, as it might be difficult to avoid Ga incorporation while continuously depositing the layers using the MOVPE growth method. Our present work shows the necessity of a multicharacterization approach to correlate structural and electrical properties to understand device structures and their performance.
Welded joints integrity analysis and optimization for fiber laser welding of dissimilar materials
NASA Astrophysics Data System (ADS)
Ai, Yuewei; Shao, Xinyu; Jiang, Ping; Li, Peigen; Liu, Yang; Liu, Wei
2016-11-01
Dissimilar materials welded joints provide many advantages in power, automotive, chemical, and spacecraft industries. The weld bead integrity which is determined by process parameters plays a significant role in the welding quality during the fiber laser welding (FLW) of dissimilar materials. In this paper, an optimization method by taking the integrity of the weld bead and weld area into consideration is proposed for FLW of dissimilar materials, the low carbon steel and stainless steel. The relationships between the weld bead integrity and process parameters are developed by the genetic algorithm optimized back propagation neural network (GA-BPNN). The particle swarm optimization (PSO) algorithm is taken for optimizing the predicted outputs from GA-BPNN for the objective. Through the optimization process, the desired weld bead with good integrity and minimum weld area are obtained and the corresponding microstructure and microhardness are excellent. The mechanical properties of the optimized joints are greatly improved compared with that of the un-optimized welded joints. Moreover, the effects of significant factors are analyzed based on the statistical approach and the laser power (LP) is identified as the most significant factor on the weld bead integrity and weld area. The results indicate that the proposed method is effective for improving the reliability and stability of welded joints in the practical production.
Cordero, Amy M; Crider, Krista S; Rogers, Lisa M; Cannon, Michael J; Berry, R J
2015-04-24
Neural tube defects (NTDs) such as spina bifida, anencephaly, and encephalocele are serious birth defects of the brain and spine that occur during the first month of pregnancy when the neural tube fails to close completely. Randomized controlled trials and observational studies have shown that adequate daily consumption of folic acid before and during early pregnancy considerably reduces the risk for NTDs. The U.S. Public Health Service recommends that women capable of becoming pregnant consume 400 µg of folic acid daily for NTD prevention. Furthermore, fortification of staple foods (e.g., wheat flour) with folic acid has decreased folate-sensitive NTD prevalence in multiple settings and is a highly cost-effective intervention. PMID:25905896
A study on ionospheric TEC forecast using genetic algorithm and neural network
NASA Astrophysics Data System (ADS)
Huang, Zhi; Yuan, Hong
Back propagation artificial neural network (ANN) augmented by genetic algorithm (GA) is introduced to forecast ionospheric TEC with the dual-frequency GPS measurements from the low and high solar activity years in this paper due to ionosphere space characterizing by the highly nonlinear and time-varying with random variations. First, with different number of neurons in the hidden layer, different transfer function and training function, the training performance of network model is analyzed and then optimized network structure is determined. The ionospheric TEC values one hour in advance are forecasted and further the prediction performance of the developed network model is evaluated at the given criterions. The results show that predicted TEC using BP neural network improved by genetic algorithm has good agreement with observed data. In addition, the prediction errors are smaller in middle and high latitudes than in low latitudes, smaller in low solar activity than in high solar activity. Compared with BP Network with three layers structure, Prediction precision of network model optimized by genetic algorithm is further improved. The resolution quality indicate that the proposed algorithm can offer a powerful and reliable alternative to the design of ionospheric TEC forecast technologies, and provide advice for the regional ionospheric TEC maps. Key words: Neural network, Genetic algorithm, Ionospheric TEC, Forecast,
Pi, Erxu; Qu, Liqun; Tang, Xi; Peng, Tingting; Jiang, Bo; Guo, Jiangfeng; Lu, Hongfei; Du, Liqun
2015-01-01
Temperature is a predominant environmental factor affecting grass germination and distribution. Various thermal-germination models for prediction of grass seed germination have been reported, in which the relationship between temperature and germination were defined with kernel functions, such as quadratic or quintic function. However, their prediction accuracies warrant further improvements. The purpose of this study is to evaluate the relative prediction accuracies of genetic algorithm (GA) models, which are automatically parameterized with observed germination data. The seeds of five P. pratensis (Kentucky bluegrass, KB) cultivars were germinated under 36 day/night temperature regimes ranging from 5/5 to 40/40 °C with 5 °C increments. Results showed that optimal germination percentages of all five tested KB cultivars were observed under a fluctuating temperature regime of 20/25 °C. Meanwhile, the constant temperature regimes (e.g., 5/5, 10/10, 15/15 °C, etc.) suppressed the germination of all five cultivars. Furthermore, the back propagation artificial neural network (BP-ANN) algorithm was integrated to optimize temperature-germination response models from these observed germination data. It was found that integrations of GA-BP-ANN (back propagation aided genetic algorithm artificial neural network) significantly reduced the Root Mean Square Error (RMSE) values from 0.21~0.23 to 0.02~0.09. In an effort to provide a more reliable prediction of optimum sowing time for the tested KB cultivars in various regions in the country, the optimized GA-BP-ANN models were applied to map spatial and temporal germination percentages of blue grass cultivars in China. Our results demonstrate that the GA-BP-ANN model is a convenient and reliable option for constructing thermal-germination response models since it automates model parameterization and has excellent prediction accuracy. PMID:26154163
Peng, Tingting; Jiang, Bo; Guo, Jiangfeng; Lu, Hongfei; Du, Liqun
2015-01-01
Temperature is a predominant environmental factor affecting grass germination and distribution. Various thermal-germination models for prediction of grass seed germination have been reported, in which the relationship between temperature and germination were defined with kernel functions, such as quadratic or quintic function. However, their prediction accuracies warrant further improvements. The purpose of this study is to evaluate the relative prediction accuracies of genetic algorithm (GA) models, which are automatically parameterized with observed germination data. The seeds of five P. pratensis (Kentucky bluegrass, KB) cultivars were germinated under 36 day/night temperature regimes ranging from 5/5 to 40/40°C with 5°C increments. Results showed that optimal germination percentages of all five tested KB cultivars were observed under a fluctuating temperature regime of 20/25°C. Meanwhile, the constant temperature regimes (e.g., 5/5, 10/10, 15/15°C, etc.) suppressed the germination of all five cultivars. Furthermore, the back propagation artificial neural network (BP-ANN) algorithm was integrated to optimize temperature-germination response models from these observed germination data. It was found that integrations of GA-BP-ANN (back propagation aided genetic algorithm artificial neural network) significantly reduced the Root Mean Square Error (RMSE) values from 0.21~0.23 to 0.02~0.09. In an effort to provide a more reliable prediction of optimum sowing time for the tested KB cultivars in various regions in the country, the optimized GA-BP-ANN models were applied to map spatial and temporal germination percentages of blue grass cultivars in China. Our results demonstrate that the GA-BP-ANN model is a convenient and reliable option for constructing thermal-germination response models since it automates model parameterization and has excellent prediction accuracy. PMID:26154163
Zhang, Yu; Xu, Jing-Liang; Yuan, Zhen-Hong; Qi, Wei; Liu, Yun-Yun; He, Min-Chao
2012-01-01
Two artificial intelligence techniques, namely artificial neural network (ANN) and genetic algorithm (GA) were combined to be used as a tool for optimizing the covalent immobilization of cellulase on a smart polymer, Eudragit L-100. 1-Ethyl-3-(3-dimethyllaminopropyl) carbodiimide (EDC) concentration, N-hydroxysuccinimide (NHS) concentration and coupling time were taken as independent variables, and immobilization efficiency was taken as the response. The data of the central composite design were used to train ANN by back-propagation algorithm, and the result showed that the trained ANN fitted the data accurately (correlation coefficient R(2) = 0.99). Then a maximum immobilization efficiency of 88.76% was searched by genetic algorithm at a EDC concentration of 0.44%, NHS concentration of 0.37% and a coupling time of 2.22 h, where the experimental value was 87.97 ± 6.45%. The application of ANN based optimization by GA is quite successful.
Zhang, Yu; Xu, Jing-Liang; Yuan, Zhen-Hong; Qi, Wei; Liu, Yun-Yun; He, Min-Chao
2012-01-01
Two artificial intelligence techniques, namely artificial neural network (ANN) and genetic algorithm (GA) were combined to be used as a tool for optimizing the covalent immobilization of cellulase on a smart polymer, Eudragit L-100. 1-Ethyl-3-(3-dimethyllaminopropyl) carbodiimide (EDC) concentration, N-hydroxysuccinimide (NHS) concentration and coupling time were taken as independent variables, and immobilization efficiency was taken as the response. The data of the central composite design were used to train ANN by back-propagation algorithm, and the result showed that the trained ANN fitted the data accurately (correlation coefficient R2 = 0.99). Then a maximum immobilization efficiency of 88.76% was searched by genetic algorithm at a EDC concentration of 0.44%, NHS concentration of 0.37% and a coupling time of 2.22 h, where the experimental value was 87.97 ± 6.45%. The application of ANN based optimization by GA is quite successful. PMID:22942683
Criticality Maximizes Complexity in Neural Tissue
Timme, Nicholas M.; Marshall, Najja J.; Bennett, Nicholas; Ripp, Monica; Lautzenhiser, Edward; Beggs, John M.
2016-01-01
The analysis of neural systems leverages tools from many different fields. Drawing on techniques from the study of critical phenomena in statistical mechanics, several studies have reported signatures of criticality in neural systems, including power-law distributions, shape collapses, and optimized quantities under tuning. Independently, neural complexity—an information theoretic measure—has been introduced in an effort to quantify the strength of correlations across multiple scales in a neural system. This measure represents an important tool in complex systems research because it allows for the quantification of the complexity of a neural system. In this analysis, we studied the relationships between neural complexity and criticality in neural culture data. We analyzed neural avalanches in 435 recordings from dissociated hippocampal cultures produced from rats, as well as neural avalanches from a cortical branching model. We utilized recently developed maximum likelihood estimation power-law fitting methods that account for doubly truncated power-laws, an automated shape collapse algorithm, and neural complexity and branching ratio calculation methods that account for sub-sampling, all of which are implemented in the freely available Neural Complexity and Criticality MATLAB toolbox. We found evidence that neural systems operate at or near a critical point and that neural complexity is optimized in these neural systems at or near the critical point. Surprisingly, we found evidence that complexity in neural systems is dependent upon avalanche profiles and neuron firing rate, but not precise spiking relationships between neurons. In order to facilitate future research, we made all of the culture data utilized in this analysis freely available online. PMID:27729870
Chen, Jr-Tai Hsu, Chih-Wei; Forsberg, Urban; Janzén, Erik
2015-02-28
Severe surface decomposition of semi-insulating (SI) GaN templates occurred in high-temperature H{sub 2} atmosphere prior to epitaxial growth in a metalorganic chemical vapor deposition system. A two-step heating process with a surface stabilization technique was developed to preserve the GaN template surface. Utilizing the optimized heating process, a high two-dimensional electron gas mobility ∼2000 cm{sup 2}/V·s was obtained in a thin AlGaN/AlN/GaN heterostructure with an only 100-nm-thick GaN spacer layer homoepitaxially grown on the GaN template. This technique was also demonstrated viable for native GaN substrates to stabilize the surface facilitating two-dimensional growth of GaN layers. Very high residual silicon and oxygen concentrations were found up to ∼1 × 10{sup 20 }cm{sup −3} at the interface between the GaN epilayer and the native GaN substrate. Capacitance-voltage measurements confirmed that the residual carbon doping controlled by growth conditions of the GaN epilayer can be used to successfully compensate the donor-like impurities. State-of-the-art structural properties of a high-mobility AlGaN/AlN/GaN heterostructure was then realized on a 1 × 1 cm{sup 2} SI native GaN substrate; the full width at half maximum of the X-ray rocking curves of the GaN (002) and (102) peaks are only 21 and 14 arc sec, respectively. The surface morphology of the heterostructure shows uniform parallel bilayer steps, and no morphological defects were noticeable over the entire epi-wafer.
NASA Astrophysics Data System (ADS)
Chen-Tai, Jr.; Hsu, Chih-Wei; Forsberg, Urban; Janzén, Erik
2015-02-01
Severe surface decomposition of semi-insulating (SI) GaN templates occurred in high-temperature H2 atmosphere prior to epitaxial growth in a metalorganic chemical vapor deposition system. A two-step heating process with a surface stabilization technique was developed to preserve the GaN template surface. Utilizing the optimized heating process, a high two-dimensional electron gas mobility ˜2000 cm2/V.s was obtained in a thin AlGaN/AlN/GaN heterostructure with an only 100-nm-thick GaN spacer layer homoepitaxially grown on the GaN template. This technique was also demonstrated viable for native GaN substrates to stabilize the surface facilitating two-dimensional growth of GaN layers. Very high residual silicon and oxygen concentrations were found up to ˜1 × 1020 cm-3 at the interface between the GaN epilayer and the native GaN substrate. Capacitance-voltage measurements confirmed that the residual carbon doping controlled by growth conditions of the GaN epilayer can be used to successfully compensate the donor-like impurities. State-of-the-art structural properties of a high-mobility AlGaN/AlN/GaN heterostructure was then realized on a 1 × 1 cm2 SI native GaN substrate; the full width at half maximum of the X-ray rocking curves of the GaN (002) and (102) peaks are only 21 and 14 arc sec, respectively. The surface morphology of the heterostructure shows uniform parallel bilayer steps, and no morphological defects were noticeable over the entire epi-wafer.
Evolvable synthetic neural system
NASA Technical Reports Server (NTRS)
Curtis, Steven A. (Inventor)
2009-01-01
An evolvable synthetic neural system includes an evolvable neural interface operably coupled to at least one neural basis function. Each neural basis function includes an evolvable neural interface operably coupled to a heuristic neural system to perform high-level functions and an autonomic neural system to perform low-level functions. In some embodiments, the evolvable synthetic neural system is operably coupled to one or more evolvable synthetic neural systems in a hierarchy.
Smith, Patrick I.
2003-09-23
Physicists use large detectors to measure particles created in high-energy collisions at particle accelerators. These detectors typically produce signals indicating either where ionization occurs along the path of the particle, or where energy is deposited by the particle. The data produced by these signals is fed into pattern recognition programs to try to identify what particles were produced, and to measure the energy and direction of these particles. Ideally, there are many techniques used in this pattern recognition software. One technique, neural networks, is particularly suitable for identifying what type of particle caused by a set of energy deposits. Neural networks can derive meaning from complicated or imprecise data, extract patterns, and detect trends that are too complex to be noticed by either humans or other computer related processes. To assist in the advancement of this technology, Physicists use a tool kit to experiment with several neural network techniques. The goal of this research is interface a neural network tool kit into Java Analysis Studio (JAS3), an application that allows data to be analyzed from any experiment. As the final result, a physicist will have the ability to train, test, and implement a neural network with the desired output while using JAS3 to analyze the results or output. Before an implementation of a neural network can take place, a firm understanding of what a neural network is and how it works is beneficial. A neural network is an artificial representation of the human brain that tries to simulate the learning process [5]. It is also important to think of the word artificial in that definition as computer programs that use calculations during the learning process. In short, a neural network learns by representative examples. Perhaps the easiest way to describe the way neural networks learn is to explain how the human brain functions. The human brain contains billions of neural cells that are responsible for processing
Investigation of Photovoltaic Properties of Single Core-Shell GaN/InGaN Wires.
Messanvi, A; Zhang, H; Neplokh, V; Julien, F H; Bayle, F; Foldyna, M; Bougerol, C; Gautier, E; Babichev, A; Durand, C; Eymery, J; Tchernycheva, M
2015-10-01
We report the investigation of the photovoltaic properties of core-shell GaN/InGaN wires. The radial structure is grown on m-plane {11̅00} facets of self-assembled c̅-axis GaN wires elaborated by metal-organic vapor phase epitaxy (MOVPE) on sapphire substrates. The conversion efficiency of wires with radial shell composed of thick In0.1Ga0.9N layers and of 30× In0.18Ga0.82N/GaN quantum wells are compared. We also investigate the impact of the contact nature and layout on the carrier collection and photovoltaic performances. The contact optimization results in an improved conversion efficiency of 0.33% and a fill factor of 83% under 1 sun (AM1.5G) on single wires with a quantum well-based active region. Photocurrent spectroscopy demonstrates that the response ascribed to the absorption of InGaN/GaN quantum wells appears at wavelengths shorter than 440 nm.
Investigation of Photovoltaic Properties of Single Core-Shell GaN/InGaN Wires.
Messanvi, A; Zhang, H; Neplokh, V; Julien, F H; Bayle, F; Foldyna, M; Bougerol, C; Gautier, E; Babichev, A; Durand, C; Eymery, J; Tchernycheva, M
2015-10-01
We report the investigation of the photovoltaic properties of core-shell GaN/InGaN wires. The radial structure is grown on m-plane {11̅00} facets of self-assembled c̅-axis GaN wires elaborated by metal-organic vapor phase epitaxy (MOVPE) on sapphire substrates. The conversion efficiency of wires with radial shell composed of thick In0.1Ga0.9N layers and of 30× In0.18Ga0.82N/GaN quantum wells are compared. We also investigate the impact of the contact nature and layout on the carrier collection and photovoltaic performances. The contact optimization results in an improved conversion efficiency of 0.33% and a fill factor of 83% under 1 sun (AM1.5G) on single wires with a quantum well-based active region. Photocurrent spectroscopy demonstrates that the response ascribed to the absorption of InGaN/GaN quantum wells appears at wavelengths shorter than 440 nm. PMID:26378593
Jamshidi, S.; Yadollahi, A.; Ahmadi, H.; Arab, M. M.; Eftekhari, M.
2016-01-01
Two modeling techniques [artificial neural network-genetic algorithm (ANN-GA) and stepwise regression analysis] were used to predict the effect of medium macro-nutrients on in vitro performance of pear rootstocks (OHF and Pyrodwarf). The ANN-GA described associations between investigating eight macronutrients (NO3-, NH4+, Ca2+, K+, Mg2+, PO42-, SO42-, and Cl−) and explant growth parameters [proliferation rate (PR), shoot length (SL), shoot tip necrosis (STN), chlorosis (Chl), and vitrification (Vitri)]. ANN-GA revealed a substantially higher accuracy of prediction than for regression models. According to the ANN-GA results, among the input variables concentrations (mM), NH4+ (301.7), and NO3-, NH4+ (64), SO42- (54.1), K+ (40.4), and NO3- (35.1) in OHF and Ca2+ (23.7), NH4+ (10.7), NO3- (9.1), NH4+ (317.6), and NH4+ (79.6) in Pyrodwarf had the highest values of VSR in data set, respectively, for PR, SL, STN, Chl, and Vitri. The ANN-GA showed that media containing (mM) 62.5 NO3-, 5.7 NH4+, 2.7 Ca2+, 31.5 K+, 3.3 Mg2+, 2.6 PO42-, 5.6 SO42-, and 3.5 Cl− could lead to optimal PR for OHF and optimal PR for Pyrodwarf may be obtained with media containing 25.6 NO3-, 13.1 NH4+, 5.5 Ca2+, 35.7 K+, 1.5 Mg2+, 2.1 PO42-, 3.6 SO42-, and 3 Cl−. PMID:27066013
Huang, Wei; Oh, Sung-Kwun; Pedrycz, Witold
2014-12-01
In this study, we propose Hybrid Radial Basis Function Neural Networks (HRBFNNs) realized with the aid of fuzzy clustering method (Fuzzy C-Means, FCM) and polynomial neural networks. Fuzzy clustering used to form information granulation is employed to overcome a possible curse of dimensionality, while the polynomial neural network is utilized to build local models. Furthermore, genetic algorithm (GA) is exploited here to optimize the essential design parameters of the model (including fuzzification coefficient, the number of input polynomial fuzzy neurons (PFNs), and a collection of the specific subset of input PFNs) of the network. To reduce dimensionality of the input space, principal component analysis (PCA) is considered as a sound preprocessing vehicle. The performance of the HRBFNNs is quantified through a series of experiments, in which we use several modeling benchmarks of different levels of complexity (different number of input variables and the number of available data). A comparative analysis reveals that the proposed HRBFNNs exhibit higher accuracy in comparison to the accuracy produced by some models reported previously in the literature.
Prediction of Heart Attack Risk Using GA-ANFIS Expert System Prototype.
Begic Fazlic, Lejla; Avdagic, Aja; Besic, Ingmar
2015-01-01
The aim of this research is to develop a novel GA-ANFIS expert system prototype for classifying heart disease degree of a patient by using heart diseases attributes (features) and diagnoses taken in the real conditions. Thirteen attributes have been used as inputs to classifiers being based on Adaptive Neuro-Fuzzy Inference Systems (ANFIS) for the first level of fuzzy model optimization. They are used as inputs in Genetic Algorithm (GA) for the second level of fuzzy model optimization within GA-ANFIS system. GA-ANFIS system performs optimization in two steps. Modelling and validating of the novel GA-ANFIS system approach is performed in MATLAB environment. We compared GA-ANFIS and ANFIS results. The proposed GA-ANFIS model with the predicted value technique is more efficient when diagnosis of heart disease is concerned, as well the earlier method we got by ANFIS model. PMID:25980885
Prediction of Heart Attack Risk Using GA-ANFIS Expert System Prototype.
Begic Fazlic, Lejla; Avdagic, Aja; Besic, Ingmar
2015-01-01
The aim of this research is to develop a novel GA-ANFIS expert system prototype for classifying heart disease degree of a patient by using heart diseases attributes (features) and diagnoses taken in the real conditions. Thirteen attributes have been used as inputs to classifiers being based on Adaptive Neuro-Fuzzy Inference Systems (ANFIS) for the first level of fuzzy model optimization. They are used as inputs in Genetic Algorithm (GA) for the second level of fuzzy model optimization within GA-ANFIS system. GA-ANFIS system performs optimization in two steps. Modelling and validating of the novel GA-ANFIS system approach is performed in MATLAB environment. We compared GA-ANFIS and ANFIS results. The proposed GA-ANFIS model with the predicted value technique is more efficient when diagnosis of heart disease is concerned, as well the earlier method we got by ANFIS model.
Science of artificial neural networks; Proceedings of the Meeting, Orlando, FL, Apr. 21-24, 1992
Ruck, D.W.
1992-01-01
The present conference discusses high-order neural networks with adaptive architecture, a parallel cascaded one-step learning machine, stretch and hammer neural networks, visual grammars for neural networks, the net pruning of a multilayer perceptron, neural correlates of the sensorial and cognitive control of behavior, neural nets for massively parallel optimization, parametric and additive perturbations for global optimization, design rules for multilayer perceptrons, the negative transfer problem in neural networks, and a vision-based neural multimap pattern recognition architecture. Also discussed are function prediction with recurrent neural networks, fuzzy neural computing systems, edge detection via fuzzy neural networks, modeling confusion for autonomous systems, self-organization by fuzzy clustering, neural nets in information retrieval, neighborhoods and trajectories in Kohonen maps, the random structure of error surfaces, and conceptual recognition by neural networks.
Waveguide structural effect on ripples of far-field pattern in 405-nm GaN-based laser diodes
NASA Astrophysics Data System (ADS)
Hwang, Sungmin; Shim, Jongin; Ryu, Hanyoul; Ha, Kyung-ho; Chae, Junghye; Nam, Okhyun
2006-09-01
We investigated the dependency of waveguide structures on ripples of far-field patterns in 405nm GaN-based laser diodes theoretically and experimentally. As the n-type cladding layer thickness decreases, the passive waveguide modes strongly interact with an active layer mode. This suggests that the thicknesses of n-AlGaN/GaN superlattice clad and n-GaN waveguide layers have significant influences on FFP ripples. We successfully obtained very smooth far-field patterns perpendicular to the junction plane by optimizing both n-AlGaN/GaN clad layer thickness and n-GaN waveguide layer thickness.
GaN hexagonal pyramids formed by a photo-assisted chemical etching method
NASA Astrophysics Data System (ADS)
Zhang, Shi-Ying; Xiu, Xiang-Qian; Hua, Xue-Mei; Xie, Zi-Li; Liu, Bin; Chen, Peng; Han, Ping; Lu, Hai; Zhang, Rong; Zheng, You-Dou
2014-05-01
A series of experiments were conducted to systematically study the effects of etching conditions on GaN by a convenient photo-assisted chemical (PAC) etching method. The solution concentration has an evident influence on the surface morphology of GaN and the optimal solution concentrations for GaN hexagonal pyramids have been identified. GaN with hexagonal pyramids have higher crystal quality and tensile strain relaxation compared with as-grown GaN. A detailed analysis about evolution of the size, density and optical property of GaN hexagonal pyramids is described as a function of light intensity. The intensity of photoluminescence spectra of GaN etched with hexagonal pyramids significantly increases compared to that of as-grown GaN due to multiple scattering events, high quality GaN with pyramids and the Bragg effect.
Information complexity of neural networks.
Kon, M A; Plaskota, L
2000-04-01
This paper studies the question of lower bounds on the number of neurons and examples necessary to program a given task into feed forward neural networks. We introduce the notion of information complexity of a network to complement that of neural complexity. Neural complexity deals with lower bounds for neural resources (numbers of neurons) needed by a network to perform a given task within a given tolerance. Information complexity measures lower bounds for the information (i.e. number of examples) needed about the desired input-output function. We study the interaction of the two complexities, and so lower bounds for the complexity of building and then programming feed-forward nets for given tasks. We show something unexpected a priori--the interaction of the two can be simply bounded, so that they can be studied essentially independently. We construct radial basis function (RBF) algorithms of order n3 that are information-optimal, and give example applications.
Hybrid intelligent optimization methods for engineering problems
NASA Astrophysics Data System (ADS)
Pehlivanoglu, Yasin Volkan
quantification studies, we improved new mutation strategies and operators to provide beneficial diversity within the population. We called this new approach as multi-frequency vibrational GA or PSO. They were applied to different aeronautical engineering problems in order to study the efficiency of these new approaches. These implementations were: applications to selected benchmark test functions, inverse design of two-dimensional (2D) airfoil in subsonic flow, optimization of 2D airfoil in transonic flow, path planning problems of autonomous unmanned aerial vehicle (UAV) over a 3D terrain environment, 3D radar cross section minimization problem for a 3D air vehicle, and active flow control over a 2D airfoil. As demonstrated by these test cases, we observed that new algorithms outperform the current popular algorithms. The principal role of this multi-frequency approach was to determine which individuals or particles should be mutated, when they should be mutated, and which ones should be merged into the population. The new mutation operators, when combined with a mutation strategy and an artificial intelligent method, such as, neural networks or fuzzy logic process, they provided local and global diversities during the reproduction phases of the generations. Additionally, the new approach also introduced random and controlled diversity. Due to still being population-based techniques, these methods were as robust as the plain GA or PSO algorithms. Based on the results obtained, it was concluded that the variants of the present multi-frequency vibrational GA and PSO were efficient algorithms, since they successfully avoided all local optima within relatively short optimization cycles.
Short-wavelength light beam in situ monitoring growth of InGaN/GaN green LEDs by MOCVD.
Sun, Xiaojuan; Li, Dabing; Song, Hang; Chen, Yiren; Jiang, Hong; Miao, Guoqing; Li, Zhiming
2012-05-31
In this paper, five-period InGaN/GaN multiple quantum well green light-emitting diodes (LEDs) were grown by metal organic chemical vapor deposition with 405-nm light beam in situ monitoring system. Based on the signal of 405-nm in situ monitoring system, the related information of growth rate, indium composition and interfacial quality of each InGaN/GaN QW were obtained, and thus, the growth conditions and structural parameters were optimized to grow high-quality InGaN/GaN green LED structure. Finally, a green LED with a wavelength of 509 nm was fabricated under the optimal parameters, which was also proved by ex situ characterization such as high-resolution X-ray diffraction, photoluminescence, and electroluminescence. The results demonstrated that short-wavelength in situ monitoring system was a quick and non-destroyed tool to provide the growth information on InGaN/GaN, which would accelerate the research and development of GaN-based green LEDs.
Realization of defect-free epitaxial core-shell GaAs/AlGaAs nanowire heterostructures
Tambe, Michael J.; Lim, Sung Keun; Smith, Matthew J.; Gradecak, Silvija; Allard, Lawrence F.
2008-10-13
We report the controlled growth of vertically aligned GaAs/AlGaAs core-shell nanowires. By optimizing the shell deposition temperature and catalyst density we maintain high temperature stability and achieve defect-free epitaxial AlGaAs shell deposition with high aluminum incorporation. Energy dispersive x-ray analysis determines the shell composition to be Al{sub 0.9}Ga{sub 0.1}As and measures the uniformity of the shell thickness. Lattice-resolved high-angle annular dark-field scanning transmission electron microscopy images confirm the core-shell interface to be defect-free, epitaxial, and atomically sharp. The ability to realize GaAs/AlGaAs core-shell nanowires with precise control over the morphology and composition is essential to the development of nanowire-based high mobility electronics.
Rekha, V. P. B.; Ghosh, Mrinmoy; Adapa, Vijayanand; Oh, Sung-Jong; Pulicherla, K. K.; Sambasiva Rao, K. R. S.
2013-01-01
The present study deals with the production of cold active polygalacturonase (PGase) by submerged fermentation using Thalassospira frigidphilosprofundus, a novel species isolated from deep waters of Bay of Bengal. Nonlinear models were applied to optimize the medium components for enhanced production of PGase. Taguchi orthogonal array design was adopted to evaluate the factors influencing the yield of PGase, followed by the central composite design (CCD) of response surface methodology (RSM) to identify the optimum concentrations of the key factors responsible for PGase production. Data obtained from the above mentioned statistical experimental design was used for final optimization study by linking the artificial neural network and genetic algorithm (ANN-GA). Using ANN-GA hybrid model, the maximum PGase activity (32.54 U/mL) was achieved at the optimized concentrations of medium components. In a comparison between the optimal output of RSM and ANN-GA hybrid, the latter favored the production of PGase. In addition, the study also focused on the determination of factors responsible for pectin hydrolysis by crude pectinase extracted from T. frigidphilosprofundus through the central composite design. Results indicated 80% degradation of pectin in banana fiber at 20°C in 120 min, suggesting the scope of cold active PGase usage in the treatment of raw banana fibers. PMID:24455722
Chen, Kejian; Li, Yu-tai; Yang, Mong-huan; Cheung, Wing Yiu; Pan, Ci-Ling; Chan, Kam Tai
2009-04-01
Terahertz wave (THz) photoconductive (PC) antennas were fabricated on oxygen-implanted GaAs (GaAs:O) and low-temperature-grown GaAs (LT-GaAs). The measured cw THz power at 0.358 THz from the GaAs:O antenna is about twice that from the LT-GaAs antenna under the same testing conditions, with the former showing no saturation up to a bias of 40 kV/cm, while the latter is already beginning to saturate at 20 kV/cm. A modified theoretical model incorporating bias-field-dependent electron saturation velocity is employed to explain the results. It shows that GaAs:O exhibits a higher electron saturation velocity, which may be further exploited to generate even larger THz powers by reducing the ion dosage and optimizing the annealing process in GaAs:O. PMID:19340176
Ghaedi, M; Zeinali, N; Ghaedi, A M; Teimuori, M; Tashkhourian, J
2014-05-01
In this study, graphite oxide (GO) nano according to Hummers method was synthesized and subsequently was used for the removal of methylene blue (MB) and brilliant green (BG). The detail information about the structure and physicochemical properties of GO are investigated by different techniques such as XRD and FTIR analysis. The influence of solution pH, initial dye concentration, contact time and adsorbent dosage was examined in batch mode and optimum conditions was set as pH=7.0, 2 mg of GO and 10 min contact time. Employment of equilibrium isotherm models for description of adsorption capacities of GO explore the good efficiency of Langmuir model for the best presentation of experimental data with maximum adsorption capacity of 476.19 and 416.67 for MB and BG dyes in single solution. The analysis of adsorption rate at various stirring times shows that both dyes adsorption followed a pseudo second-order kinetic model with cooperation with interparticle diffusion model. Subsequently, the adsorption data as new combination of artificial neural network was modeled to evaluate and obtain the real conditions for fast and efficient removal of dyes. A three-layer artificial neural network (ANN) model is applicable for accurate prediction of dyes removal percentage from aqueous solution by GO following conduction of 336 experimental data. The network was trained using the obtained experimental data at optimum pH with different GO amount (0.002-0.008 g) and 5-40 mg/L of both dyes over contact time of 0.5-30 min. The ANN model was able to predict the removal efficiency with Levenberg-Marquardt algorithm (LMA), a linear transfer function (purelin) at output layer and a tangent sigmoid transfer function (tansig) at hidden layer with 10 and 11 neurons for MB and BG dyes, respectively. The minimum mean squared error (MSE) of 0.0012 and coefficient of determination (R(2)) of 0.982 were found for prediction and modeling of MB removal, while the respective value for BG was the
Coherence resonance in bursting neural networks
NASA Astrophysics Data System (ADS)
Kim, June Hoan; Lee, Ho Jun; Min, Cheol Hong; Lee, Kyoung J.
2015-10-01
Synchronized neural bursts are one of the most noticeable dynamic features of neural networks, being essential for various phenomena in neuroscience, yet their complex dynamics are not well understood. With extrinsic electrical and optical manipulations on cultured neural networks, we demonstrate that the regularity (or randomness) of burst sequences is in many cases determined by a (few) low-dimensional attractor(s) working under strong neural noise. Moreover, there is an optimal level of noise strength at which the regularity of the interburst interval sequence becomes maximal—a phenomenon of coherence resonance. The experimental observations are successfully reproduced through computer simulations on a well-established neural network model, suggesting that the same phenomena may occur in many in vivo as well as in vitro neural networks.
Coherence resonance in bursting neural networks.
Kim, June Hoan; Lee, Ho Jun; Min, Cheol Hong; Lee, Kyoung J
2015-10-01
Synchronized neural bursts are one of the most noticeable dynamic features of neural networks, being essential for various phenomena in neuroscience, yet their complex dynamics are not well understood. With extrinsic electrical and optical manipulations on cultured neural networks, we demonstrate that the regularity (or randomness) of burst sequences is in many cases determined by a (few) low-dimensional attractor(s) working under strong neural noise. Moreover, there is an optimal level of noise strength at which the regularity of the interburst interval sequence becomes maximal-a phenomenon of coherence resonance. The experimental observations are successfully reproduced through computer simulations on a well-established neural network model, suggesting that the same phenomena may occur in many in vivo as well as in vitro neural networks.
NASA Technical Reports Server (NTRS)
Mcneely, J. B.; Negley, G. H.; Barnett, A. M.
1985-01-01
GaAsP on GaP top solar cells as an attachment to silicon bottom solar cells are being developed. The GaAsP on GaP system offers several advantages for this top solar cell. The most important is that the gallium phosphide substrate provides a rugged, transparent mechanical substrate which does not have to be removed or thinned during processing. Additional advantages are that: (1) gallium phosphide is more oxidation resistant than the III-V aluminum compounds, (2) a range of energy band gaps higher than 1.75 eV is readily available for system efficiency optimization, (3) reliable ohmic contact technology is available from the light-emitting diode industry, and (4) the system readily lends itself to graded band gap structures for additional increases in efficiency.
NASA Astrophysics Data System (ADS)
Lu, W., Sr.; Xin, X.; Luo, J.; Jiang, X.; Zhang, Y.; Zhao, Y.; Chen, M.; Hou, Z.; Ouyang, Q.
2015-12-01
The purpose of this study was to identify an optimal surfactant-enhanced aquifer remediation (SEAR) strategy for aquifers contaminated by dense non-aqueous phase liquid (DNAPL) based on an ensemble of surrogates-based optimization technique. A saturated heterogeneous medium contaminated by nitrobenzene was selected as case study. A new kind of surrogate-based SEAR optimization employing an ensemble surrogate (ES) model together with a genetic algorithm (GA) is presented. Four methods, namely radial basis function artificial neural network (RBFANN), kriging (KRG), support vector regression (SVR), and kernel extreme learning machines (KELM), were used to create four individual surrogate models, which were then compared. The comparison enabled us to select the two most accurate models (KELM and KRG) to establish an ES model of the SEAR simulation model, and the developed ES model as well as these four stand-alone surrogate models was compared. The results showed that the average relative error of the average nitrobenzene removal rates between the ES model and the simulation model for 20 test samples was 0.8%, which is a high approximation accuracy, and which indicates that the ES model provides more accurate predictions than the stand-alone surrogate models. Then, a nonlinear optimization model was formulated for the minimum cost, and the developed ES model was embedded into this optimization model as a constrained condition. Besides, GA was used to solve the optimization model to provide the optimal SEAR strategy. The developed ensemble surrogate-optimization approach was effective in seeking a cost-effective SEAR strategy for heterogeneous DNAPL-contaminated sites. This research is expected to enrich and develop the theoretical and technical implications for the analysis of remediation strategy optimization of DNAPL-contaminated aquifers.
NASA Astrophysics Data System (ADS)
Jiang, Xue; Lu, Wenxi; Hou, Zeyu; Zhao, Haiqing; Na, Jin
2015-11-01
The purpose of this study was to identify an optimal surfactant-enhanced aquifer remediation (SEAR) strategy for aquifers contaminated by dense non-aqueous phase liquid (DNAPL) based on an ensemble of surrogates-based optimization technique. A saturated heterogeneous medium contaminated by nitrobenzene was selected as case study. A new kind of surrogate-based SEAR optimization employing an ensemble surrogate (ES) model together with a genetic algorithm (GA) is presented. Four methods, namely radial basis function artificial neural network (RBFANN), kriging (KRG), support vector regression (SVR), and kernel extreme learning machines (KELM), were used to create four individual surrogate models, which were then compared. The comparison enabled us to select the two most accurate models (KELM and KRG) to establish an ES model of the SEAR simulation model, and the developed ES model as well as these four stand-alone surrogate models was compared. The results showed that the average relative error of the average nitrobenzene removal rates between the ES model and the simulation model for 20 test samples was 0.8%, which is a high approximation accuracy, and which indicates that the ES model provides more accurate predictions than the stand-alone surrogate models. Then, a nonlinear optimization model was formulated for the minimum cost, and the developed ES model was embedded into this optimization model as a constrained condition. Besides, GA was used to solve the optimization model to provide the optimal SEAR strategy. The developed ensemble surrogate-optimization approach was effective in seeking a cost-effective SEAR strategy for heterogeneous DNAPL-contaminated sites. This research is expected to enrich and develop the theoretical and technical implications for the analysis of remediation strategy optimization of DNAPL-contaminated aquifers.
Tan, Joo Shun; Mohamad, Rosfarizan; Mokhtar, Mohd Noriznan; Ariff, Arbakariya B.
2013-01-01
Mixotrophic metabolism was evaluated as an option to augment the growth and lipid production of marine microalga Tetraselmis sp. FTC 209. In this study, a five-level three-factor central composite design (CCD) was implemented in order to enrich the W-30 algal growth medium. Response surface methodology (RSM) was employed to model the effect of three medium variables, that is, glucose (organic C source), NaNO3 (primary N source), and yeast extract (supplementary N, amino acids, and vitamins) on biomass concentration, Xmax, and lipid yield, Pmax/Xmax. RSM capability was also weighed against an artificial neural network (ANN) approach for predicting a composition that would result in maximum lipid productivity, Prlipid. A quadratic regression from RSM and a Levenberg-Marquardt trained ANN network composed of 10 hidden neurons eventually produced comparable results, albeit ANN formulation was observed to yield higher values of response outputs. Finalized glucose (24.05 g/L), NaNO3 (4.70 g/L), and yeast extract (0.93 g/L) concentration, affected an increase of Xmax to 12.38 g/L and lipid a accumulation of 195.77 mg/g dcw. This contributed to a lipid productivity of 173.11 mg/L per day in the course of two-week cultivation. PMID:24109209
Harbi, Soumaya; Guesmi, Fatma; Tabassi, Dorra; Hannachi, Chiraz; Hamrouni, Bechir
2016-01-01
We report the adsorption efficiency of Cr(VI) on a strong anionic resin Dowex 1X8. The Fourier transform infrared spectroscopy (FTIR) and X-ray diffraction (XRD) analysis of this adsorbent were investigated. Response surface methodology was applied to evaluate the main effects and interactions among initial pH, initial Cr(VI) concentration, adsorbent dose and temperature. Analysis of variance depicted that resin dose and initial pH were the most significant factors. Desirability function (DF) showed that the maximum Cr(VI) removal of 95.96% was obtained at initial pH 5, initial Cr(VI) concentration of 100 mg/L, resin dose of 2 g and temperature of 283 K. Additionally, a simulated industrial wastewater containing 14.95 mg/L of Cr(VI) was treated successfully by Dowex 1X8 at optimum conditions. Same experimental design was employed to develop the artificial neural network. Both models gave a high correlation coefficient (RRSM(2) = 0.932, RANN(2) = 0.996).
Tolkunov, Denis; Rubin, Denis; Mujica-Parodi, LR
2010-01-01
In a well-regulated control system, excitatory and inhibitory components work closely together with minimum lag; in response to inputs of finite duration, outputs should show rapid rise and, following the input's termination, immediate return to baseline. The efficiency of this response can be quantified using the power spectrum density's scaling parameter β, a measure of self-similarity, applied to the first-derivative of the raw signal. In this study, we adapted power spectrum density methods, previously used to quantify autonomic dysregulation (heart rate variability), to neural time-series obtained via functional MRI. The negative feedback loop we investigated was the limbic system, using affect-valent faces as stimuli. We hypothesized that trait anxiety would be related to efficiency of regulation of limbic responses, as quantified by power law scaling of fMRI time series. Our results supported this hypothesis, showing moderate to strong correlations of β (r = 0.4–0.54) for the amygdala, orbitofrontal cortex, hippocampus, superior temporal gyrus, posterior insula, and anterior cingulate. Strong anticorrelations were also found between the amygdala's β and wake heart rate variability (r = −0.61), suggesting a robust relationship between dysregulated limbic outputs and their autonomic consequences. PMID:20025979
Harbi, Soumaya; Guesmi, Fatma; Tabassi, Dorra; Hannachi, Chiraz; Hamrouni, Bechir
2016-01-01
We report the adsorption efficiency of Cr(VI) on a strong anionic resin Dowex 1X8. The Fourier transform infrared spectroscopy (FTIR) and X-ray diffraction (XRD) analysis of this adsorbent were investigated. Response surface methodology was applied to evaluate the main effects and interactions among initial pH, initial Cr(VI) concentration, adsorbent dose and temperature. Analysis of variance depicted that resin dose and initial pH were the most significant factors. Desirability function (DF) showed that the maximum Cr(VI) removal of 95.96% was obtained at initial pH 5, initial Cr(VI) concentration of 100 mg/L, resin dose of 2 g and temperature of 283 K. Additionally, a simulated industrial wastewater containing 14.95 mg/L of Cr(VI) was treated successfully by Dowex 1X8 at optimum conditions. Same experimental design was employed to develop the artificial neural network. Both models gave a high correlation coefficient (RRSM(2) = 0.932, RANN(2) = 0.996). PMID:27191561
[Resolution of overlapping chromatographic peaks by radial basis function neural network].
Li, Y B; Huang, X Y; Sha, M; Meng, X S
2001-03-01
A new algorithm-resolution of overlapping chromatographic peaks by radial basis function neural network(RBFNN) is presented. A two-phase genetic algorithm(GA) which has robustness and random globe optimization is used to train RBFNN so that it has the ability on the resolution of overlapping chromatographic peaks. The two-phase genetic algorithm involves two procedures: training structure and optimizing parameter. The first procedure uses GA to train the architectures of RBFNN, the second procedure uses gradient descent to train the center(tR) and the width(sigma) of RBFNN. The alternate use of these two procedures makes the network having the ability to learn structure, therefore makes itself adaptable to resolution of the chromatographic peaks with unknown number of components. The method proposed here needs no artificial interference, not only has it robustness and globalism, but also the ability of accurate resolution to completely overlapped chromatographic peaks. The simulation experiments show that this method is more accurate than other methods. PMID:12541651
Solving quadratic programming problems by delayed projection neural network.
Yang, Yongqing; Cao, Jinde
2006-11-01
In this letter, the delayed projection neural network for solving convex quadratic programming problems is proposed. The neural network is proved to be globally exponentially stable and can converge to an optimal solution of the optimization problem. Three examples show the effectiveness of the proposed network.
NASA Astrophysics Data System (ADS)
He, Bin
About the Series: Bioelectric Engineering presents state-of-the-art discussions on modern biomedical engineering with respect to applications of electrical engineering and information technology in biomedicine. This focus affirms Springer's commitment to publishing important reviews of the broadest interest to biomedical engineers, bioengineers, and their colleagues in affiliated disciplines. Recent volumes have covered modeling and imaging of bioelectric activity, neural engineering, biosignal processing, bionanotechnology, among other topics.
NASA Astrophysics Data System (ADS)
Ni, Yiqiang; He, Zhiyuan; Zhou, Deqiu; Yao, Yao; Yang, Fan; Zhou, Guilin; Shen, Zhen; Zhong, Jian; Zhen, Yue; Zhang, Baijun; Liu, Yang
2015-07-01
The influence of AlN/GaN superlattices (SL) buffer on the characteristics of AlGaN/GaN-on-Si (1 1 1) template was studied in detail. There existed an optimized Relative AlN Thickness (RAT) in the superlattices buffer which can not only further filtering the edge- and screw-type dislocations to the upper epilayer and lead to a good crystal quality with narrowest (0 0 0 2) and (1 0 -1 2) full width of half maximum (FWHMs), 439″ and 843″, but also improve the surface roughness to enhance the Two dimensional electron gas (2DEG) mobility and superior electrical properties were achieved. Moreover, an optimized RAT in SL can induce a proper compressive stress to the subsequently grown GaN epilayer and protect it from crack during the cooling step, which can also lead to a better wafer bending.
Averkiev, N. S.; Slipchenko, S. O. Sokolova, Z. N.; Pikhtin, N. A.; Tarasov, I. S.
2007-03-15
Generation of a difference-frequency wave by two electromagnetic waves propagating in a heterolaser is analyzed theoretically. Calculations are carried out for InGaAs/GaAs/AlGaAs heterostructures of design optimized to attain maximum lasing power. It is shown that phase matching between the primary waves and the difference-frequency wave may persist over a distance of {approx}1 mm, comparable to the cavity length (2-3 mm), and the conversion coefficient can be as large as several percent.
Interfacial, electrical, and spin-injection properties of epitaxial Co2MnGa grown on GaAs(100)
NASA Astrophysics Data System (ADS)
Damsgaard, C. D.; Hickey, M. C.; Holmes, S. N.; Feidenhans'l, R.; Mariager, S. O.; Jacobsen, C. S.; Hansen, J. B.
2009-06-01
The interfacial, electrical, and magnetic properties of the Heusler alloy Co2MnGa grown epitaxially on GaAs(100) are presented with an emphasis on the use of this metal-semiconductor combination for a device that operates on the principles of spin-injection between the two materials. Through systematic growth optimization the stoichiometry in the bulk Co2MnGa can be controlled to better than ±2%, although the interface is disordered and limits the spin-injection efficiency in a practical spintronic device irrespective of the half-metallic nature of the bulk metal. Molecular beam epitaxial growth was monitored in situ by reflection high energy electron diffraction and the bulk composition was measured ex situ with inductively coupled plasma optical emission spectroscopy. The Co2MnGa L21 cubic structure is strained below a thickness of 20 nm on GaAs(100) but relaxed in films thicker than 20 nm. Electrical measurements on the Co2MnGa reveal general characteristics of a disordered electron system with insulating behavior for layer thicknesses <4 nm. Thicker layers show a negative magnetoresistance with extraordinary Hall effect constants up to 30 Ω T-1. Spin polarization transfer across the interface between Co2MnGa and GaAs is approximately 6.4% at 5 K in the current of a GaAs p-i-n diode even with compositional disorder at the interface.
Aerodynamic Design Using Neural Networks
NASA Technical Reports Server (NTRS)
Rai, Man Mohan; Madavan, Nateri K.
2003-01-01
The design of aerodynamic components of aircraft, such as wings or engines, involves a process of obtaining the most optimal component shape that can deliver the desired level of component performance, subject to various constraints, e.g., total weight or cost, that the component must satisfy. Aerodynamic design can thus be formulated as an optimization problem that involves the minimization of an objective function subject to constraints. A new aerodynamic design optimization procedure based on neural networks and response surface methodology (RSM) incorporates the advantages of both traditional RSM and neural networks. The procedure uses a strategy, denoted parameter-based partitioning of the design space, to construct a sequence of response surfaces based on both neural networks and polynomial fits to traverse the design space in search of the optimal solution. Some desirable characteristics of the new design optimization procedure include the ability to handle a variety of design objectives, easily impose constraints, and incorporate design guidelines and rules of thumb. It provides an infrastructure for variable fidelity analysis and reduces the cost of computation by using less-expensive, lower fidelity simulations in the early stages of the design evolution. The initial or starting design can be far from optimal. The procedure is easy and economical to use in large-dimensional design space and can be used to perform design tradeoff studies rapidly. Designs involving multiple disciplines can also be optimized. Some practical applications of the design procedure that have demonstrated some of its capabilities include the inverse design of an optimal turbine airfoil starting from a generic shape and the redesign of transonic turbines to improve their unsteady aerodynamic characteristics.
Ali, Masood; Hsieh, William; Tsopelas, Chris
2015-07-01
The objective of this study was to identify a more rapid assay for (68)Ga(OH)3 impurity in (68)Ga-DOTATATE formulations. Three methods were used to prepare (68)Ga(OH)3 reference material (pharmacopoeial, bench titration and automated radiosynthesis), and four quality control methods for its assessment (thin layer chromatography, membrane filtration, HPLC and solid phase extraction). The optimal method of preparing (68)Ga(OH)3 was by titrating (68)Ga(3+) with buffered sodium hydroxide solutions to pH 5.6 ± 0.2. The precipitate was quantitatively isolated by membrane filtration (0.02 µm)/hydrochloric acid (HCl; pH 5.6) solvent, and also it remained 100% at the origin on instant thin layer chromatography with silica gel paper/HCl (pH 5.6) solvent. For (68)Ga-DOTATATE samples, the thin layer chromatography technique was used with a single paper strip developed separately on two occasions, once in HCl (pH 5.6) and next in methanol solvent. This so-called double-developed (DD) method separated (68)Ga(OH)3 impurity located at the origin, from (68)Ga-DOTATATE plus (68)Ga(3+) at ~Rf 0.4, and it was superior to the other methods. It assayed for the impurity similarly to the pharmacopoeial method. The advantages of the DD method were that it required inexpensive test materials and it reproducibly determined % (68)Ga(OH)3 in (68)Ga-DOTATATE in 12 min, 13 min earlier than the pharmacopoeial method. This time efficiency resulted in a surplus of 12% (68)Ga-DOTATATE counts in the product vial, and this provided a contingency of radioactivity or time for the injection/imaging processes in the Nuclear Medicine Department. PMID:26201091
Self-consistent vertical transport calculations in AlxGa1-xN/GaN based resonant tunneling diode
NASA Astrophysics Data System (ADS)
Rached, A.; Bhouri, A.; Sakr, S.; Lazzari, J.-L.; Belmabrouk, H.
2016-03-01
The formation of two-dimensional electron gases (2DEGs) at AlxGa1-xN/GaN hexagonal double-barriers (DB) resonant tunneling diodes (RTD) is investigated by numerical self-consistent (SC) solutions of the coupled Schrödinger and Poisson equations. Spontaneous and piezoelectric effects across the material interfaces are rigorously taken into account. Conduction band profiles, band edges and corresponding envelope functions are calculated in the AlxGa1-xN/GaN structures and likened to those where no polarization effects are included. The combined effect of the polarization-induced bound charge and conduction band offsets between the hexagonal AlGaN and GaN results in the formation of 2DEGs on one side of the DB and a depletion region on the other side. Using the transfer matrix formalism, the vertical transport (J-V characteristics) in AlGaN/GaN RTDs is calculated with a fully SC calculation in the ballistic regime. Compared to standard calculations where the voltage drop along the structure is supposed to be linear, the SC method leads to strong quantitative changes in the J-V characteristics showing that the applied electric field varies significantly in the active region of the structure. The influences of the aluminum composition and the GaN(AlGaN) thickness layers on the evolution of the current characteristics are also self-consistently investigated and discussed. We show that the electrical characteristics are very sensitive to the potential barrier due to the interplay between the potential symmetry and the barrier height and width. More interestingly, we demonstrate that the figures of merit namely the peak-to-valley ratio (PVR) of GaN/AlGaN RTDs can be optimized by increasing the quantum well width.
A hybrid neural networks-fuzzy logic-genetic algorithm for grade estimation
NASA Astrophysics Data System (ADS)
Tahmasebi, Pejman; Hezarkhani, Ardeshir
2012-05-01
The grade estimation is a quite important and money/time-consuming stage in a mine project, which is considered as a challenge for the geologists and mining engineers due to the structural complexities in mineral ore deposits. To overcome this problem, several artificial intelligence techniques such as Artificial Neural Networks (ANN) and Fuzzy Logic (FL) have recently been employed with various architectures and properties. However, due to the constraints of both methods, they yield the desired results only under the specific circumstances. As an example, one major problem in FL is the difficulty of constructing the membership functions (MFs).Other problems such as architecture and local minima could also be located in ANN designing. Therefore, a new methodology is presented in this paper for grade estimation. This method which is based on ANN and FL is called "Coactive Neuro-Fuzzy Inference System" (CANFIS) which combines two approaches, ANN and FL. The combination of these two artificial intelligence approaches is achieved via the verbal and numerical power of intelligent systems. To improve the performance of this system, a Genetic Algorithm (GA) - as a well-known technique to solve the complex optimization problems - is also employed to optimize the network parameters including learning rate, momentum of the network and the number of MFs for each input. A comparison of these techniques (ANN, Adaptive Neuro-Fuzzy Inference System or ANFIS) with this new method (CANFIS-GA) is also carried out through a case study in Sungun copper deposit, located in East-Azerbaijan, Iran. The results show that CANFIS-GA could be a faster and more accurate alternative to the existing time-consuming methodologies for ore grade estimation and that is, therefore, suggested to be applied for grade estimation in similar problems.
A hybrid neural networks-fuzzy logic-genetic algorithm for grade estimation
Tahmasebi, Pejman; Hezarkhani, Ardeshir
2012-01-01
The grade estimation is a quite important and money/time-consuming stage in a mine project, which is considered as a challenge for the geologists and mining engineers due to the structural complexities in mineral ore deposits. To overcome this problem, several artificial intelligence techniques such as Artificial Neural Networks (ANN) and Fuzzy Logic (FL) have recently been employed with various architectures and properties. However, due to the constraints of both methods, they yield the desired results only under the specific circumstances. As an example, one major problem in FL is the difficulty of constructing the membership functions (MFs).Other problems such as architecture and local minima could also be located in ANN designing. Therefore, a new methodology is presented in this paper for grade estimation. This method which is based on ANN and FL is called “Coactive Neuro-Fuzzy Inference System” (CANFIS) which combines two approaches, ANN and FL. The combination of these two artificial intelligence approaches is achieved via the verbal and numerical power of intelligent systems. To improve the performance of this system, a Genetic Algorithm (GA) – as a well-known technique to solve the complex optimization problems – is also employed to optimize the network parameters including learning rate, momentum of the network and the number of MFs for each input. A comparison of these techniques (ANN, Adaptive Neuro-Fuzzy Inference System or ANFIS) with this new method (CANFIS–GA) is also carried out through a case study in Sungun copper deposit, located in East-Azerbaijan, Iran. The results show that CANFIS–GA could be a faster and more accurate alternative to the existing time-consuming methodologies for ore grade estimation and that is, therefore, suggested to be applied for grade estimation in similar problems. PMID:25540468
Systematic investigation on topological properties of layered GaS and GaSe under strain
An, Wei; Tian, Guang-Shan; Wu, Feng; Jiang, Hong; Li, Xin-Zheng
2014-08-28
The topological properties of layered β-GaS and ε-GaSe under strain are systematically investigated by ab initio calculations with the electronic exchange-correlation interactions treated beyond the generalized gradient approximation (GGA). Based on the GW method and the Tran-Blaha modified Becke-Johnson potential approach, we find that while ε-GaSe can be strain-engineered to become a topological insulator, β-GaS remains a trivial one even under strong strain, which is different from the prediction based on GGA. The reliability of the fixed volume assumption rooted in nearly all the previous calculations is discussed. By comparing to strain calculations with optimized inter-layer distance, we find that the fixed volume assumption is qualitatively valid for β-GaS and ε-GaSe, but there are quantitative differences between the results from the fixed volume treatment and those from more realistic treatments. This work indicates that it is risky to use theoretical approaches like GGA that suffer from the band gap problem to address physical properties, including, in particular, the topological nature of band structures, for which the band gap plays a crucial role. In the latter case, careful calibration against more reliable methods like the GW approach is strongly recommended.
Electron and proton degradation in /AlGa/As-GaAs solar cells
NASA Technical Reports Server (NTRS)
Loo, R.; Knechtli, R. C.; Kamath, G. S.; Goldhammer, L.; Anspaugh, B.
1978-01-01
Results on radiation damage in (AlGa)As-GaAs solar cells by 1 MeV electron fluences up to 10 to the 16th electrons/sq cm and by 15, 20, 30 and 40 MeV proton fluences up to 5 times 10 to the 11th protons/sq cm are presented. The damage is compared with data on state-of-the-art silicon cells which were irradiated along with the gallium arsenide cells. The theoretical expectation that the junction depth has to be kept relatively shallow, to minimize radiation damage has been verified experimentally. The damage to the GaAs cells as a function of irradiation, is correlated with the change in their spectral response and dark I-V characteristics. The effect of thermal annealing on the (AlGa)As-GaAs solar cells was also investigated. This data is used to predict further avenues of optimization of the GaAs cells.
NASA Astrophysics Data System (ADS)
Yu, Xuezhe; Li, Lixia; Wang, Hailong; Xiao, Jiaxing; Shen, Chao; Pan, Dong; Zhao, Jianhua
2016-05-01
For the epitaxial growth of Ga-based III-V semiconductor nanowires (NWs) on Si, Ga droplets could provide a clean and compatible solution in contrast to the common Au catalyst. However, the use of Ga droplets is rather limited except for that in Ga-catalyzed GaAs NW studies in a relatively narrow growth temperature (Ts) window around 620 °C on Si. In this paper, we have investigated the two-step growth of Ga-catalyzed III-V NWs on Si (111) substrates by molecular-beam epitaxy. First, by optimizing the surface oxide, vertically aligned GaAs NWs with a high yield are obtained at Ts = 620 °C. Then a two-temperature procedure is adopted to preserve Ga droplets at lower Ts, which leads to an extension of Ts down to 500 °C for GaAs NWs. Based on this procedure, systematic morphological and structural studies for Ga-catalyzed GaAs NWs in the largest Ts range could be presented. Then within the same growth scheme, for the first time, we demonstrate Ga-catalyzed GaAs/GaSb heterostructure NWs. These GaSb NWs are axially grown on the GaAs NW sections and are pure zinc-blende single crystals. Compositional measurements confirm that the catalyst particles indeed mainly consist of Ga and GaSb sections are of high purity but with a minor composition of As. In the end, we present GaAsSb NW growth with a tunable Sb composition. Our results provide useful information for the controllable synthesis of multi-compositional Ga-catalyzed III-V semiconductor NWs on Si for heterogeneous integration.For the epitaxial growth of Ga-based III-V semiconductor nanowires (NWs) on Si, Ga droplets could provide a clean and compatible solution in contrast to the common Au catalyst. However, the use of Ga droplets is rather limited except for that in Ga-catalyzed GaAs NW studies in a relatively narrow growth temperature (Ts) window around 620 °C on Si. In this paper, we have investigated the two-step growth of Ga-catalyzed III-V NWs on Si (111) substrates by molecular-beam epitaxy. First, by
NASA Astrophysics Data System (ADS)
Dong, L.; Mantese, J. V.; Avrutin, V.; Özgür, Ü.; Morkoç, H.; Alpay, S. P.
2013-07-01
The band structure, quantum confinement of charge carriers, and their localization affect the optoelectronic properties of compound semiconductor heterostructures and multiple quantum wells (MQWs). We present here the results of a systematic first-principles based density functional theory (DFT) investigation of the dependence of the valence band offsets and band bending in polar and non-polar strain-free and in-plane strained heteroepitaxial InxGa1-xN(InGaN)/GaN multilayers on the In composition and misfit strain. The results indicate that for non-polar m-plane configurations with [12¯10]InGaN//[12¯10]GaN and [0001]InGaN//[0001]GaN epitaxial alignments, the valence band offset changes linearly from 0 to 0.57 eV as the In composition is varied from 0 (GaN) to 1 (InN). These offsets are relatively insensitive to the misfit strain between InGaN and GaN. On the other hand, for polar c-plane strain-free heterostructures with [101¯0]InGaN//[101¯0]GaN and [12¯10]InGaN//[12¯10]GaN epitaxial alignments, the valence band offset increases nonlinearly from 0 eV (GaN) to 0.90 eV (InN). This is significantly reduced beyond x ≥ 0.5 by the effect of the equi-biaxial misfit strain. Thus, our results affirm that a combination of mechanical boundary conditions, epitaxial orientation, and variation in In concentration can be used as design parameters to rapidly tailor the band offsets in InGaN/GaN MQWs. Typically, calculations of the built-in electric field in complex semiconductor structures often must rely upon sequential optimization via repeated ab initio simulations. Here, we develop a formalism that augments such first-principles computations by including an electrostatic analysis (ESA) using Maxwell and Poisson's relations, thereby converting laborious DFT calculations into finite difference equations that can be rapidly solved. We use these tools to determine the bound sheet charges and built-in electric fields in polar epitaxial InGaN/GaN MQWs on c-plane Ga
1.58 {mu}m InGaAs quantum well laser on GaAs
Taangring, I.; Ni, H. Q.; Wu, B. P.; Wu, D. H.; Xiong, Y. H.; Huang, S. S.; Niu, Z. C.; Wang, S. M.; Lai, Z. H.; Larsson, A.
2007-11-26
We demonstrate the 1.58 {mu}m emission at room temperature from a metamorphic In{sub 0.6}Ga{sub 0.4}As quantum well laser grown on GaAs by molecular beam epitaxy. The large lattice mismatch was accommodated through growth of a linearly graded buffer layer to create a high quality virtual In{sub 0.32}Ga{sub 0.68}As substrate. Careful growth optimization ensured good optical and structural qualities. For a 1250x50 {mu}m{sup 2} broad area laser, a minimum threshold current density of 490 A/cm{sup 2} was achieved under pulsed operation. This result indicates that metamorphic InGaAs quantum wells can be an alternative approach for 1.55 {mu}m GaAs-based lasers.
Graded-bandgap AlGaAs solar cells for AlGaAs/Ge cascade cells
NASA Technical Reports Server (NTRS)
Timmons, M. L.; Venkatasubramanian, R.; Colpitts, T. S.; Hills, J. S.; Hutchby, J. A.; Iles, P. A.; Chu, C. L.
1991-01-01
Some p/n graded-bandgap Al(x)Ga(1-x)As solar cells were fabricated and show AMO conversion efficiencies in excess of 15 percent without antireflection (AR) coatings. The emitters of these cells are graded between 0.008 is less than or equal to x is less than or equal to 0.02 during growth of 0.25 to 0.30 micron thick layers. The keys to achieving this performance were careful selection of organometallic sources and scrubbing oxygen and water vapor from the AsH3 source. Source selection and growth were optimized using time-resolved photoluminescence. Preliminary radiation-resistance measurements show AlGaAs cells degraded less than GaAs cells at high 1 MeV electron fluences, and AlGaAs cells grown on GaAs and Ge substrates degrade comparably.
Alicia Hofler; Pavel Evtushenko
2007-07-03
Injector gun design is an iterative process where the designer optimizes a few nonlinearly interdependent beam parameters to achieve the required beam quality for a particle accelerator. Few tools exist to automate the optimization process and thoroughly explore the parameter space. The challenging beam requirements of new accelerator applications such as light sources and electron cooling devices drive the development of RF and SRF photo injectors. A genetic algorithm (GA) has been successfully used to optimize DC photo injector designs at Cornell University [1] and Jefferson Lab [2]. We propose to apply GA techniques to the design of RF and SRF gun injectors. In this paper, we report on the initial phase of the study where we model and optimize a system that has been benchmarked with beam measurements and simulation.
Nikolsky, Aleksey
2016-01-01
thinking, adopted as a standard to optimize individual perception of reality within a social group in a way optimal for one's success, thereby setting the conventions of intellectual and emotional intelligence.
Nikolsky, Aleksey
2016-01-01
thinking, adopted as a standard to optimize individual perception of reality within a social group in a way optimal for one's success, thereby setting the conventions of intellectual and emotional intelligence. PMID:27065893
Nikolsky, Aleksey
2016-01-01
reflects the culture of thinking, adopted as a standard to optimize individual perception of reality within a social group in a way optimal for one's success, thereby setting the conventions of intellectual and emotional intelligence. PMID:27065893
Pell, Gaby S; Abbott, David F; Fleming, Steven W; Prichard, James W; Jackson, Graeme D
2006-05-01
The characteristics of an MRI technique that could be used for direct detection of neuronal activity are investigated. It was shown that magnitude imaging using echo planar imaging can detect transient local currents. The sensitivity of this method was thoroughly investigated. A partial k-space EPI acquisition with homodyne reconstruction was found to increase the signal change. A unique sensitivity to the position of the current pulse within the imaging sequence was demonstrated with the greatest signal change occurring when the current pulse coincides with the acquisition of the center lines of k-space. The signal change was shown to be highly sensitive to the spatial position of the current conductor relative to the voxel. Furthermore, with the use of optimization of spatial and temporal placement of the current pulse, the level of signal change obtained at this lower limit of current detectability was considerably magnified. It was possible to detect a current of 1.7 microA applied for 20 ms with an imaging time of 1.8 min. The level of sensitivity observed in our study brings us closer to that theoretically required for the detection of action currents in nerves.
Zhang, Zi-Hui; Tan, Swee Tiam; Liu, Wei; Ju, Zhengang; Zheng, Ke; Kyaw, Zabu; Ji, Yun; Hasanov, Namig; Sun, Xiao Wei; Demir, Hilmi Volkan
2013-02-25
This work reports both experimental and theoretical studies on the InGaN/GaN light-emitting diodes (LEDs) with optical output power and external quantum efficiency (EQE) levels substantially enhanced by incorporating p-GaN/n-GaN/p-GaN/n-GaN/p-GaN (PNPNP-GaN) current spreading layers in p-GaN. Each thin n-GaN layer sandwiched in the PNPNP-GaN structure is completely depleted due to the built-in electric field in the PNPNP-GaN junctions, and the ionized donors in these n-GaN layers serve as the hole spreaders. As a result, the electrical performance of the proposed device is improved and the optical output power and EQE are enhanced.
Monolithic AlGaAs second-harmonic nanoantennas.
Gili, V F; Carletti, L; Locatelli, A; Rocco, D; Finazzi, M; Ghirardini, L; Favero, I; Gomez, C; Lemaître, A; Celebrano, M; De Angelis, C; Leo, G
2016-07-11
We demonstrate monolithic aluminum gallium arsenide (AlGaAs) optical nanoantennas. Using a selective oxidation technique, we fabricated epitaxial semiconductor nanocylinders on an aluminum oxide substrate. Second harmonic generation from AlGaAs nanocylinders of 400 nm height and varying radius pumped with femtosecond pulses delivered at 1554-nm wavelength has been measured, revealing a peak conversion efficiency exceeding 10^{-5} for nanocylinders with an optimized geometry. PMID:27410864
Flexible body control using neural networks
NASA Technical Reports Server (NTRS)
Mccullough, Claire L.
1992-01-01
Progress is reported on the control of Control Structures Interaction suitcase demonstrator (a flexible structure) using neural networks and fuzzy logic. It is concluded that while control by neural nets alone (i.e., allowing the net to design a controller with no human intervention) has yielded less than optimal results, the neural net trained to emulate the existing fuzzy logic controller does produce acceptible system responses for the initial conditions examined. Also, a neural net was found to be very successful in performing the emulation step necessary for the anticipatory fuzzy controller for the CSI suitcase demonstrator. The fuzzy neural hybrid, which exhibits good robustness and noise rejection properties, shows promise as a controller for practical flexible systems, and should be further evaluated.
Yu, Xuezhe; Li, Lixia; Wang, Hailong; Xiao, Jiaxing; Shen, Chao; Pan, Dong; Zhao, Jianhua
2016-05-19
For the epitaxial growth of Ga-based III-V semiconductor nanowires (NWs) on Si, Ga droplets could provide a clean and compatible solution in contrast to the common Au catalyst. However, the use of Ga droplets is rather limited except for that in Ga-catalyzed GaAs NW studies in a relatively narrow growth temperature (Ts) window around 620 °C on Si. In this paper, we have investigated the two-step growth of Ga-catalyzed III-V NWs on Si (111) substrates by molecular-beam epitaxy. First, by optimizing the surface oxide, vertically aligned GaAs NWs with a high yield are obtained at Ts = 620 °C. Then a two-temperature procedure is adopted to preserve Ga droplets at lower Ts, which leads to an extension of Ts down to 500 °C for GaAs NWs. Based on this procedure, systematic morphological and structural studies for Ga-catalyzed GaAs NWs in the largest Ts range could be presented. Then within the same growth scheme, for the first time, we demonstrate Ga-catalyzed GaAs/GaSb heterostructure NWs. These GaSb NWs are axially grown on the GaAs NW sections and are pure zinc-blende single crystals. Compositional measurements confirm that the catalyst particles indeed mainly consist of Ga and GaSb sections are of high purity but with a minor composition of As. In the end, we present GaAsSb NW growth with a tunable Sb composition. Our results provide useful information for the controllable synthesis of multi-compositional Ga-catalyzed III-V semiconductor NWs on Si for heterogeneous integration.
Monitoring Neural Activity with Bioluminescence during Natural Behavior
Naumann, Eva A.; Kampff, Adam R.; Prober, David A.; Schier, Alexander F.; Engert, Florian
2010-01-01
Existing techniques for monitoring neural activity in awake, freely behaving vertebrates are invasive and difficult to target to genetically identified neurons. Here we describe the use of bioluminescence to non-invasively monitor the activity of genetically specified neurons in freely behaving zebrafish. Transgenic fish expressing the Ca2+-sensitive photoprotein GFP-apoAequorin (GA) in most neurons generated large and fast bioluminescent signals related to neural activity, neuroluminescence, that could be recorded continuously for many days. To test the limits of this technique, GA was specifically targeted to the hypocretin-positive neurons of the hypothalamus. We found that neuroluminescence generated by this group of ~20 neurons was associated with periods of increased locomotor activity and identified two classes of neural activity corresponding to distinct swim latencies. Thus, our neuroluminescence assay can report, with high temporal resolution and sensitivity, the activity of small subsets of neurons during unrestrained behavior. PMID:20305645
Optimization design for the supporting system of 2m telescope primary mirror
NASA Astrophysics Data System (ADS)
Zhao, Fu; Wang, Ping; Gong, Yanjue; Zhang, Li; Lin, Jianlong
2008-12-01
This paper describes the optimization solution improving the total quality of the primary mirror supporting type. With the methods of Finite element analysis(FEA), Orthogonal experiment and BP Neural Network, the relationship between the structure parameters in primary mirror supporting type and the deformation of the primary mirror is built. With this relationship and Genetic Algorithm(GA) optimization design, a group of reasonable technology parameters is found that can improve the static stiffness of the primary mirror supporting type so as to reduce the gravity deformation of the primary mirror. The modal analysis and random vibration analysis are also discussed in detail, and the results indicate that the dynamic stiffness of the primary mirror supporting type is also improved.
Optimal Planning Strategy for Large PV/Battery System Based on Long-Term Insolation Forecasting
NASA Astrophysics Data System (ADS)
Yona, Atsushi; Uchida, Kosuke; Senjyu, Tomonobu; Funabashi, Toshihisa
Photovoltaic (PV) systems are rapidly gaining acceptance as some of the best alternative energy sources. Usually the power output of PV system fluctuates depending on weather conditions. In order to control the fluctuating power output for PV system, it requires control method of energy storage system. This paper proposes an optimization approach to determine the operational planning of power output for PV system with battery energy storage system (BESS). This approach aims to obtain more benefit for electrical power selling and to smooth the fluctuating power output for PV system. The optimization method applies genetic algorithm (GA) considering PV power output forecast error. The forecast error is based on our previous works with the insolation forecasting at one day ahead by using weather reported data, fuzzy theory and neural network(NN). The validity of the proposed method is confirmed by the computer simulations.
VLSI Cells Placement Using the Neural Networks
Azizi, Hacene; Zouaoui, Lamri; Mokhnache, Salah
2008-06-12
The artificial neural networks have been studied for several years. Their effectiveness makes it possible to expect high performances. The privileged fields of these techniques remain the recognition and classification. Various applications of optimization are also studied under the angle of the artificial neural networks. They make it possible to apply distributed heuristic algorithms. In this article, a solution to placement problem of the various cells at the time of the realization of an integrated circuit is proposed by using the KOHONEN network.
Efficiency of GaInAs/GaAs quantum-well lasers upon inhomogeneous excitation of quantum wells
Ushakov, D V; Afonenko, A A; Aleshkin, V Ya
2013-11-30
A model for calculating the power characteristics of laser structures taking into account inhomogeneous excitation of quantum wells (QWs), recombination in the barrier regions, and nonlinear gain effects is developed. It is shown that, with increasing number of QWs, the output power of the Ga{sub 0.8}In{sub 0.2}As/GaAs/InGaP structures at first considerably increases and then slightly decreases. In a wide range of injection currents, the optimal number of QWs is 5 ± 1. The inhomogeneity of QW excitation increases with increasing injection current and decreases the laser power compared to homogeneous excitation. (lasers)
A novel recurrent neural network with finite-time convergence for linear programming.
Liu, Qingshan; Cao, Jinde; Chen, Guanrong
2010-11-01
In this letter, a novel recurrent neural network based on the gradient method is proposed for solving linear programming problems. Finite-time convergence of the proposed neural network is proved by using the Lyapunov method. Compared with the existing neural networks for linear programming, the proposed neural network is globally convergent to exact optimal solutions in finite time, which is remarkable and rare in the literature of neural networks for optimization. Some numerical examples are given to show the effectiveness and excellent performance of the new recurrent neural network.
Electron transport simulation in resonant-tunneling GaN/AlGaN heterostructures
Egorkin, V. I. Zhuravlev, M. N.; Kapaev, V. V.
2011-12-15
A numerical method for electron transport calculations in resonant-tunneling GaN/AlGaN heterostructures has been developed on the basis of a self-consistent solution of the Schroedinger and Poisson equations. Dependences of the system's transmission coefficient on the external field and of the peak current on the ratio between the well and barrier widths have been studied for a double-barrier resonant-tunneling diode. For technical applications, the optimal values of the structure's parameters have been found.
NASA Astrophysics Data System (ADS)
Shevchenko, E. A.; Nechaev, D. V.; Jmerik, V. N.; Kaibyshev, V. Kh; Ivanov, S. V.; Toropov, A. A.
2016-08-01
We present photoluminescence studies of AIxGa1-xN/AlyGa1-yN (y = x+0.3) quantum well (QW) heterostructures with graded AI content in barrier layers, emitting in the range 285-315 nm. The best-established internal quantum efficiency of the QW emission is as high as 81% at 300 K, owing to enhanced activation energy of charge carriers and exciton binding energy in the QW heterostructure with optimized design.
Multijunction GaInP/GaInAs/Ge solar cells with Bragg reflectors
Emelyanov, V. M. Kalyuzhniy, N. A.; Mintairov, S. A.; Shvarts, M. Z.; Lantratov, V. M.
2010-12-15
Effect of subcell parameters on the efficiency of GaInP/Ga(In)As/Ge tandem solar cells irradiated with 1-MeV electrons at fluences of up to 3 x 10{sup 15} cm{sup -2} has been theoretically studied. The optimal thicknesses of GaInP and GaInAs subcells, which provide the best photocurrent matching at various irradiation doses in solar cells with and without built-in Bragg reflectors, were determined. The dependences of the photoconverter efficiency on the fluence of 1-MeV electrons and on the time of residence in the geostationary orbit were calculated for structures optimized to the beginning and end of their service lives. It is shown that the optimization of the subcell heterostructures for a rated irradiation dose and the introduction of Bragg reflectors into the structure provide a 5% overall increase in efficiency for solar cells operating in the orbit compared with unoptimized cells having no Bragg reflector.