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Sample records for artificial neural nets

  1. Hadamard design and artificial neural nets

    SciTech Connect

    Kuerten, K.E. Universitaet Wien ); Klingen, N. )

    1993-04-01

    Hadamard theory is shown to play an important role in the generation of Boolean decision functions, a fundamental tool in the field of artificial neural network design. Based on a group-theoretic introduction of a complete set of Hadamard vectors, whose matrices are of the order of a power of two, the authors classify subsets according to the degree of their linear dependence. They show in the thermodynamic limit that essentially the whole Hadamard space is occupied by representatives with defect not exceeding two or three. 15 refs., 1 fig.

  2. 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.

  3. 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.

  4. Applications of artificial neural nets in clinical biomechanics.

    PubMed

    Schöllhorn, W I

    2004-11-01

    The purpose of this article is to provide an overview of current applications of artificial neural networks in the area of clinical biomechanics. The body of literature on artificial neural networks grew intractably vast during the last 15 years. Conventional statistical models may present certain limitations that can be overcome by neural networks. Artificial neural networks in general are introduced, some limitations, and some proven benefits are discussed. PMID:15475120

  5. Surface daytime net radiation estimation using artificial neural networks

    DOE PAGESBeta

    Jiang, Bo; Zhang, Yi; Liang, Shunlin; Zhang, Xiaotong; Xiao, Zhiqiang

    2014-11-11

    Net all-wave surface radiation (Rn) is one of the most important fundamental parameters in various applications. However, conventional Rn measurements are difficult to collect because of the high cost and ongoing maintenance of recording instruments. Therefore, various empirical Rn estimation models have been developed. This study presents the results of two artificial neural network (ANN) models (general regression neural networks (GRNN) and Neuroet) to estimate Rn globally from multi-source data, including remotely sensed products, surface measurements, and meteorological reanalysis products. Rn estimates provided by the two ANNs were tested against in-situ radiation measurements obtained from 251 global sites between 1991–2010more » both in global mode (all data were used to fit the models) and in conditional mode (the data were divided into four subsets and the models were fitted separately). Based on the results obtained from extensive experiments, it has been proved that the two ANNs were superior to linear-based empirical models in both global and conditional modes and that the GRNN performed better and was more stable than Neuroet. The GRNN estimates had a determination coefficient (R2) of 0.92, a root mean square error (RMSE) of 34.27 W·m–2 , and a bias of –0.61 W·m–2 in global mode based on the validation dataset. In conclusion, ANN methods are a potentially powerful tool for global Rn estimation.« less

  6. Surface daytime net radiation estimation using artificial neural networks

    SciTech Connect

    Jiang, Bo; Zhang, Yi; Liang, Shunlin; Zhang, Xiaotong; Xiao, Zhiqiang

    2014-11-11

    Net all-wave surface radiation (Rn) is one of the most important fundamental parameters in various applications. However, conventional Rn measurements are difficult to collect because of the high cost and ongoing maintenance of recording instruments. Therefore, various empirical Rn estimation models have been developed. This study presents the results of two artificial neural network (ANN) models (general regression neural networks (GRNN) and Neuroet) to estimate Rn globally from multi-source data, including remotely sensed products, surface measurements, and meteorological reanalysis products. Rn estimates provided by the two ANNs were tested against in-situ radiation measurements obtained from 251 global sites between 1991–2010 both in global mode (all data were used to fit the models) and in conditional mode (the data were divided into four subsets and the models were fitted separately). Based on the results obtained from extensive experiments, it has been proved that the two ANNs were superior to linear-based empirical models in both global and conditional modes and that the GRNN performed better and was more stable than Neuroet. The GRNN estimates had a determination coefficient (R2) of 0.92, a root mean square error (RMSE) of 34.27 W·m–2 , and a bias of –0.61 W·m–2 in global mode based on the validation dataset. In conclusion, ANN methods are a potentially powerful tool for global Rn estimation.

  7. Application of artificial neural nets to Shashlik calorimetry

    NASA Astrophysics Data System (ADS)

    Bonesini, M.; Gumenyuk, S.; Paganoni, M.; Petrovykh, L.; Terranova, F.

    1997-02-01

    Artificial Neural Networks (ANN) are powerful tools widely used in high-energy physics to solve track finding and particle identification problems. An entirely new class of application is related to the problem of recovering the information lost during data taking or signal transmission. Good performances can be reached by ANN when the events are described by quite regular patterns. Such a method was used for the DELPHI luminosity monitor (STIC) to recover calorimeter dead channels. A comparison with more traditional techniques is also given.

  8. A comparison of polynomial approximations and artificial neural nets as response surfaces

    NASA Technical Reports Server (NTRS)

    Carpenter, William C.; Barthelemy, Jean-Francois M.

    1992-01-01

    Artificial neural nets and polynomial approximations were used to develop response surfaces for several test problems. Based on the number of functional evaluations required to build the approximations and the number of undetermined parameters associated with the approximations, the performance of the two types of approximations was found to be comparable. A rule of thumb is developed for determining the number of nodes to be used on a hidden layer of an artificial neural net, and the number of designs needed to train an approximation is discussed.

  9. Artificial awareness for robots using artificial neural nets to monitor robotic workcells

    SciTech Connect

    Tucker, S.D.; Ray, L.P.

    1997-04-01

    Current robotic systems are unable to recognize most unexpected changes in the work environment, such as tool breakage, workpiece motion, or sensor failure. Unless halted by a human operator, they are likely to continue actions that are at best inappropriate, and at worst may cause damage to the workpiece or robot. This project investigated use of Artificial Neural Networks (ANNs) to learn the expected characteristics of sensor data during normal operations, recognize when data no longer is consistent with normal operation, suspend operations and alert a human operator. Data on force and torque applied at the robot tool tip were collected from two workcells: a robotic deburring system and a robot material-handling system. Data were collected for normal operations and for operations in which a fault condition was introduced. Data simulating sensor failure and excessive sensor noise were generated. Artificial Neural Networks (ANN) were trained to classify operating conditions; several ANN architectures were tested. The selected ANNs were able to correctly classify all valid operating conditions and the majority of fault conditions over the entire range of operating conditions, having {open_quotes}learned{close_quotes} the expected force/torque data. Most faults introduced appreciable error in the data; these were correctly classified. However, a small minority of faults did not give rise to a detectable difference in force and torque data. It is believed that these faults could be detected using other sensors. The computational workload varies with the implementation, but is moderate: up to 2.3 megaflops. This makes implementation of a real-time workcell monitor feasible.

  10. Discussion of using artificial neural nets to identify the well-test interpretation model

    SciTech Connect

    Yeung, K. ); Chakrabarty, C. ); Wu, S. )

    1994-09-01

    Use of artificial neural nets (ANN's) to identify noisy and apparently unrecognizable patterns is common for many real-world problems, ranging from applications such as speech recognition to stock market prediction. ANN approaches are often good candidates for recognizing patterns when rigid mathematical models do not exist or are insufficient to meet a full-scale identification requirement. Al-Kaabi and Lee's proposal of using ANN's to identify the well-test interpretation model is appropriate because well-test data is often highly nonlinear and noisy. The purpose of this discussion is to present some of the authors results in a similar study and to suggest a simple technique that would enhance the use of ANN's in Al-Kaabi and Lee's approach.

  11. Comparison of polynomial approximations and artificial neural nets for response surfaces in engineering optimization

    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.

  12. Smallest artificial molecular neural-net for collective and emergent information processing

    NASA Astrophysics Data System (ADS)

    Bandyopadhyay, Anirban; Sahu, Satyajit; Fujita, Daisuke

    2009-09-01

    While exploring the random diffusion of 2 bit molecular switches (we define as molecular neuron) on an atomic flat Au (111) substrate, we have found that at least four molecules are required to construct a functional neural net. Surface electron density wave enables communication of one to many molecules at a time—a prerequisite for the parallel processing. Here we have shown that in a neural net of several molecules, some of them could dynamically store information as memory and consistently replicate the fundamental relationship that is found only in a collective and emergent computing system like our brain.

  13. Detection and location of pipe damage by artificial-neural-net-processed moire error maps

    NASA Astrophysics Data System (ADS)

    Grossman, Barry G.; Gonzalez, Frank S.; Blatt, Joel H.; Cahall, Scott C.

    1993-05-01

    A novel automated inspection technique to recognize, locate, and quantify damage is developed. This technique is based on two already existing technologies: video moire metrology and artificial neural networks. Contour maps generated by video moire techniques provide an accurate description of surface structure that can then be automated by means of neutral networks. Artificial neural networks offer an attractive solution to the automated interpretation problem because they can generalize from the learned samples and provide an intelligent response for similar patterns having missing or noisy data. Two dimensional video moire images of pipes with dents of different depths, at several rotations, were used to train a multilayer feedforward neural network by the backpropagation algorithm. The backpropagation network is trained to recognize and classify the video moire images according to the dent's depth. Once trained, the network outputs give an indication of the probability that a dent has been found, a depth estimate, and the axial location of the center of the dent. This inspection technique has been demonstrated to be a powerful tool for the automatic location and quantification of structural damage, as illustrated using dented pipes.

  14. Neural Network Development Tool (NETS)

    NASA Technical Reports Server (NTRS)

    Baffes, Paul T.

    1990-01-01

    Artificial neural networks formed from hundreds or thousands of simulated neurons, connected in manner similar to that in human brain. Such network models learning behavior. Using NETS involves translating problem to be solved into input/output pairs, designing network configuration, and training network. Written in C.

  15. An artificial neural net and error backpropagation to reconstruct single photon emission computerized tomography data.

    PubMed

    Knoll, P; Mirzaei, S; Müllner, A; Leitha, T; Koriska, K; Köhn, H; Neumann, M

    1999-02-01

    At present, algorithms used in nuclear medicine to reconstruct single photon emission computerized tomography (SPECT) data are usually based on one of two principles: filtered backprojection and iterative methods. In this paper a different algorithm, applying an artificial neural network (multilayer perception) and error backpropagation as training method are used to reconstruct transaxial slices from SPECT data. The algorithm was implemented on an Elscint XPERT workstation (i486, 50 MHz), used as a routine digital image processing tool in our departments. Reconstruction time for a 64 x 64 matrix is approximately 45 s/transaxial slice. The algorithm has been validated by a mathematical model and tested on heart and Jaszczak phantoms. Phantom studies and very first clinical results ((111)In octreotide SPECT, 99mTc MDP bone SPECT) show in comparison with filtered backprojection an enhancement in image quality. PMID:10076982

  16. Refractory neural nets and vision

    NASA Astrophysics Data System (ADS)

    Fall, Thomas C.

    2014-02-01

    Biological understandings have served as the basis for new computational approaches. A prime example is artificial neural nets which are based on the biological understanding of the trainability of neural synapses. In this paper, we will investigate features of the biological vision system to see if they can also be exploited. These features are 1) the neuron's refractory period - the period of time after the neuron fires before it can fire again and 2) the ocular microtremor which moves the retinal neural array relative to the image. The short term memory due to the refractory period allows the before and after movement views to be compared. This paper will discuss the investigation of the implications of these two features.

  17. 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.

  18. Quantum Neural Nets

    NASA Technical Reports Server (NTRS)

    Zak, Michail; Williams, Colin P.

    1997-01-01

    The capacity of classical neurocomputers is limited by the number of classical degrees of freedom which is roughly proportional to the size of the computer. By Contrast, a Hypothetical quantum neurocomputer can implement an exponentially large number of the degrees of freedom within the same size. In this paper an attempt is made to reconcile linear reversible structure of quantum evolution with nonlinear irreversible dynamics for neural nets.

  19. Neural nets on the MPP

    NASA Technical Reports Server (NTRS)

    Hastings, Harold M.; Waner, Stefan

    1987-01-01

    The Massively Parallel Processor (MPP) is an ideal machine for computer experiments with simulated neural nets as well as more general cellular automata. Experiments using the MPP with a formal model neural network are described. The results on problem mapping and computational efficiency apply equally well to the neural nets of Hopfield, Hinton et al., and Geman and Geman.

  20. Synchronization in neural nets

    NASA Technical Reports Server (NTRS)

    Vidal, Jacques J.; Haggerty, John

    1988-01-01

    The paper presents an artificial neural network concept (the Synchronizable Oscillator Networks) where the instants of individual firings in the form of point processes constitute the only form of information transmitted between joining neurons. In the model, neurons fire spontaneously and regularly in the absence of perturbation. When interaction is present, the scheduled firings are advanced or delayed by the firing of neighboring neurons. Networks of such neurons become global oscillators which exhibit multiple synchronizing attractors. From arbitrary initial states, energy minimization learning procedures can make the network converge to oscillatory modes that satisfy multi-dimensional constraints. Such networks can directly represent routing and scheduling problems that consist of ordering sequences of events.

  1. Generalized Adaptive Artificial Neural Networks

    NASA Technical Reports Server (NTRS)

    Tawel, Raoul

    1993-01-01

    Mathematical model of supervised learning by artificial neural network provides for simultaneous adjustments of both temperatures of neurons and synaptic weights, and includes feedback as well as feedforward synaptic connections. Extension of mathematical model described in "Adaptive Neurons For Artificial Neural Networks" (NPO-17803). Dynamics of neural network represented in new model by less-restrictive continuous formalism.

  2. Artificial neural superposition eye.

    PubMed

    Brückner, Andreas; Duparré, Jacques; Dannberg, Peter; Bräuer, Andreas; Tünnermann, Andreas

    2007-09-17

    We propose an ultra-thin imaging system which is based on the neural superposition compound eye of insects. Multiple light sensitive pixels in the footprint of each lenslet of this multi-channel configuration enable the parallel imaging of the individual object points. Together with the digital superposition of related signals this multiple sampling enables advanced functionalities for artificial compound eyes. Using this technique, color imaging and a circumvention for the trade-off between resolution and sensitivity of ultra-compact camera devices have been demonstrated in this article. The optical design and layout of such a system is discussed in detail. Experimental results are shown which indicate the attractiveness of microoptical artificial compound eyes for applications in the field of machine vision, surveillance or automotive imaging. PMID:19547555

  3. Weakly connected neural nets

    NASA Technical Reports Server (NTRS)

    Zak, Michail

    1990-01-01

    A new neural network architecture is proposed based upon effects of non-Lipschitzian dynamics. The network is fully connected, but these connections are active only during vanishingly short time periods. The advantages of this architecture are discussed.

  4. Implementing neural nets with programmable logic

    NASA Technical Reports Server (NTRS)

    Vidal, Jacques J.

    1988-01-01

    Networks of Boolean programmable logic modules are presented as one purely digital class of artificial neural nets. The approach contrasts with the continuous analog framework usually suggested. Programmable logic networks are capable of handling many neural-net applications. They avoid some of the limitations of threshold logic networks and present distinct opportunities. The network nodes are called dynamically programmable logic modules. They can be implemented with digitally controlled demultiplexers. Each node performs a Boolean function of its inputs which can be dynamically assigned. The overall network is therefore a combinational circuit and its outputs are Boolean global functions of the network's input variables. The approach offers definite advantages for VLSI implementation, namely, a regular architecture with limited connectivity, simplicity of the control machinery, natural modularity, and the support of a mature technology.

  5. Stochastic neural nets and vision

    NASA Astrophysics Data System (ADS)

    Fall, Thomas C.

    1991-03-01

    A stochastic neural net shares with the normally defined neural nets the concept that information is processed by a system consisting of a set of nodes (neurons) connected by weighted links (axons). The normal neural net takes in inputs on an initial layer of neurons which fire appropriately; a neuron of the next layer fires depending on the sum of weights of the axons leading to it from fired neurons of the first layer. The stochastic neural net differs in that the neurons are more complex and that the vision activity is a dynamic process. The first layer (viewing layer) of neurons fires stochastically based on the average brightness of the area it sees and then has a refractory period. The viewing layer looks at the image for several clock cycles. The effect is like those photo sensitive sunglasses that darken in bright light. The neurons over the bright areas are most likely in a refractory period (and this can't fire) and the neurons over the dark areas are not. Now if we move the sensing layer with respect to the image so that a portion of the neurons formerly over the dark are now over the bright, they will likely all fire on that first cycle. Thus, on that cycle, one would see a flash from that portion significantly stronger than surrounding regions. Movement the other direction would produce a patch that is darker, but this effect is not as noticeable. These effects are collected in a collection layer. This paper will discuss the use of the stochastic neural net for edge detection and segmentation of some simple images.

  6. Multifunctional hybrid optical/digital neural net

    NASA Astrophysics Data System (ADS)

    Casasent, David P.

    1990-08-01

    A multi-functional hybrid neural net is described. It is hybrid since it uses a digital hardware Hecht-Nielsen Corporation (HNC) neural net for adaptive learning and an optical neural net for on-line processing/classification. It is also hybrid in its combination of pattern recognition and neural net techniques. The system is multi-functional. It can function as an optimization and adaptive pattern recognition neural net as well as an auto and heteroassociative processor. I . W. JTRODUCTION Neural nets (NNs) have recently received enormous attention [1 -2] with increasing attention to the use of optical processors and a variety of new learning algorithms. Section 2 describes our hybrid NN with attention to Its fabrication and the role for optical and digital processors. Section 3 details Its use as an associative processor. Section 4 highlights is use in 3 optimization NN problems (a mixture NN a multitarget tracker (MTT) NN and a matrix inversion NN). Section 5 briefly notes it use as a production NN system and symbolic NN. Section 6 describes its use as an adaptive pattern recognition (PR) NN (that marries PR and NN techniques). 2. HYBRID ARCHITECTURE Figure 1 shows our basic hybrid NN [3]. The optical portion of the system is a matrix-vector (M-V) processor whose vector output P3 is the product of the vector at P1 and the matrix at P2. An HNC digital hardware NN is used during learning determine the interconnection weights forP2. If P2 is a spatial light modulator (SLM) its contents can be updated (using gated learning) from thedigital NN. The operations in most adaptive PR NN learning algorithms are sufficiently complex thatthey are best implemented digitally. In addition the learning operations required are often not well suited for optical realization for optimization NNs the weights are fixed and in adaptive learning learning is off-line and once completed the weights can often be fixed. Four gates are shown that determine the final output or the new P1

  7. A practical guide to neural nets

    SciTech Connect

    Nelson, M.M.; Illingworth, W.T.

    1991-01-01

    The concept of neural networks, their operation, and applications are reviewed. Topics discussed include definitions, terminology, and concepts of neural networks, the principal issues and problems addressed by neural network technology, recent developments in the field of artificial intelligence, characteristics and limitations of neural networks, and various neural network architectures. Other topics covered include the basic learning mechanisms of neural networks, examples of neural network applications, implementations of neural networks, some current problems in neural network research, and suggestions for future research. 126 refs.

  8. Artificial neural networks in medicine

    SciTech Connect

    Keller, P.E.

    1994-07-01

    This Technology Brief provides an overview of artificial neural networks (ANN). A definition and explanation of an ANN is given and situations in which an ANN is used are described. ANN applications to medicine specifically are then explored and the areas in which it is currently being used are discussed. Included are medical diagnostic aides, biochemical analysis, medical image analysis and drug development.

  9. Artificial nets from superconducting nanogranules

    SciTech Connect

    Ovchinnikov, Yu. N.; Kresin, V. Z.

    2012-06-15

    We show that a large transport current can flow through superconducting nets composed of nano-clusters. Although thermal and quantum fluctuations lead to a finite value of dissipation, this value can be very small in one- and two-dimensional systems for realistic parameters of the nanoclusters and distances between them. The value of the action for vortex tunneling at zero temperature can be made sufficiently large to make the dissipation negligibly small. We estimate the temperature T{sub 0} of the transition from the thermal activation to quantum tunneling.

  10. Introduction to Concepts in Artificial Neural Networks

    NASA Technical Reports Server (NTRS)

    Niebur, Dagmar

    1995-01-01

    This introduction to artificial neural networks summarizes some basic concepts of computational neuroscience and the resulting models of artificial neurons. The terminology of biological and artificial neurons, biological and machine learning and neural processing is introduced. The concepts of supervised and unsupervised learning are explained with examples from the power system area. Finally, a taxonomy of different types of neurons and different classes of artificial neural networks is presented.

  11. Neural Net Safety Monitor Design

    NASA Technical Reports Server (NTRS)

    Larson, Richard R.

    2007-01-01

    The National Aeronautics and Space Administration (NASA) at the Dryden Flight Research Center (DFRC) has been conducting flight-test research using an F-15 aircraft (figure 1). This aircraft has been specially modified to interface a neural net (NN) controller as part of a single-string Airborne Research Test System (ARTS) computer with the existing quad-redundant flight control system (FCC) shown in figure 2. The NN commands are passed to FCC channels 2 and 4 and are cross channel data linked (CCDL) to the other computers as shown. Numerous types of fault-detection monitors exist in the FCC when the NN mode is engaged; these monitors would cause an automatic disengagement of the NN in the event of a triggering fault. Unfortunately, these monitors still may not prevent a possible NN hard-over command from coming through to the control laws. Therefore, an additional and unique safety monitor was designed for a single-string source that allows authority at maximum actuator rates but protects the pilot and structural loads against excessive g-limits in the case of a NN hard-over command input. This additional monitor resides in the FCCs and is executed before the control laws are computed. This presentation describes a floating limiter (FL) concept1 that was developed and successfully test-flown for this program (figure 3). The FL computes the rate of change of the NN commands that are input to the FCC from the ARTS. A window is created with upper and lower boundaries, which is constantly floating and trying to stay centered as the NN command rates are changing. The limiter works by only allowing the window to move at a much slower rate than those of the NN commands. Anywhere within the window, however, full rates are allowed. If a rate persists in one direction, it will eventually hit the boundary and be rate-limited to the floating limiter rate. When this happens, a persistent counter begins and after a limit is reached, a NN disengage command is generated. The

  12. Tuning of power system stabilizers using an artificial neural network

    SciTech Connect

    Hsu, Y.Y.; Chen, C.R. )

    1991-12-01

    This paper reports on tuning of power system stabilizers (PSS) which is investigated using an artificial neural network (ANN). To have good damping characteristics over a wide range of operating conditions, it is desirable to adapt the PSS parameters in real-time based on generator loading conditions. To do this, a pair of on-line measurements, i.e. generator real power output (P) and power factor (PF), which are representative of generator operating condition, are chosen as the input signals to the neural net. The outputs of the neural net are the desired PSS parameters. The neural net, once trained by a set of input-output patterns in the training set, can yield proper PSS parameters under any generator loading condition. Digital simulations of a synchronous machine subject to a major disturbance of three-phase fault under different operating conditions are performed to demonstrate the effectiveness of the proposed neural network.

  13. Correcting wave predictions with artificial neural networks

    NASA Astrophysics Data System (ADS)

    Makarynskyy, O.; Makarynska, D.

    2003-04-01

    The predictions of wind waves with different lead times are necessary in a large scope of coastal and open ocean activities. Numerical wave models, which usually provide this information, are based on deterministic equations that do not entirely account for the complexity and uncertainty of the wave generation and dissipation processes. An attempt to improve wave parameters short-term forecasts based on artificial neural networks is reported. In recent years, artificial neural networks have been used in a number of coastal engineering applications due to their ability to approximate the nonlinear mathematical behavior without a priori knowledge of interrelations among the elements within a system. The common multilayer feed-forward networks, with a nonlinear transfer functions in the hidden layers, were developed and employed to forecast the wave characteristics over one hour intervals starting from one up to 24 hours, and to correct these predictions. Three non-overlapping data sets of wave characteristics, both from a buoy, moored roughly 60 miles west of the Aran Islands, west coast of Ireland, were used to train and validate the neural nets involved. The networks were trained with error back propagation algorithm. Time series plots and scatterplots of the wave characteristics as well as tables with statistics show an improvement of the results achieved due to the correction procedure employed.

  14. Devices and circuits for nanoelectronic implementation of artificial neural networks

    NASA Astrophysics Data System (ADS)

    Turel, Ozgur

    Biological neural networks perform complicated information processing tasks at speeds better than conventional computers based on conventional algorithms. This has inspired researchers to look into the way these networks function, and propose artificial networks that mimic their behavior. Unfortunately, most artificial neural networks, either software or hardware, do not provide either the speed or the complexity of a human brain. Nanoelectronics, with high density and low power dissipation that it provides, may be used in developing more efficient artificial neural networks. This work consists of two major contributions in this direction. First is the proposal of the CMOL concept, hybrid CMOS-molecular hardware [1-8]. CMOL may circumvent most of the problems in posed by molecular devices, such as low yield, vet provide high active device density, ˜1012/cm 2. The second contribution is CrossNets, artificial neural networks that are based on CMOL. We showed that CrossNets, with their fault tolerance, exceptional speed (˜ 4 to 6 orders of magnitude faster than biological neural networks) can perform any task any artificial neural network can perform. Moreover, there is a hope that if their integration scale is increased to that of human cerebral cortex (˜ 1010 neurons and ˜ 1014 synapses), they may be capable of performing more advanced tasks.

  15. Plant Growth Models Using Artificial Neural Networks

    NASA Technical Reports Server (NTRS)

    Bubenheim, David

    1997-01-01

    In this paper, we descrive our motivation and approach to devloping models and the neural network architecture. Initial use of the artificial neural network for modeling the single plant process of transpiration is presented.

  16. Overview of artificial neural networks.

    PubMed

    Zou, Jinming; Han, Yi; So, Sung-Sau

    2008-01-01

    The artificial neural network (ANN), or simply neural network, is a machine learning method evolved from the idea of simulating the human brain. The data explosion in modem drug discovery research requires sophisticated analysis methods to uncover the hidden causal relationships between single or multiple responses and a large set of properties. The ANN is one of many versatile tools to meet the demand in drug discovery modeling. Compared to a traditional regression approach, the ANN is capable of modeling complex nonlinear relationships. The ANN also has excellent fault tolerance and is fast and highly scalable with parallel processing. This chapter introduces the background of ANN development and outlines the basic concepts crucially important for understanding more sophisticated ANN. Several commonly used learning methods and network setups are discussed briefly at the end of the chapter. PMID:19065803

  17. Artificial neural networks in neurosurgery.

    PubMed

    Azimi, Parisa; Mohammadi, Hasan Reza; Benzel, Edward C; Shahzadi, Sohrab; Azhari, Shirzad; Montazeri, Ali

    2015-03-01

    Artificial neural networks (ANNs) effectively analyze non-linear data sets. The aimed was A review of the relevant published articles that focused on the application of ANNs as a tool for assisting clinical decision-making in neurosurgery. A literature review of all full publications in English biomedical journals (1993-2013) was undertaken. The strategy included a combination of key words 'artificial neural networks', 'prognostic', 'brain', 'tumor tracking', 'head', 'tumor', 'spine', 'classification' and 'back pain' in the title and abstract of the manuscripts using the PubMed search engine. The major findings are summarized, with a focus on the application of ANNs for diagnostic and prognostic purposes. Finally, the future of ANNs in neurosurgery is explored. A total of 1093 citations were identified and screened. In all, 57 citations were found to be relevant. Of these, 50 articles were eligible for inclusion in this review. The synthesis of the data showed several applications of ANN in neurosurgery, including: (1) diagnosis and assessment of disease progression in low back pain, brain tumours and primary epilepsy; (2) enhancing clinically relevant information extraction from radiographic images, intracranial pressure processing, low back pain and real-time tumour tracking; (3) outcome prediction in epilepsy, brain metastases, lumbar spinal stenosis, lumbar disc herniation, childhood hydrocephalus, trauma mortality, and the occurrence of symptomatic cerebral vasospasm in patients with aneurysmal subarachnoid haemorrhage; (4) the use in the biomechanical assessments of spinal disease. ANNs can be effectively employed for diagnosis, prognosis and outcome prediction in neurosurgery. PMID:24987050

  18. Accelerator diagnosis and control by Neural Nets

    SciTech Connect

    Spencer, J.E.

    1989-01-01

    Neural Nets (NN) have been described as a solution looking for a problem. In the last conference, Artificial Intelligence (AI) was considered in the accelerator context. While good for local surveillance and control, its use for large complex systems (LCS) was much more restricted. By contrast, NN provide a good metaphor for LCS. It can be argued that they are logically equivalent to multi-loop feedback/forward control of faulty systems, and therefore provide an ideal adaptive control system. Thus, where AI may be good for maintaining a 'golden orbit,' NN should be good for obtaining it via a quantitative approach to 'look and adjust' methods like operator tweaking which use pattern recognition to deal with hardware and software limitations, inaccuracies or errors as well as imprecise knowledge or understanding of effects like annealing and hysteresis. Further, insights from NN allow one to define feasibility conditions for LCS in terms of design constraints and tolerances. Hardware and software implications are discussed and several LCS of current interest are compared and contrasted. 15 refs., 5 figs.

  19. Modular, Hierarchical Learning By Artificial Neural Networks

    NASA Technical Reports Server (NTRS)

    Baldi, Pierre F.; Toomarian, Nikzad

    1996-01-01

    Modular and hierarchical approach to supervised learning by artificial neural networks leads to neural networks more structured than neural networks in which all neurons fully interconnected. These networks utilize general feedforward flow of information and sparse recurrent connections to achieve dynamical effects. The modular organization, sparsity of modular units and connections, and fact that learning is much more circumscribed are all attractive features for designing neural-network hardware. Learning streamlined by imitating some aspects of biological neural networks.

  20. Creativity in design and artificial neural networks

    SciTech Connect

    Neocleous, C.C.; Esat, I.I.; Schizas, C.N.

    1996-12-31

    The creativity phase is identified as an integral part of the design phase. The characteristics of creative persons which are relevant to designing artificial neural networks manifesting aspects of creativity, are identified. Based on these identifications, a general framework of artificial neural network characteristics to implement such a goal are proposed.

  1. Introduction to artificial neural networks.

    PubMed

    Grossi, Enzo; Buscema, Massimo

    2007-12-01

    The coupling of computer science and theoretical bases such as nonlinear dynamics and chaos theory allows the creation of 'intelligent' agents, such as artificial neural networks (ANNs), able to adapt themselves dynamically to problems of high complexity. ANNs are able to reproduce the dynamic interaction of multiple factors simultaneously, allowing the study of complexity; they can also draw conclusions on individual basis and not as average trends. These tools can offer specific advantages with respect to classical statistical techniques. This article is designed to acquaint gastroenterologists with concepts and paradigms related to ANNs. The family of ANNs, when appropriately selected and used, permits the maximization of what can be derived from available data and from complex, dynamic, and multidimensional phenomena, which are often poorly predictable in the traditional 'cause and effect' philosophy. PMID:17998827

  2. The power of neural nets

    NASA Technical Reports Server (NTRS)

    Ryan, J. P.; Shah, B. H.

    1987-01-01

    Implementation of the Hopfield net which is used in the image processing type of applications where only partial information about the image may be available is discussed. The image classification type of algorithm of Hopfield and other learning algorithms, such as the Boltzmann machine and the back-propagation training algorithm, have many vital applications in space.

  3. Analysis of torsional oscillations using an artificial neural network

    SciTech Connect

    Hsu, Y.Y.; Jeng, L,H. )

    1992-12-01

    In this paper, a novel approach using an artificial neural network (ANN) is proposed for the analysis of torsional oscillations in a power system. In the developed artificial neural network, those system variables such as generator loadings and capacitor compensation ratio which have major impacts on the damping characteristics of torsional oscillatio modes are employed as the inputs. The outputs of the neural net provide the desired eigenvalues for torsional modes. Once the connection weights of the neural network have been learned using a set of training data derived off-line, the neural network can be applied to torsional analysis in real-time situations. To demonstrate the effectiveness of the proposed neural net, torsional analysis is performed on the IEEE First Benchmark Model. It is concluded from the test results that accurate assessment of the torsional mode eigenvalues can be achieved by the neural network in a very efficient manner. Thereofore, the proposed neural network approach can serve as a valuable tool to system operators in conducting SSR analysis in operational planning.

  4. Learning in Artificial Neural Systems

    NASA Technical Reports Server (NTRS)

    Matheus, Christopher J.; Hohensee, William E.

    1987-01-01

    This paper presents an overview and analysis of learning in Artificial Neural Systems (ANS's). It begins with a general introduction to neural networks and connectionist approaches to information processing. The basis for learning in ANS's is then described, and compared with classical Machine learning. While similar in some ways, ANS learning deviates from tradition in its dependence on the modification of individual weights to bring about changes in a knowledge representation distributed across connections in a network. This unique form of learning is analyzed from two aspects: the selection of an appropriate network architecture for representing the problem, and the choice of a suitable learning rule capable of reproducing the desired function within the given network. The various network architectures are classified, and then identified with explicit restrictions on the types of functions they are capable of representing. The learning rules, i.e., algorithms that specify how the network weights are modified, are similarly taxonomized, and where possible, the limitations inherent to specific classes of rules are outlined.

  5. Detection of Structural Abnormalities Using Neural Nets

    NASA Technical Reports Server (NTRS)

    Zak, M.; Maccalla, A.; Daggumati, V.; Gulati, S.; Toomarian, N.

    1996-01-01

    This paper describes a feed-forward neural net approach for detection of abnormal system behavior based upon sensor data analyses. A new dynamical invariant representing structural parameters of the system is introduced in such a way that any structural abnormalities in the system behavior are detected from the corresponding changes to the invariant.

  6. The use of artificial neural networks in experimental data acquisition and aerodynamic design

    NASA Technical Reports Server (NTRS)

    Meade, Andrew J., Jr.

    1991-01-01

    It is proposed that an artificial neural network be used to construct an intelligent data acquisition system. The artificial neural networks (ANN) model has a potential for replacing traditional procedures as well as for use in computational fluid dynamics validation. Potential advantages of the ANN model are listed. As a proof of concept, the author modeled a NACA 0012 airfoil at specific conditions, using the neural network simulator NETS, developed by James Baffes of the NASA Johnson Space Center. The neural network predictions were compared to the actual data. It is concluded that artificial neural networks can provide an elegant and valuable class of mathematical tools for data analysis.

  7. Neural-net Processed Electronic Holography for Rotating Machines

    NASA Technical Reports Server (NTRS)

    Decker, Arthur J.

    2003-01-01

    This report presents the results of an R&D effort to apply neural-net processed electronic holography to NDE of rotors. Electronic holography was used to generate characteristic patterns or mode shapes of vibrating rotors and rotor components. Artificial neural networks were trained to identify damage-induced changes in the characteristic patterns. The development and optimization of a neural-net training method were the most significant contributions of this work, and the training method and its optimization are discussed in detail. A second positive result was the assembly and testing of a fiber-optic holocamera. A major disappointment was the inadequacy of the high-speed-holography hardware selected for this effort, but the use of scaled holograms to match the low effective resolution of an image intensifier was one interesting attempt to compensate. This report also discusses in some detail the physics and environmental requirements for rotor electronic holography. The major conclusions were that neural-net and electronic-holography inspections of stationary components in the laboratory and the field are quite practical and worthy of continuing development, but that electronic holography of moving rotors is still an expensive high-risk endeavor.

  8. A mixture neural net for multispectral imaging spectrometer processing

    NASA Technical Reports Server (NTRS)

    Casasent, David; Slagle, Timothy

    1990-01-01

    Each spatial region viewed by an imaging spectrometer contains various elements in a mixture. The elements present and the amount of each are to be determined. A neural net solution is considered. Initial optical neural net hardware is described. The first simulations on the component requirements of a neural net are considered. The pseudoinverse solution is shown to not suffice, i.e. a neural net solution is required.

  9. Artificial Neural Networks and Instructional Technology.

    ERIC Educational Resources Information Center

    Carlson, Patricia A.

    1991-01-01

    Artificial neural networks (ANN), part of artificial intelligence, are discussed. Such networks are fed sample cases (training sets), learn how to recognize patterns in the sample data, and use this experience in handling new cases. Two cognitive roles for ANNs (intelligent filters and spreading, associative memories) are examined. Prototypes…

  10. Artificial Astrocytes Improve Neural Network Performance

    PubMed Central

    Porto-Pazos, Ana B.; Veiguela, Noha; Mesejo, Pablo; Navarrete, Marta; Alvarellos, Alberto; Ibáñez, Oscar; Pazos, Alejandro; Araque, Alfonso

    2011-01-01

    Compelling evidence indicates the existence of bidirectional communication between astrocytes and neurons. Astrocytes, a type of glial cells classically considered to be passive supportive cells, have been recently demonstrated to be actively involved in the processing and regulation of synaptic information, suggesting that brain function arises from the activity of neuron-glia networks. However, the actual impact of astrocytes in neural network function is largely unknown and its application in artificial intelligence remains untested. We have investigated the consequences of including artificial astrocytes, which present the biologically defined properties involved in astrocyte-neuron communication, on artificial neural network performance. Using connectionist systems and evolutionary algorithms, we have compared the performance of artificial neural networks (NN) and artificial neuron-glia networks (NGN) to solve classification problems. We show that the degree of success of NGN is superior to NN. Analysis of performances of NN with different number of neurons or different architectures indicate that the effects of NGN cannot be accounted for an increased number of network elements, but rather they are specifically due to astrocytes. Furthermore, the relative efficacy of NGN vs. NN increases as the complexity of the network increases. These results indicate that artificial astrocytes improve neural network performance, and established the concept of Artificial Neuron-Glia Networks, which represents a novel concept in Artificial Intelligence with implications in computational science as well as in the understanding of brain function. PMID:21526157

  11. Artificial astrocytes improve neural network performance.

    PubMed

    Porto-Pazos, Ana B; Veiguela, Noha; Mesejo, Pablo; Navarrete, Marta; Alvarellos, Alberto; Ibáñez, Oscar; Pazos, Alejandro; Araque, Alfonso

    2011-01-01

    Compelling evidence indicates the existence of bidirectional communication between astrocytes and neurons. Astrocytes, a type of glial cells classically considered to be passive supportive cells, have been recently demonstrated to be actively involved in the processing and regulation of synaptic information, suggesting that brain function arises from the activity of neuron-glia networks. However, the actual impact of astrocytes in neural network function is largely unknown and its application in artificial intelligence remains untested. We have investigated the consequences of including artificial astrocytes, which present the biologically defined properties involved in astrocyte-neuron communication, on artificial neural network performance. Using connectionist systems and evolutionary algorithms, we have compared the performance of artificial neural networks (NN) and artificial neuron-glia networks (NGN) to solve classification problems. We show that the degree of success of NGN is superior to NN. Analysis of performances of NN with different number of neurons or different architectures indicate that the effects of NGN cannot be accounted for an increased number of network elements, but rather they are specifically due to astrocytes. Furthermore, the relative efficacy of NGN vs. NN increases as the complexity of the network increases. These results indicate that artificial astrocytes improve neural network performance, and established the concept of Artificial Neuron-Glia Networks, which represents a novel concept in Artificial Intelligence with implications in computational science as well as in the understanding of brain function. PMID:21526157

  12. Neural net forecasting for geomagnetic activity

    NASA Technical Reports Server (NTRS)

    Hernandez, J. V.; Tajima, T.; Horton, W.

    1993-01-01

    We use neural nets to construct nonlinear models to forecast the AL index given solar wind and interplanetary magnetic field (IMF) data. We follow two approaches: (1) the state space reconstruction approach, which is a nonlinear generalization of autoregressive-moving average models (ARMA) and (2) the nonlinear filter approach, which reduces to a moving average model (MA) in the linear limit. The database used here is that of Bargatze et al. (1985).

  13. Neural Net Gains Estimation Based on an Equivalent Model

    PubMed Central

    Aguilar Cruz, Karen Alicia; Medel Juárez, José de Jesús; Fernández Muñoz, José Luis; Esmeralda Vigueras Velázquez, Midory

    2016-01-01

    A model of an Equivalent Artificial Neural Net (EANN) describes the gains set, viewed as parameters in a layer, and this consideration is a reproducible process, applicable to a neuron in a neural net (NN). The EANN helps to estimate the NN gains or parameters, so we propose two methods to determine them. The first considers a fuzzy inference combined with the traditional Kalman filter, obtaining the equivalent model and estimating in a fuzzy sense the gains matrix A and the proper gain K into the traditional filter identification. The second develops a direct estimation in state space, describing an EANN using the expected value and the recursive description of the gains estimation. Finally, a comparison of both descriptions is performed; highlighting the analytical method describes the neural net coefficients in a direct form, whereas the other technique requires selecting into the Knowledge Base (KB) the factors based on the functional error and the reference signal built with the past information of the system. PMID:27366146

  14. Neural Net Gains Estimation Based on an Equivalent Model.

    PubMed

    Aguilar Cruz, Karen Alicia; Medel Juárez, José de Jesús; Fernández Muñoz, José Luis; Esmeralda Vigueras Velázquez, Midory

    2016-01-01

    A model of an Equivalent Artificial Neural Net (EANN) describes the gains set, viewed as parameters in a layer, and this consideration is a reproducible process, applicable to a neuron in a neural net (NN). The EANN helps to estimate the NN gains or parameters, so we propose two methods to determine them. The first considers a fuzzy inference combined with the traditional Kalman filter, obtaining the equivalent model and estimating in a fuzzy sense the gains matrix A and the proper gain K into the traditional filter identification. The second develops a direct estimation in state space, describing an EANN using the expected value and the recursive description of the gains estimation. Finally, a comparison of both descriptions is performed; highlighting the analytical method describes the neural net coefficients in a direct form, whereas the other technique requires selecting into the Knowledge Base (KB) the factors based on the functional error and the reference signal built with the past information of the system. PMID:27366146

  15. Load forecasting using artificial neural networks

    SciTech Connect

    Pham, K.D.

    1995-12-31

    Artificial neural networks, modeled after their biological counterpart, have been successfully applied in many diverse areas including speech and pattern recognition, remote sensing, electrical power engineering, robotics and stock market forecasting. The most commonly used neural networks are those that gained knowledge from experience. Experience is presented to the network in form of the training data. Once trained, the neural network can recognized data that it has not seen before. This paper will present a fundamental introduction to the manner in which neural networks work and how to use them in load forecasting.

  16. An artificial neural network for wavelet steganalysis

    NASA Astrophysics Data System (ADS)

    Davidson, Jennifer; Bergman, Clifford; Bartlett, Eric

    2005-08-01

    Hiding messages in image data, called steganography, is used for both legal and illicit purposes. The detection of hidden messages in image data stored on websites and computers, called steganalysis, is of prime importance to cyber forensics personnel. Automating the detection of hidden messages is a requirement, since the shear amount of image data stored on computers or websites makes it impossible for a person to investigate each image separately. This paper describes research on a prototype software system that automatically classifies an image as having hidden information or not, using a sophisticated artificial neural network (ANN) system. An ANN software package, the ISU ACL NetWorks Toolkit, is trained on a selection of image features that distinguish between stego and nonstego images. The novelty of this ANN is that it is a blind classifier that gives more accurate results than previous systems. It can detect messages hidden using a variety of different types of embedding algorithms. A Graphical User Interface (GUI) combines the ANN, feature selection, and embedding algorithms into a prototype software package that is not currently available to the cyber forensics community.

  17. Document analysis with neural net circuits

    NASA Technical Reports Server (NTRS)

    Graf, Hans Peter

    1994-01-01

    Document analysis is one of the main applications of machine vision today and offers great opportunities for neural net circuits. Despite more and more data processing with computers, the number of paper documents is still increasing rapidly. A fast translation of data from paper into electronic format is needed almost everywhere, and when done manually, this is a time consuming process. Markets range from small scanners for personal use to high-volume document analysis systems, such as address readers for the postal service or check processing systems for banks. A major concern with present systems is the accuracy of the automatic interpretation. Today's algorithms fail miserably when noise is present, when print quality is poor, or when the layout is complex. A common approach to circumvent these problems is to restrict the variations of the documents handled by a system. In our laboratory, we had the best luck with circuits implementing basic functions, such as convolutions, that can be used in many different algorithms. To illustrate the flexibility of this approach, three applications of the NET32K circuit are described in this short viewgraph presentation: locating address blocks, cleaning document images by removing noise, and locating areas of interest in personal checks to improve image compression. Several of the ideas realized in this circuit that were inspired by neural nets, such as analog computation with a low resolution, resulted in a chip that is well suited for real-world document analysis applications and that compares favorably with alternative, 'conventional' circuits.

  18. Optimization of Training Sets for Neural-Net Processing of Characteristic Patterns from Vibrating Solids

    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.

  19. An Artificial Neural Networks Method for Solving Partial Differential Equations

    NASA Astrophysics Data System (ADS)

    Alharbi, Abir

    2010-09-01

    While there already exists many analytical and numerical techniques for solving PDEs, this paper introduces an approach using artificial neural networks. The approach consists of a technique developed by combining the standard numerical method, finite-difference, with the Hopfield neural network. The method is denoted Hopfield-finite-difference (HFD). The architecture of the nets, energy function, updating equations, and algorithms are developed for the method. The HFD method has been used successfully to approximate the solution of classical PDEs, such as the Wave, Heat, Poisson and the Diffusion equations, and on a system of PDEs. The software Matlab is used to obtain the results in both tabular and graphical form. The results are similar in terms of accuracy to those obtained by standard numerical methods. In terms of speed, the parallel nature of the Hopfield nets methods makes them easier to implement on fast parallel computers while some numerical methods need extra effort for parallelization.

  20. Data compression using artificial neural networks

    SciTech Connect

    Watkins, B.E.

    1991-09-01

    This thesis investigates the application of artificial neural networks for the compression of image data. An algorithm is developed using the competitive learning paradigm which takes advantage of the parallel processing and classification capability of neural networks to produce an efficient implementation of vector quantization. Multi-Stage, tree searched, and classification vector quantization codebook design are adapted to the neural network design to reduce the computational cost and hardware requirements. The results show that the new algorithm provides a substantial reduction in computational costs and an improvement in performance.

  1. Optical neural nets for scene analysis

    NASA Astrophysics Data System (ADS)

    Casasent, David

    1991-04-01

    This project involves hybrid optical/digital neural nets (NNs) with attention to one of the most formidable NN problems: scene analysis and pattern recognition. Our research is unique in its attention to a hybrid optical/digital NN architecture that is very multifunctional. We describe the various novel uses for optics we employ within a NN and how our hybrid architecture can implement most major NNs (specifically: associative processors, optimization NNs, NNs to handle multiple objects in the field of view, and adaptive learning NNs). We also include new matrix inversion NN concepts. Our scene analysis algorithm work includes a new feature space, a new hybrid pattern recognition/neural net algorithm (the ACNNO, our symbolic correlator production system NN (that handles multiple objects in the field of view in parallel), and an advanced piecewise quadratic NN (PQNN) concept. Our major thrust has been the optical laboratory realization of these NN algorithms. Our initial work in this area is noted and includes: new error source modeling simulations of our initial and new real time optical laboratory system, a description of our newest optical laboratory system, and initial test results obtained with it.

  2. Comparing artificial and biological dynamical neural networks

    NASA Astrophysics Data System (ADS)

    McAulay, Alastair D.

    2006-05-01

    Modern computers can be made more friendly and otherwise improved by making them behave more like humans. Perhaps we can learn how to do this from biology in which human brains evolved over a long period of time. Therefore, we first explain a commonly used biological neural network (BNN) model, the Wilson-Cowan neural oscillator, that has cross-coupled excitatory (positive) and inhibitory (negative) neurons. The two types of neurons are used for frequency modulation communication between neurons which provides immunity to electromagnetic interference. We then evolve, for the first time, an artificial neural network (ANN) to perform the same task. Two dynamical feed-forward artificial neural networks use cross-coupling feedback (like that in a flip-flop) to form an ANN nonlinear dynamic neural oscillator with the same equations as the Wilson-Cowan neural oscillator. Finally we show, through simulation, that the equations perform the basic neural threshold function, switching between stable zero output and a stable oscillation, that is a stable limit cycle. Optical implementation with an injected laser diode and future research are discussed.

  3. Speech synthesis with artificial neural networks

    NASA Astrophysics Data System (ADS)

    Weijters, Ton; Thole, Johan

    1992-10-01

    The application of neural nets to speech synthesis is considered. In speech synthesis, the main efforts so far have been to master the grapheme to phoneme conversion. During this conversion symbols (graphemes) are converted into other symbols (phonemes). Neural networks, however, are especially competitive for tasks in which complex nonlinear transformations are needed and sufficient domain specific knowledge is not available. The conversion of text into speech parameters appropriate as input for a speech generator seems such a task. Results of a pilot study in which an attempt is made to train a neural network for this conversion are presented.

  4. Psychometric Measurement Models and Artificial Neural Networks

    ERIC Educational Resources Information Center

    Sese, Albert; Palmer, Alfonso L.; Montano, Juan J.

    2004-01-01

    The study of measurement models in psychometrics by means of dimensionality reduction techniques such as Principal Components Analysis (PCA) is a very common practice. In recent times, an upsurge of interest in the study of artificial neural networks apt to computing a principal component extraction has been observed. Despite this interest, the…

  5. Web traffic prediction with artificial neural networks

    NASA Astrophysics Data System (ADS)

    Gluszek, Adam; Kekez, Michal; Rudzinski, Filip

    2005-02-01

    The main aim of the paper is to present application of the artificial neural network in the web traffic prediction. First, the general problem of time series modelling and forecasting is shortly described. Next, the details of building of dynamic processes models with the neural networks are discussed. At this point determination of the model structure in terms of its inputs and outputs is the most important question because this structure is a rough approximation of the dynamics of the modelled process. The following section of the paper presents the results obtained applying artificial neural network (classical multilayer perceptron trained with backpropagation algorithm) to the real-world web traffic prediction. Finally, we discuss the results, describe weak points of presented method and propose some alternative approaches.

  6. [Application of artificial neural networks in infectious diseases].

    PubMed

    Xu, Jun-fang; Zhou, Xiao-nong

    2011-02-28

    With the development of information technology, artificial neural networks has been applied to many research fields. Due to the special features such as nonlinearity, self-adaptation, and parallel processing, artificial neural networks are applied in medicine and biology. This review summarizes the application of artificial neural networks in the relative factors, prediction and diagnosis of infectious diseases in recent years. PMID:21823326

  7. Predicting permeability from porosity using artificial neural networks

    SciTech Connect

    Rogers, S.J.; Fang, J.H.; Chen, H.C. Kopaska-Merkel, D.C.

    1995-12-01

    Permeability values in a borehole are predicted by an artificial neural network from the porosity values at the same depths. THe network used in this study employs an architecture called backpropagation that is good at making predictions. The traditional approach for permeability prediction is regression analysis, the relationship between porosity and permeability is assumed to be known. In reality, the functional form of this relationship, i.e., the model equation, is unknown. In contrast, the neural-network approach assumes no functional relationship. Six wells from Big Escambia Creek (Jurassic Smackover carbonate) field in southern Alabama were used to test predicting permeability from porosity using a neural network. Porosity and spatial data alone were used to predict permeability because these data are readily available from any hydrocarbon field. Three scenarios were performed; in each one, a subset of the six wells was used for a training set, one well for calibration, and one or two wells were used for prediction. For each scenario, simple linear regression was also used to predict permeability from porosity. The neural net predicted permeability much better than did regression in one scenario; in the other two scenarios the two methods performed equally well. The neural net predicted permeability accurately using minimal data, but other kinds of information (e.g., log- or core-derived lithologic information) are easily incorporated if available. In addition, compartmentalization of carbonate reservoirs may be recognizable by this approach.

  8. Artificial neural network simulation of battery performance

    SciTech Connect

    O`Gorman, C.C.; Ingersoll, D.; Jungst, R.G.; Paez, T.L.

    1998-12-31

    Although they appear deceptively simple, batteries embody a complex set of interacting physical and chemical processes. While the discrete engineering characteristics of a battery such as the physical dimensions of the individual components, are relatively straightforward to define explicitly, their myriad chemical and physical processes, including interactions, are much more difficult to accurately represent. Within this category are the diffusive and solubility characteristics of individual species, reaction kinetics and mechanisms of primary chemical species as well as intermediates, and growth and morphology characteristics of reaction products as influenced by environmental and operational use profiles. For this reason, development of analytical models that can consistently predict the performance of a battery has only been partially successful, even though significant resources have been applied to this problem. As an alternative approach, the authors have begun development of a non-phenomenological model for battery systems based on artificial neural networks. Both recurrent and non-recurrent forms of these networks have been successfully used to develop accurate representations of battery behavior. The connectionist normalized linear spline (CMLS) network has been implemented with a self-organizing layer to model a battery system with the generalized radial basis function net. Concurrently, efforts are under way to use the feedforward back propagation network to map the {open_quotes}state{close_quotes} of a battery system. Because of the complexity of battery systems, accurate representation of the input and output parameters has proven to be very important. This paper describes these initial feasibility studies as well as the current models and makes comparisons between predicted and actual performance.

  9. Neural-net based real-time economic dispatch for thermal power plants

    SciTech Connect

    Djukanovic, M.; Milosevic, B.; Calovic, M.; Sobajic, D.J.

    1996-12-01

    This paper proposes the application of artificial neural networks to real-time optimal generation dispatch of thermal units. The approach can take into account the operational requirements and network losses. The proposed economic dispatch uses an artificial neural network (ANN) for generation of penalty factors, depending on the input generator powers and identified system load change. Then, a few additional iterations are performed within an iterative computation procedure for the solution of coordination equations, by using reference-bus penalty-factors derived from the Newton-Raphson load flow. A coordination technique for environmental and economic dispatch of pure thermal systems, based on the neural-net theory for simplified solution algorithms and improved man-machine interface is introduced. Numerical results on two test examples show that the proposed algorithm can efficiently and accurately develop optimal and feasible generator output trajectories, by applying neural-net forecasts of system load patterns.

  10. Computer-assisted diagnosis of lung nodule detection using artificial convoultion neural network

    NASA Astrophysics Data System (ADS)

    Lo, Shih-Chung B.; Lin, Jyh-Shyan; Freedman, Matthew T.; Mun, Seong K.

    1993-09-01

    Several fuzzy assignment methods for the output association with convolution neural network are proposed for general medical image pattern recognition. A non-conventional method of using rotation and shift invariance is also proposed to enhance the neural net performance. These methods in conjunction with the convolution neural network technique are generally applicable to the recognition of medical disease patterns in gray scale imaging. The structure of the artificial neural network is a simplified network structure of neocognitron. Two- dimensional local connection as a group is the fundamental architecture for the signal propagation in the convolution (vision type) neural network. Weighting coefficients of convolution kernels are formed by neural network through backpropagated training for this artificial neural net. In addition, radiologists' reading procedure was modeled in order to instruct the artificial neural network to recognize the pre-defined image patterns and those of interest to experts. We have tested this method for lung nodule detection. The performance studies have shown the potential use of this technique in a clinical environment. Our computer program uses a sphere profile double-matching technique for initial nodule search. We set searching parameters in a highly sensitive level to identify all potential disease areas. The artificial convolution neural network acts as a final detection classifier to determine if a disease pattern is shown on the suspected image area. The total processing time for the automatic detection of lung nodules using both pre-scan and convolution neural network evaluation is about 10 seconds in a DEC Alpha workstation.

  11. Extraction of shoreline features by neural nets and image processing

    SciTech Connect

    Ryan, T.W.; Sementilli, P.J.; Yuen, P.; Hunt, B.R. )

    1991-07-01

    This paper demonstrates the capability of using neural networks as a tool for delineation of shorelines. The neural nets used are multilayer perceptrons, i.e., feed-forward nets with one or more layers of nodes between the input and output nodes. The back-propagation learning algorithm is used as the adaptation rule. 24 refs.

  12. Development of programmable artificial neural networks

    NASA Technical Reports Server (NTRS)

    Meade, Andrew J.

    1993-01-01

    Conventionally programmed digital computers can process numbers with great speed and precision, but do not easily recognize patterns or imprecise or contradictory data. Instead of being programmed in the conventional sense, artificial neural networks are capable of self-learning through exposure to repeated examples. However, the training of an ANN can be a time consuming and unpredictable process. A general method is being developed to mate the adaptability of the ANN with the speed and precision of the digital computer. This method was successful in building feedforward networks that can approximate functions and their partial derivatives from examples in a single iteration. The general method also allows the formation of feedforward networks that can approximate the solution to nonlinear ordinary and partial differential equations to desired accuracy without the need of examples. It is believed that continued research will produce artificial neural networks that can be used with confidence in practical scientific computing and engineering applications.

  13. Artificial neural network cardiopulmonary modeling and diagnosis

    DOEpatents

    Kangas, Lars J.; Keller, Paul E.

    1997-01-01

    The present invention is a method of diagnosing a cardiopulmonary condition in an individual by comparing data from a progressive multi-stage test for the individual to a non-linear multi-variate model, preferably a recurrent artificial neural network having sensor fusion. The present invention relies on a cardiovascular model developed from physiological measurements of an individual. Any differences between the modeled parameters and the parameters of an individual at a given time are used for diagnosis.

  14. Artificial neural network cardiopulmonary modeling and diagnosis

    DOEpatents

    Kangas, L.J.; Keller, P.E.

    1997-10-28

    The present invention is a method of diagnosing a cardiopulmonary condition in an individual by comparing data from a progressive multi-stage test for the individual to a non-linear multi-variate model, preferably a recurrent artificial neural network having sensor fusion. The present invention relies on a cardiovascular model developed from physiological measurements of an individual. Any differences between the modeled parameters and the parameters of an individual at a given time are used for diagnosis. 12 figs.

  15. Artificial neural networks for small dataset analysis.

    PubMed

    Pasini, Antonello

    2015-05-01

    Artificial neural networks (ANNs) are usually considered as tools which can help to analyze cause-effect relationships in complex systems within a big-data framework. On the other hand, health sciences undergo complexity more than any other scientific discipline, and in this field large datasets are seldom available. In this situation, I show how a particular neural network tool, which is able to handle small datasets of experimental or observational data, can help in identifying the main causal factors leading to changes in some variable which summarizes the behaviour of a complex system, for instance the onset of a disease. A detailed description of the neural network tool is given and its application to a specific case study is shown. Recommendations for a correct use of this tool are also supplied. PMID:26101654

  16. Artificial neural networks for small dataset analysis

    PubMed Central

    2015-01-01

    Artificial neural networks (ANNs) are usually considered as tools which can help to analyze cause-effect relationships in complex systems within a big-data framework. On the other hand, health sciences undergo complexity more than any other scientific discipline, and in this field large datasets are seldom available. In this situation, I show how a particular neural network tool, which is able to handle small datasets of experimental or observational data, can help in identifying the main causal factors leading to changes in some variable which summarizes the behaviour of a complex system, for instance the onset of a disease. A detailed description of the neural network tool is given and its application to a specific case study is shown. Recommendations for a correct use of this tool are also supplied. PMID:26101654

  17. Real-time applications of neural nets

    SciTech Connect

    Spencer, J.E. )

    1989-10-01

    Producing, accelerating and colliding very high power, low emittance beams for long periods is a formidable problem in real-time control. As energy has grown exponentially in time so has the complexity of the machines and their control systems. Similar growth rates have occurred in many areas e.g. improved integrated circuits have been paid for with comparable increases in complexity. However, in this case, reliability, capability and cost have improved due to reduced size, high production and increased integration which allow various kinds of feedback. In contrast, most large complex systems (LCS) are perceived to lack such possibilities because only one copy is made. Neural nets, as a metaphor for LCS, suggest ways to circumvent such limitations. It is argued that they are logically equivalent to multi-loop feedback/forward control of faulty systems. While complimentary to AI, they mesh nicely with characteristics desired for real-time systems. In this paper, such issues are considered, examples given and possibilities discussed.

  18. Real-time applications of neural nets

    SciTech Connect

    Spencer, J.E.

    1989-05-01

    Producing, accelerating and colliding very high power, low emittance beams for long periods is a formidable problem in real-time control. As energy has grown exponentially in time so has the complexity of the machines and their control systems. Similar growth rates have occurred in many areas, e.g., improved integrated circuits have been paid for with comparable increases in complexity. However, in this case, reliability, capability and cost have improved due to reduced size, high production and increased integration which allow various kinds of feedback. In contrast, most large complex systems (LCS) are perceived to lack such possibilities because only one copy is made. Neural nets, as a metaphor for LCS, suggest ways to circumvent such limitations. It is argued that they are logically equivalent to multi-loop feedback/forward control of faulty systems. While complimentary to AI, they mesh nicely with characteristics desired for real-time systems. Such issues are considered, examples given and possibilities discussed. 21 refs., 6 figs.

  19. Proceedings of intelligent engineering systems through artificial neural networks

    SciTech Connect

    Dagli, C.H. . Dept. of Engineering Management); Kumara, S.R. . Dept. of Industrial Management Systems Engineering); Shin, Y.C. . School of Mechanical Engineering)

    1991-01-01

    This book contains the edited versions of the technical presentation of ANNIE '91, the first international meeting on Artificial Neural Networks in Engineering. The conference covered the theory of Artificial Neural Networks and its contributions in the engineering domain and attracted researchers from twelve countries. The papers in this edited book are grouped into four categories: Artificial Neural Network Architectures; Pattern Recognition; Adaptive Control, Diagnosis and Process Monitoring; and Neuro-Engineering Systems.

  20. Artificial convolution neural network techniques and applications for lung nodule detection.

    PubMed

    Lo, S B; Lou, S A; Lin, J S; Freedman, M T; Chien, M V; Mun, S K

    1995-01-01

    We have developed a double-matching method and an artificial visual neural network technique for lung nodule detection. This neural network technique is generally applicable to the recognition of medical image pattern in gray scale imaging. The structure of the artificial neural net is a simplified network structure of human vision. The fundamental operation of the artificial neural network is local two-dimensional convolution rather than full connection with weighted multiplication. Weighting coefficients of the convolution kernels are formed by the neural network through backpropagated training. In addition, we modeled radiologists' reading procedures in order to instruct the artificial neural network to recognize the image patterns predefined and those of interest to experts in radiology. We have tested this method for lung nodule detection. The performance studies have shown the potential use of this technique in a clinical setting. This program first performed an initial nodule search with high sensitivity in detecting round objects using a sphere template double-matching technique. The artificial convolution neural network acted as a final classifier to determine whether the suspected image block contains a lung nodule. The total processing time for the automatic detection of lung nodules using both prescan and convolution neural network evaluation was about 15 seconds in a DEC Alpha workstation. PMID:18215875

  1. A Novel Higher Order Artificial Neural Networks

    NASA Astrophysics Data System (ADS)

    Xu, Shuxiang

    2010-05-01

    In this paper a new Higher Order Neural Network (HONN) model is introduced and applied in several data mining tasks. Data Mining extracts hidden patterns and valuable information from large databases. A hyperbolic tangent function is used as the neuron activation function for the new HONN model. Experiments are conducted to demonstrate the advantages and disadvantages of the new HONN model, when compared with several conventional Artificial Neural Network (ANN) models: Feedforward ANN with the sigmoid activation function; Feedforward ANN with the hyperbolic tangent activation function; and Radial Basis Function (RBF) ANN with the Gaussian activation function. The experimental results seem to suggest that the new HONN holds higher generalization capability as well as abilities in handling missing data.

  2. Synchronous machine steady-state stability analysis using an artificial neural network

    SciTech Connect

    Chen, C.R.; Hsu, Y.Y. . Dept. of Electrical Engineering)

    1991-03-01

    A new type of artificial neural network is proposed for the steady-state stability analysis of a synchronous generator. In the developed artificial neutral network, those system variables which play an important role in steady-state stability such as generator outputs and power system stabilizer parameters are employed as the inputs. The output of the neural net provides the information on steady-state stability. Once the connection weights of the neural network have been learned using a set of training data derived off-line, the neural net can be applied to analyze the steady-state stability of the system time. To demonstrate the effectiveness of the proposed neural net, steady-state stability analysis is performed on a synchronous generator connected to a large power system. It is found that the proposed neural net requires much less training time than the multilayer feedforward network with backpropagation-momentum learning algorithm. It is also concluded from the test results that correct stability assessment can be achieved by the neural network.

  3. Artificial neural networks and Abelian harmonic analysis

    NASA Astrophysics Data System (ADS)

    Rodriguez, Domingo; Pertuz-Campo, Jairo

    1991-12-01

    This work deals with the use of artificial neural networks (ANN) for the digital processing of finite discrete time signals. The effort concentrates on the efficient replacement of fast Fourier transform (FFT) algorithms with ANN algorithms in certain engineering and scientific applications. The FFT algorithms are efficient methods of computing the discrete Fourier transform (DFT). The ubiquitous DFT is utilized in almost every digital signal processing application where harmonic analysis information is needed. Applications abound in areas such as audio acoustics, geophysics, biomedicine, telecommunications, astrophysics, etc. To identify more efficient methods to obtain a desired spectral information will result in a reduction in the computational effort required to implement these applications.

  4. Digital Image Compression Using Artificial Neural Networks

    NASA Technical Reports Server (NTRS)

    Serra-Ricart, M.; Garrido, L.; Gaitan, V.; Aloy, A.

    1993-01-01

    The problem of storing, transmitting, and manipulating digital images is considered. Because of the file sizes involved, large amounts of digitized image information are becoming common in modern projects. Our goal is to described an image compression transform coder based on artificial neural networks techniques (NNCTC). A comparison of the compression results obtained from digital astronomical images by the NNCTC and the method used in the compression of the digitized sky survey from the Space Telescope Science Institute based on the H-transform is performed in order to assess the reliability of the NNCTC.

  5. FPGA-based artificial neural network using CORDIC modules

    NASA Astrophysics Data System (ADS)

    Liddicoat, Albert A.; Slivovsky, Lynne A.; McLenegan, Tim; Heyer, Don

    2006-08-01

    Artificial neural networks have been used in applications that require complex procedural algorithms and in systems which lack an analytical mathematic model. By designing a large network of computing nodes based on the artificial neuron model, new solutions can be developed for computational problems in fields such as image processing and speech recognition. Neural networks are inherently parallel since each neuron, or node, acts as an autonomous computational element. Artificial neural networks use a mathematical model for each node that processes information from other nodes in the same region. The information processing entails computing a weighted average computation followed by a nonlinear mathematical transformation. Some typical artificial neural network applications use the exponential function or trigonometric functions for the nonlinear transformation. Various simple artificial neural networks have been implemented using a processor to compute the output for each node sequentially. This approach uses sequential processing and does not take advantage of the parallelism of a complex artificial neural network. In this work a hardware-based approach is investigated for artificial neural network applications. A Field Programmable Gate Arrays (FPGAs) is used to implement an artificial neuron using hardware multipliers, adders and CORDIC functional units. In order to create a large scale artificial neural network, area efficient hardware units such as CORDIC units are needed. High performance and low cost bit serial CORDIC implementations are presented. Finally, the FPGA resources and the performance of a hardware-based artificial neuron are presented.

  6. Examples of Current and Future Uses of Neural-Net Image Processing for Aerospace Applications

    NASA Technical Reports Server (NTRS)

    Decker, Arthur J.

    2004-01-01

    Feed forward artificial neural networks are very convenient for performing correlated interpolation of pairs of complex noisy data sets as well as detecting small changes in image data. Image-to-image, image-to-variable and image-to-index applications have been tested at Glenn. Early demonstration applications are summarized including image-directed alignment of optics, tomography, flow-visualization control of wind-tunnel operations and structural-model-trained neural networks. A practical application is reviewed that employs neural-net detection of structural damage from interference fringe patterns. Both sensor-based and optics-only calibration procedures are available for this technique. These accomplishments have generated the knowledge necessary to suggest some other applications for NASA and Government programs. A tomography application is discussed to support Glenn's Icing Research tomography effort. The self-regularizing capability of a neural net is shown to predict the expected performance of the tomography geometry and to augment fast data processing. Other potential applications involve the quantum technologies. It may be possible to use a neural net as an image-to-image controller of an optical tweezers being used for diagnostics of isolated nano structures. The image-to-image transformation properties also offer the potential for simulating quantum computing. Computer resources are detailed for implementing the black box calibration features of the neural nets.

  7. Identification and interpretation of patterns in rocket engine data: Artificial intelligence and neural network approaches

    NASA Technical Reports Server (NTRS)

    Ali, Moonis; Whitehead, Bruce; Gupta, Uday K.; Ferber, Harry

    1995-01-01

    This paper describes an expert system which is designed to perform automatic data analysis, identify anomalous events and determine the characteristic features of these events. We have employed both artificial intelligence and neural net approaches in the design of this expert system.

  8. Molnets: An Artificial Chemistry Based on Neural Networks

    NASA Technical Reports Server (NTRS)

    Colombano, Silvano; Luk, Johnny; Segovia-Juarez, Jose L.; Lohn, Jason; Clancy, Daniel (Technical Monitor)

    2002-01-01

    The fundamental problem in the evolution of matter is to understand how structure-function relationships are formed and increase in complexity from the molecular level all the way to a genetic system. We have created a system where structure-function relationships arise naturally and without the need of ad hoc function assignments to given structures. The idea was inspired by neural networks, where the structure of the net embodies specific computational properties. In this system networks interact with other networks to create connections between the inputs of one net and the outputs of another. The newly created net then recomputes its own synaptic weights, based on anti-hebbian rules. As a result some connections may be cut, and multiple nets can emerge as products of a 'reaction'. The idea is to study emergent reaction behaviors, based on simple rules that constitute a pseudophysics of the system. These simple rules are parameterized to produce behaviors that emulate chemical reactions. We find that these simple rules show a gradual increase in the size and complexity of molecules. We have been building a virtual artificial chemistry laboratory for discovering interesting reactions and for testing further ideas on the evolution of primitive molecules. Some of these ideas include the potential effect of membranes and selective diffusion according to molecular size.

  9. Use of artifical neural nets to predict permeability in Hugoton Field

    SciTech Connect

    Thompson, K.A.; Franklin, M.H.; Olson, T.M.

    1996-12-31

    One of the most difficult tasks in petrophysics is establishing a quantitative relationship between core permeability and wireline logs. This is a tough problem in Hugoton Field, where a complicated mix of carbonates and clastics further obscure the correlation. One can successfully model complex relationships such as permeability-to-logs using artificial neural networks. Mind and Vision, Inc.`s neural net software was used because of its orientation toward depth-related data (such as logs) and its ability to run on a variety of log analysis platforms. This type of neural net program allows the expert geologist to select a few (10-100) points of control to train the {open_quotes}brainstate{close_quotes} using logs as predicters and core permeability as {open_quotes}truth{close_quotes}. In Hugoton Field, the brainstate provides an estimate of permeability at each depth in 474 logged wells. These neural net-derived permeabilities are being used in reservoir characterization models for fluid saturations. Other applications of this artificial neural network technique include deterministic relationships of logs to: core lithology, core porosity, pore type, and other wireline logs (e.g., predicting a sonic log from a density log).

  10. Use of artifical neural nets to predict permeability in Hugoton Field

    SciTech Connect

    Thompson, K.A.; Franklin, M.H.; Olson, T.M. )

    1996-01-01

    One of the most difficult tasks in petrophysics is establishing a quantitative relationship between core permeability and wireline logs. This is a tough problem in Hugoton Field, where a complicated mix of carbonates and clastics further obscure the correlation. One can successfully model complex relationships such as permeability-to-logs using artificial neural networks. Mind and Vision, Inc.'s neural net software was used because of its orientation toward depth-related data (such as logs) and its ability to run on a variety of log analysis platforms. This type of neural net program allows the expert geologist to select a few (10-100) points of control to train the [open quotes]brainstate[close quotes] using logs as predicters and core permeability as [open quotes]truth[close quotes]. In Hugoton Field, the brainstate provides an estimate of permeability at each depth in 474 logged wells. These neural net-derived permeabilities are being used in reservoir characterization models for fluid saturations. Other applications of this artificial neural network technique include deterministic relationships of logs to: core lithology, core porosity, pore type, and other wireline logs (e.g., predicting a sonic log from a density log).

  11. Automation of Some Operations of a Wind Tunnel Using Artificial Neural Networks

    NASA Technical Reports Server (NTRS)

    Decker, Arthur J.; Buggele, Alvin E.

    1996-01-01

    Artificial neural networks were used successfully to sequence operations in a small, recently modernized, supersonic wind tunnel at NASA-Lewis Research Center. The neural nets generated correct estimates of shadowgraph patterns, pressure sensor readings and mach numbers for conditions occurring shortly after startup and extending to fully developed flow. Artificial neural networks were trained and tested for estimating: sensor readings from shadowgraph patterns, shadowgraph patterns from shadowgraph patterns and sensor readings from sensor readings. The 3.81 by 10 in. (0.0968 by 0.254 m) tunnel was operated with its mach 2.0 nozzle, and shadowgraph was recorded near the nozzle exit. These results support the thesis that artificial neural networks can be combined with current workstation technology to automate wind tunnel operations.

  12. Functional expansion representations of artificial neural networks

    NASA Technical Reports Server (NTRS)

    Gray, W. Steven

    1992-01-01

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

  13. A neural net approach to space vehicle guidance

    NASA Technical Reports Server (NTRS)

    Caglayan, Alper K.; Allen, Scott M.

    1990-01-01

    The space vehicle guidance problem is formulated using a neural network approach, and the appropriate neural net architecture for modeling optimum guidance trajectories is investigated. In particular, an investigation is made of the incorporation of prior knowledge about the characteristics of the optimal guidance solution into the neural network architecture. The online classification performance of the developed network is demonstrated using a synthesized network trained with a database of optimum guidance trajectories. Such a neural-network-based guidance approach can readily adapt to environment uncertainties such as those encountered by an AOTV during atmospheric maneuvers.

  14. Artificial neural network for multifunctional areas.

    PubMed

    Riccioli, Francesco; El Asmar, Toufic; El Asmar, Jean-Pierre; Fagarazzi, Claudio; Casini, Leonardo

    2016-01-01

    The issues related to the appropriate planning of the territory are particularly pronounced in highly inhabited areas (urban areas), where in addition to protecting the environment, it is important to consider an anthropogenic (urban) development placed in the context of sustainable growth. This work aims at mathematically simulating the changes in the land use, by implementing an artificial neural network (ANN) model. More specifically, it will analyze how the increase of urban areas will develop and whether this development would impact on areas with particular socioeconomic and environmental value, defined as multifunctional areas. The simulation is applied to the Chianti Area, located in the province of Florence, in Italy. Chianti is an area with a unique landscape, and its territorial planning requires a careful examination of the territory in which it is inserted. PMID:26718948

  15. Mesh deformation based on artificial neural networks

    NASA Astrophysics Data System (ADS)

    Stadler, Domen; Kosel, Franc; Čelič, Damjan; Lipej, Andrej

    2011-09-01

    In the article a new mesh deformation algorithm based on artificial neural networks is introduced. This method is a point-to-point method, meaning that it does not use connectivity information for calculation of the mesh deformation. Two already known point-to-point methods, based on interpolation techniques, are also presented. In contrast to the two known interpolation methods, the new method does not require a summation over all boundary nodes for one displacement calculation. The consequence of this fact is a shorter computational time of mesh deformation, which is proven by different deformation tests. The quality of the deformed meshes with all three deformation methods was also compared. Finally, the generated and the deformed three-dimensional meshes were used in the computational fluid dynamics numerical analysis of a Francis water turbine. A comparison of the analysis results was made to prove the applicability of the new method in every day computation.

  16. Dynamic Artificial Neural Networks with Affective Systems

    PubMed Central

    Schuman, Catherine D.; Birdwell, J. Douglas

    2013-01-01

    Artificial neural networks (ANNs) are processors that are trained to perform particular tasks. We couple a computational ANN with a simulated affective system in order to explore the interaction between the two. In particular, we design a simple affective system that adjusts the threshold values in the neurons of our ANN. The aim of this paper is to demonstrate that this simple affective system can control the firing rate of the ensemble of neurons in the ANN, as well as to explore the coupling between the affective system and the processes of long term potentiation (LTP) and long term depression (LTD), and the effect of the parameters of the affective system on its performance. We apply our networks with affective systems to a simple pole balancing example and briefly discuss the effect of affective systems on network performance. PMID:24303015

  17. NETS

    NASA Technical Reports Server (NTRS)

    Baffes, Paul T.

    1993-01-01

    NETS development tool provides environment for simulation and development of neural networks - computer programs that "learn" from experience. Written in ANSI standard C, program allows user to generate C code for implementation of neural network.

  18. Application of artificial neural networks to the design optimization of aerospace structural components

    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.

  19. Spatial predictive mapping using artificial neural networks

    NASA Astrophysics Data System (ADS)

    Noack, S.; Knobloch, A.; Etzold, S. H.; Barth, A.; Kallmeier, E.

    2014-11-01

    The modelling or prediction of complex geospatial phenomena (like formation of geo-hazards) is one of the most important tasks for geoscientists. But in practice it faces various difficulties, caused mainly by the complexity of relationships between the phenomena itself and the controlling parameters, as well by limitations of our knowledge about the nature of physical/ mathematical relationships and by restrictions regarding accuracy and availability of data. In this situation methods of artificial intelligence, like artificial neural networks (ANN) offer a meaningful alternative modelling approach compared to the exact mathematical modelling. In the past, the application of ANN technologies in geosciences was primarily limited due to difficulties to integrate it into geo-data processing algorithms. In consideration of this background, the software advangeo® was developed to provide a normal GIS user with a powerful tool to use ANNs for prediction mapping and data preparation within his standard ESRI ArcGIS environment. In many case studies, such as land use planning, geo-hazards analysis and prevention, mineral potential mapping, agriculture & forestry advangeo® has shown its capabilities and strengths. The approach is able to add considerable value to existing data.

  20. Microarray data classified by artificial neural networks.

    PubMed

    Linder, Roland; Richards, Tereza; Wagner, Mathias

    2007-01-01

    Systems biology has enjoyed explosive growth in both the number of people participating in this area of research and the number of publications on the topic. The field of systems biology encompasses the in silico analysis of high-throughput data as provided by DNA or protein microarrays. Along with the increasing availability of microarray data, attention is focused on methods of analyzing the expression rates. One important type of analysis is the classification task, for example, distinguishing different types of cell functions or tumors. Recently, interest has been awakened toward artificial neural networks (ANN), which have many appealing characteristics such as an exceptional degree of accuracy. Nonlinear relationships or independence from certain assumptions regarding the data distribution are also considered. The current work reviews advantages as well as disadvantages of neural networks in the context of microarray analysis. Comparisons are drawn to alternative methods. Selected solutions are discussed, and finally algorithms for the effective combination of multiple ANNs are presented. The development of approaches to use ANN-processed microarray data applicable to run cell and tissue simulations may be slated for future investigation. PMID:18220242

  1. Prospecting droughts with stochastic artificial neural networks

    NASA Astrophysics Data System (ADS)

    Ochoa-Rivera, Juan Camilo

    2008-04-01

    SummaryA non-linear multivariate model based on an artificial neural network multilayer perceptron is presented, that includes a random component. The developed model is applied to generate monthly streamflows, which are used to obtain synthetic annual droughts. The calibration of the model was undertaken using monthly streamflow records of several geographical sites of a basin. The model calibration consisted of training the neural network with the error back-propagation learning algorithm, and adding a normally distributed random noise. The model was validated by comparing relevant statistics of synthetic streamflow series to those of historical records. Annual droughts were calculated from the generated streamflow series, and then the expected values of length, intensity and magnitude of the droughts were assessed. An exercise on identical basis was made applying a second order auto-regressive multivariate model, AR(2), to compare its results with those of the developed model. The proposed model outperforms the AR(2) model in reproducing the future drought scenarios.

  2. Vibrational Analysis of Engine Components Using Neural-Net Processing and Electronic Holography

    NASA Technical Reports Server (NTRS)

    Decker, Arthur J.; Fite, E. Brian; Mehmed, Oral; Thorp, Scott A.

    1998-01-01

    The use of computational-model trained artificial neural networks to acquire damage specific information from electronic holograms is discussed. A neural network is trained to transform two time-average holograms into a pattern related to the bending-induced-strain distribution of the vibrating component. The bending distribution is very sensitive to component damage unlike the characteristic fringe pattern or the displacement amplitude distribution. The neural network processor is fast for real-time visualization of damage. The two-hologram limit makes the processor more robust to speckle pattern decorrelation. Undamaged and cracked cantilever plates serve as effective objects for testing the combination of electronic holography and neural-net processing. The requirements are discussed for using finite-element-model trained neural networks for field inspections of engine components. The paper specifically discusses neural-network fringe pattern analysis in the presence of the laser speckle effect and the performances of two limiting cases of the neural-net architecture.

  3. Vibrational Analysis of Engine Components Using Neural-Net Processing and Electronic Holography

    NASA Technical Reports Server (NTRS)

    Decker, Arthur J.; Fite, E. Brian; Mehmed, Oral; Thorp, Scott A.

    1997-01-01

    The use of computational-model trained artificial neural networks to acquire damage specific information from electronic holograms is discussed. A neural network is trained to transform two time-average holograms into a pattern related to the bending-induced-strain distribution of the vibrating component. The bending distribution is very sensitive to component damage unlike the characteristic fringe pattern or the displacement amplitude distribution. The neural network processor is fast for real-time visualization of damage. The two-hologram limit makes the processor more robust to speckle pattern decorrelation. Undamaged and cracked cantilever plates serve as effective objects for testing the combination of electronic holography and neural-net processing. The requirements are discussed for using finite-element-model trained neural networks for field inspections of engine components. The paper specifically discusses neural-network fringe pattern analysis in the presence of the laser speckle effect and the performances of two limiting cases of the neural-net architecture.

  4. Stochastic architecture for Hopfield neural nets

    NASA Technical Reports Server (NTRS)

    Pavel, Sandy

    1992-01-01

    An expandable stochastic digital architecture for recurrent (Hopfield like) neural networks is proposed. The main features and basic principles of stochastic processing are presented. The stochastic digital architecture is based on a chip with n full interconnected neurons with a pipeline, bit processing structure. For large applications, a flexible way to interconnect many such chips is provided.

  5. A real time neural net estimator of fatigue life

    NASA Technical Reports Server (NTRS)

    Troudet, T.; Merrill, W.

    1990-01-01

    A neural net architecture is proposed to estimate, in real-time, the fatigue life of mechanical components, as part of the Intelligent Control System for Reusable Rocket Engines. Arbitrary component loading values were used as input to train a two hidden-layer feedforward neural net to estimate component fatigue damage. The ability of the net to learn, based on a local strain approach, the mapping between load sequence and fatigue damage has been demonstrated for a uniaxial specimen. Because of its demonstrated performance, the neural computation may be extended to complex cases where the loads are biaxial or triaxial, and the geometry of the component is complex (e.g., turbopump blades). The generality of the approach is such that load/damage mappings can be directly extracted from experimental data without requiring any knowledge of the stress/strain profile of the component. In addition, the parallel network architecture allows real-time life calculations even for high frequency vibrations. Owing to its distributed nature, the neural implementation will be robust and reliable, enabling its use in hostile environments such as rocket engines. This neural net estimator of fatigue life is seen as the enabling technology to achieve component life prognosis, and therefore would be an important part of life extending control for reusable rocket engines.

  6. Optical neural net for classifying imaging spectrometer data

    NASA Technical Reports Server (NTRS)

    Barnard, Etienne; Casasent, David P.

    1989-01-01

    The problem of determining the composition of an unknown input mixture from its measured spectrum, given the spectra of a number of elements, is studied. The Hopfield minimization procedure was used to express the determination of the compositions as a problem suitable for solution by neural nets. A mathematical description of the problem was developed and used as a basis for a neural network solution and an optical implementation.

  7. Artificial Neural Network applied to lightning flashes

    NASA Astrophysics Data System (ADS)

    Gin, R. B.; Guedes, D.; Bianchi, R.

    2013-05-01

    The development of video cameras enabled cientists to study lightning discharges comportment with more precision. The main goal of this project is to create a system able to detect images of lightning discharges stored in videos and classify them using an Artificial Neural Network (ANN)using C Language and OpenCV libraries. The developed system, can be split in two different modules: detection module and classification module. The detection module uses OpenCV`s computer vision libraries and image processing techniques to detect if there are significant differences between frames in a sequence, indicating that something, still not classified, occurred. Whenever there is a significant difference between two consecutive frames, two main algorithms are used to analyze the frame image: brightness and shape algorithms. These algorithms detect both shape and brightness of the event, removing irrelevant events like birds, as well as detecting the relevant events exact position, allowing the system to track it over time. The classification module uses a neural network to classify the relevant events as horizontal or vertical lightning, save the event`s images and calculates his number of discharges. The Neural Network was implemented using the backpropagation algorithm, and was trained with 42 training images , containing 57 lightning events (one image can have more than one lightning). TheANN was tested with one to five hidden layers, with up to 50 neurons each. The best configuration achieved a success rate of 95%, with one layer containing 20 neurons (33 test images with 42 events were used in this phase). This configuration was implemented in the developed system to analyze 20 video files, containing 63 lightning discharges previously manually detected. Results showed that all the lightning discharges were detected, many irrelevant events were unconsidered, and the event's number of discharges was correctly computed. The neural network used in this project achieved a

  8. A Design of Neural-Net Based Decouplers

    NASA Astrophysics Data System (ADS)

    Tokuda, Makoto; Yamamoto, Toru; Monden, Yoshimi

    In process industries such as the chemical plants, a good control performance cannot be obtained by simply using the linear controllers, since most processes are nonlinear multivariable systems with mutual interactions. And now, in various fields, the neural networks are well known as the representative schemes to describe the nonlinear elements included in the systems. Also, many types of neural-net based control systems have been proposed, since they have the ability of function approximation, the training ability and versatility. However, the neural networks tend to require great deal of training iteration or careful adjustments of user-specified parameters. In this paper, a design method of neural-net based decouplers is proposed for nonlinear multivariable systems. Here, the decoupler is generated by the sum of a static decoupler and a neural-net based decoupler. The former is used so that the influence of mutual interactions is roughly removed, and the latter plays a role of compensating the nonlinearities and decoupling the remaining mutual interactions. Thus, by designing the control system as the hybrid system, the burden in training the neural networks can be considerably reduced. Finally, the effectiveness of the proposed control scheme is evaluated on a simulation example.

  9. Programmable synaptic devices for electronic neural nets

    NASA Technical Reports Server (NTRS)

    Moopenn, A.; Thakoor, A. P.

    1990-01-01

    The architecture, design, and operational characteristics of custom VLSI and thin film synaptic devices are described. The devices include CMOS-based synaptic chips containing 1024 reprogrammable synapses with a 6-bit dynamic range, and nonvolatile, write-once, binary synaptic arrays based on memory switching in hydrogenated amorphous silicon films. Their suitability for embodiment of fully parallel and analog neural hardware is discussed. Specifically, a neural network solution to an assignment problem of combinatorial global optimization, implemented in fully parallel hardware using the synaptic chips, is described. The network's ability to provide optimal and near optimal solutions over a time scale of few neuron time constants has been demonstrated and suggests a speedup improvement of several orders of magnitude over conventional search methods.

  10. Toward Real Time Neural Net Flight Controllers

    NASA Technical Reports Server (NTRS)

    Jorgensen, C. C.; Mah, R. W.; Ross, J.; Lu, Henry, Jr. (Technical Monitor)

    1994-01-01

    NASA Ames Research Center has an ongoing program in neural network control technology targeted toward real time flight demonstrations using a modified F-15 which permits direct inner loop control of actuators, rapid switching between alternative control designs, and substitutable processors. An important part of this program is the ACTIVE flight project which is examining the feasibility of using neural networks in the design, control, and system identification of new aircraft prototypes. This paper discusses two research applications initiated with this objective in mind: utilization of neural networks for wind tunnel aircraft model identification and rapid learning algorithms for on line reconfiguration and control. The first application involves the identification of aerodynamic flight characteristics from analysis of wind tunnel test data. This identification is important in the early stages of aircraft design because complete specification of control architecture's may not be possible even though concept models at varying scales are available for aerodynamic wind tunnel testing. Testing of this type is often a long and expensive process involving measurement of aircraft lift, drag, and moment of inertia at varying angles of attack and control surface configurations. This information in turn can be used in the design of the flight control systems by applying the derived lookup tables to generate piece wise linearized controllers. Thus, reduced costs in tunnel test times and the rapid transfer of wind tunnel insights into prototype controllers becomes an important factor in more efficient generation and testing of new flight systems. NASA Ames Research Center is successfully applying modular neural networks as one way of anticipating small scale aircraft model performances prior to testing, thus reducing the number of in tunnel test hours and potentially, the number of intermediate scaled models required for estimation of surface flow effects.

  11. Application of artificial neural networks to composite ply micromechanics

    NASA Technical Reports Server (NTRS)

    Brown, D. A.; Murthy, P. L. N.; Berke, L.

    1991-01-01

    Artificial neural networks can provide improved computational efficiency relative to existing methods when an algorithmic description of functional relationships is either totally unavailable or is complex in nature. For complex calculations, significant reductions in elapsed computation time are possible. The primary goal is to demonstrate the applicability of artificial neural networks to composite material characterization. As a test case, a neural network was trained to accurately predict composite hygral, thermal, and mechanical properties when provided with basic information concerning the environment, constituent materials, and component ratios used in the creation of the composite. A brief introduction on neural networks is provided along with a description of the project itself.

  12. Forecasting Zakat collection using artificial neural network

    NASA Astrophysics Data System (ADS)

    Sy Ahmad Ubaidillah, Sh. Hafizah; Sallehuddin, Roselina

    2013-04-01

    'Zakat', "that which purifies" or "alms", is the giving of a fixed portion of one's wealth to charity, generally to the poor and needy. It is one of the five pillars of Islam, and must be paid by all practicing Muslims who have the financial means (nisab). 'Nisab' is the minimum level to determine whether there is a 'zakat' to be paid on the assets. Today, in most Muslim countries, 'zakat' is collected through a decentralized and voluntary system. Under this voluntary system, 'zakat' committees are established, which are tasked with the collection and distribution of 'zakat' funds. 'Zakat' promotes a more equitable redistribution of wealth, and fosters a sense of solidarity amongst members of the 'Ummah'. The Malaysian government has established a 'zakat' center at every state to facilitate the management of 'zakat'. The center has to have a good 'zakat' management system to effectively execute its functions especially in the collection and distribution of 'zakat'. Therefore, a good forecasting model is needed. The purpose of this study is to develop a forecasting model for Pusat Zakat Pahang (PZP) to predict the total amount of collection from 'zakat' of assets more precisely. In this study, two different Artificial Neural Network (ANN) models using two different learning algorithms are developed; Back Propagation (BP) and Levenberg-Marquardt (LM). Both models are developed and compared in terms of their accuracy performance. The best model is determined based on the lowest mean square error and the highest correlations values. Based on the results obtained from the study, BP neural network is recommended as the forecasting model to forecast the collection from 'zakat' of assets for PZP.

  13. Detection of Wildfires with Artificial Neural Networks

    NASA Astrophysics Data System (ADS)

    Umphlett, B.; Leeman, J.; Morrissey, M. L.

    2011-12-01

    Currently fire detection for the National Oceanic and Atmospheric Administration (NOAA) using satellite data is accomplished with algorithms and error checking human analysts. Artificial neural networks (ANNs) have been shown to be more accurate than algorithms or statistical methods for applications dealing with multiple datasets of complex observed data in the natural sciences. ANNs also deal well with multiple data sources that are not all equally reliable or equally informative to the problem. An ANN was tested to evaluate its accuracy in detecting wildfires utilizing polar orbiter numerical data from the Advanced Very High Resolution Radiometer (AVHRR). Datasets containing locations of known fires were gathered from the NOAA's polar orbiting satellites via the Comprehensive Large Array-data Stewardship System (CLASS). The data was then calibrated and navigation corrected using the Environment for Visualizing Images (ENVI). Fires were located with the aid of shapefiles generated via ArcGIS. Afterwards, several smaller ten pixel by ten pixel datasets were created for each fire (using the ENVI corrected data). Several datasets were created for each fire in order to vary fire position and avoid training the ANN to look only at fires in the center of an image. Datasets containing no fires were also created. A basic pattern recognition neural network was established with the MATLAB neural network toolbox. The datasets were then randomly separated into categories used to train, validate, and test the ANN. To prevent over fitting of the data, the mean squared error (MSE) of the network was monitored and training was stopped when the MSE began to rise. Networks were tested using each channel of the AVHRR data independently, channels 3a and 3b combined, and all six channels. The number of hidden neurons for each input set was also varied between 5-350 in steps of 5 neurons. Each configuration was run 10 times, totaling about 4,200 individual network evaluations. Thirty

  14. Comparing and Contrasting Neural Net Solutions to Classical Statistical Solutions.

    ERIC Educational Resources Information Center

    Van Nelson, C.; Neff, Kathryn J.

    Data from two studies in which subjects were classified as successful or unsuccessful were analyzed using neural net technology after being analyzed with a linear regression function. Data were obtained from admission records of 201 students admitted to undergraduate and 285 students admitted to graduate programs. Data included grade point…

  15. Neural net diagnostics for VLSI test

    NASA Technical Reports Server (NTRS)

    Lin, T.; Tseng, H.; Wu, A.; Dogan, N.; Meador, J.

    1990-01-01

    This paper discusses the application of neural network pattern analysis algorithms to the IC fault diagnosis problem. A fault diagnostic is a decision rule combining what is known about an ideal circuit test response with information about how it is distorted by fabrication variations and measurement noise. The rule is used to detect fault existence in fabricated circuits using real test equipment. Traditional statistical techniques may be used to achieve this goal, but they can employ unrealistic a priori assumptions about measurement data. Our approach to this problem employs an adaptive pattern analysis technique based on feedforward neural networks. During training, a feedforward network automatically captures unknown sample distributions. This is important because distributions arising from the nonlinear effects of process variation can be more complex than is typically assumed. A feedforward network is also able to extract measurement features which contribute significantly to making a correct decision. Traditional feature extraction techniques employ matrix manipulations which can be particularly costly for large measurement vectors. In this paper we discuss a software system which we are developing that uses this approach. We also provide a simple example illustrating the use of the technique for fault detection in an operational amplifier.

  16. Artificial neural network model for material characterization by indentation

    NASA Astrophysics Data System (ADS)

    Tho, K. K.; Swaddiwudhipong, S.; Liu, Z. S.; Hua, J.

    2004-09-01

    Analytical methods to interpret the indentation load-displacement curves are difficult to formulate and solve due to material and geometric nonlinearities as well as complex contact interactions. In this study, large strain-large deformation finite element analyses were carried out to simulate indentation experiments. An artificial neural network model was constructed for the interpretation of indentation load-displacement curves. The data from finite element analyses were used to train and validate the artificial neural network model. The artificial neural network model was able to accurately determine the material properties when presented with the load-displacement curves that were not used in the training process. The proposed artificial neural network model is robust and directly relates the characteristics of the indentation load-displacement curve to the elasto-plastic material properties.

  17. Artificial neural network models for image understanding

    NASA Astrophysics Data System (ADS)

    Kulkarni, Arun D.; Byars, P.

    1991-06-01

    In this paper we introduce a new class of artificial neural network (ANN) models based on transformed domain feature extraction. Many optical and/or digital recognition systems based on transformed domain feature extraction are available in practice. Optical systems are inherently parallel in nature and are preferred for real time applications, whereas digital systems are more suitable for nonlinear operations. In our ANN models we combine advantages of both digital and optical systems. Many transformed domain feature extraction techniques have been developed during the last three decades. They include: the Fourier transform (FT), the Walsh Hadamard transform (WHT), the discrete cosine transform (DCT), etc. As an example, we have developed ANN models using the FT and WHT domain features. The models consist of two stages, the feature extraction stage and the recognition stage. We have used back-propagation and competitive learning algorithms in the recognition stage. We have used these ANN models for invariant object recognition. The models have been used successfully to recognize various types of aircraft, and also have been tested with test patterns. ANN models based on other transforms can be developed in a similar fashion.

  18. Groundwater remediation optimization using artificial neural networks

    SciTech Connect

    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.

  19. DEM interpolation based on artificial neural networks

    NASA Astrophysics Data System (ADS)

    Jiao, Limin; Liu, Yaolin

    2005-10-01

    This paper proposed a systemic resolution scheme of Digital Elevation model (DEM) interpolation based on Artificial Neural Networks (ANNs). In this paper, we employ BP network to fit terrain surface, and then detect and eliminate the samples with gross errors. This paper uses Self-organizing Feature Map (SOFM) to cluster elevation samples. The study area is divided into many more homogenous tiles after clustering. BP model is employed to interpolate DEM in each cluster. Because error samples are eliminated and clusters are built, interpolation result is better. The case study indicates that ANN interpolation scheme is feasible. It also shows that ANN can get a more accurate result by comparing ANN with polynomial and spline interpolation. ANN interpolation doesn't need to determine the interpolation function beforehand, so manmade influence is lessened. The ANN interpolation is more automatic and intelligent. At the end of the paper, we propose the idea of constructing ANN surface model. This model can be used in multi-scale DEM visualization, and DEM generalization, etc.

  20. Geophysical phenomena classification by artificial neural networks

    NASA Technical Reports Server (NTRS)

    Gough, M. P.; Bruckner, J. R.

    1995-01-01

    Space science information systems involve accessing vast data bases. There is a need for an automatic process by which properties of the whole data set can be assimilated and presented to the user. Where data are in the form of spectrograms, phenomena can be detected by pattern recognition techniques. Presented are the first results obtained by applying unsupervised Artificial Neural Networks (ANN's) to the classification of magnetospheric wave spectra. The networks used here were a simple unsupervised Hamming network run on a PC and a more sophisticated CALM network run on a Sparc workstation. The ANN's were compared in their geophysical data recognition performance. CALM networks offer such qualities as fast learning, superiority in generalizing, the ability to continuously adapt to changes in the pattern set, and the possibility to modularize the network to allow the inter-relation between phenomena and data sets. This work is the first step toward an information system interface being developed at Sussex, the Whole Information System Expert (WISE). Phenomena in the data are automatically identified and provided to the user in the form of a data occurrence morphology, the Whole Information System Data Occurrence Morphology (WISDOM), along with relationships to other parameters and phenomena.

  1. Geophysical phenomena classification by artificial neural networks

    SciTech Connect

    Gough, M.P.; Bruckner, J.R.

    1995-01-01

    Space science information systems involve accessing vast data bases. There is a need for an automatic process by which properties of the whole data set can be assimilated and presented to the user. Where data are in the form of spectrograms, phenomena can be detected by pattern recognition techniques. Presented are the first results obtained by applying unsupervised Artificial Neural Networks (ANN`s) to the classification of magnetospheric wave spectra. The networks used here were a simple unsupervised Hamming network run on a PC and a more sophisticated CALM network run on a Sparc workstation. The ANN`s were compared in their geophysical data recognition performance. CALM networks offer such qualities as fast learning, superiority in generalizing, the ability to continuously adapt to changes in the pattern set, and the possibility to modularize the network to allow the inter-relation between phenomena and data sets. This work is the first step toward an information system interface being developed at Sussex, the Whole Information System Expert (WISE). Phenomena in the data are automatically identified and provided to the user in the form of a data occurrence morphology, the Whole Information System Data Occurrence Morphology (WISDOM), along with relationships to other parameters and phenomena.

  2. Automated Wildfire Detection Through Artificial Neural Networks

    NASA Technical Reports Server (NTRS)

    Miller, Jerry; Borne, Kirk; Thomas, Brian; Huang, Zhenping; Chi, Yuechen

    2005-01-01

    We have tested and deployed Artificial Neural Network (ANN) data mining techniques to analyze remotely sensed multi-channel imaging data from MODIS, GOES, and AVHRR. The goal is to train the ANN to learn the signatures of wildfires in remotely sensed data in order to automate the detection process. We train the ANN using the set of human-detected wildfires in the U.S., which are provided by the Hazard Mapping System (HMS) wildfire detection group at NOAA/NESDIS. The ANN is trained to mimic the behavior of fire detection algorithms and the subjective decision- making by N O M HMS Fire Analysts. We use a local extremum search in order to isolate fire pixels, and then we extract a 7x7 pixel array around that location in 3 spectral channels. The corresponding 147 pixel values are used to populate a 147-dimensional input vector that is fed into the ANN. The ANN accuracy is tested and overfitting is avoided by using a subset of the training data that is set aside as a test data set. We have achieved an automated fire detection accuracy of 80-92%, depending on a variety of ANN parameters and for different instrument channels among the 3 satellites. We believe that this system can be deployed worldwide or for any region to detect wildfires automatically in satellite imagery of those regions. These detections can ultimately be used to provide thermal inputs to climate models.

  3. Decision net, directed graph, and neural net processing of imaging spectrometer data

    NASA Technical Reports Server (NTRS)

    Casasent, David; Liu, Shiaw-Dong; Yoneyama, Hideyuki; Barnard, Etienne

    1989-01-01

    A decision-net solution involving a novel hierarchical classifier and a set of multiple directed graphs, as well as a neural-net solution, are respectively presented for large-class problem and mixture problem treatments of imaging spectrometer data. The clustering method for hierarchical classifier design, when used with multiple directed graphs, yields an efficient decision net. New directed-graph rules for reducing local maxima as well as the number of perturbations required, and the new starting-node rules for extending the reachability and reducing the search time of the graphs, are noted to yield superior results, as indicated by an illustrative 500-class imaging spectrometer problem.

  4. Artificial Neural Networks: A New Approach to Predicting Application Behavior.

    ERIC Educational Resources Information Center

    Gonzalez, Julie M. Byers; DesJardins, Stephen L.

    2002-01-01

    Applied the technique of artificial neural networks to predict which students were likely to apply to one research university. Compared the results to the traditional analysis tool, logistic regression modeling. Found that the addition of artificial intelligence models was a useful new tool for predicting student application behavior. (EV)

  5. Advances in Artificial Neural Networks - Methodological Development and Application

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Artificial neural networks as a major soft-computing technology have been extensively studied and applied during the last three decades. Research on backpropagation training algorithms for multilayer perceptron networks has spurred development of other neural network training algorithms for other ne...

  6. Multiple image sensor data fusion through artificial neural networks

    Technology Transfer Automated Retrieval System (TEKTRAN)

    With multisensor data fusion technology, the data from multiple sensors are fused in order to make a more accurate estimation of the environment through measurement, processing and analysis. Artificial neural networks are the computational models that mimic biological neural networks. With high per...

  7. Computation and control with neural nets

    SciTech Connect

    Corneliusen, A.; Terdal, P.; Knight, T.; Spencer, J.

    1989-10-04

    As energies have increased exponentially with time so have the size and complexity of accelerators and control systems. NN may offer the kinds of improvements in computation and control that are needed to maintain acceptable functionality. For control their associative characteristics could provide signal conversion or data translation. Because they can do any computation such as least squares, they can close feedback loops autonomously to provide intelligent control at the point of action rather than at a central location that requires transfers, conversions, hand-shaking and other costly repetitions like input protection. Both computation and control can be integrated on a single chip, printed circuit or an optical equivalent that is also inherently faster through full parallel operation. For such reasons one expects lower costs and better results. Such systems could be optimized by integrating sensor and signal processing functions. Distributed nets of such hardware could communicate and provide global monitoring and multiprocessing in various ways e.g. via token, slotted or parallel rings (or Steiner trees) for compatibility with existing systems. Problems and advantages of this approach such as an optimal, real-time Turing machine are discussed. Simple examples are simulated and hardware implemented using discrete elements that demonstrate some basic characteristics of learning and parallelism. Future microprocessors' are predicted and requested on this basis. 19 refs., 18 figs.

  8. Neural net learning issues in classification of free text documents

    SciTech Connect

    Dasigi, V.R.; Mann, R.C.

    1996-03-01

    In intelligent analysis of large amounts of text, not any single clue indicates reliably that a pattern of interest has been found. When using multiple clues, it is not known how these should be integrated into a decision. In the context of this investigation, we have been using neural nets as parameterized mappings that allow for fusion of higher level clues extracted from free text. By using higher level clues and features, we avoid very large networks. By using the dominant singular values computed by Latent Semantic Indexing (LSI) and applying neural network algorithms for integrating these values and the outputs from other ``sensors,`` we have obtained preliminary encouraging results with text classification.

  9. Applications of artificial neural networks; Proceedings of the Meeting, Orlando, FL, Apr. 18-20, 1990

    SciTech Connect

    Rogers, S.K.

    1990-01-01

    The present conference discusses artificial neural networks (ANNs) for automatic target recognition, theory of networks for learning, abductive networks, target recognition in parallel networks, ANN recognition of atomic and molecular species, multispectral image fusion with ANNs, radar warning/emitter identification processing by ANNs, IR target motion estimation by ANNs, and the analog hardware implementation of neocognition networks. Also discussed are a multidimensional Kohonen net on a HyperCube, robot learning, probabilistic neural networks, ANNs for interpolation and extrapolation, knowledge-base browsing with hybrid distributed/local connectionist networks, predicate calculus for ANNs, abductive networks for electronic warfare, uncertainty computations in ANNs, and the classification power of multiple-layer ANNs.

  10. Fast neural net simulation with a DSP processor array.

    PubMed

    Muller, U A; Gunzinger, A; Guggenbuhl, W

    1995-01-01

    This paper describes the implementation of a fast neural net simulator on a novel parallel distributed-memory computer. A 60-processor system, named MUSIC (multiprocessor system with intelligent communication), is operational and runs the backpropagation algorithm at a speed of 330 million connection updates per second (continuous weight update) using 32-b floating-point precision. This is equal to 1.4 Gflops sustained performance. The complete system with 3.8 Gflops peak performance consumes less than 800 W of electrical power and fits into a 19-in rack. While reaching the speed of modern supercomputers, MUSIC still can be used as a personal desktop computer at a researcher's own disposal. In neural net simulation, this gives a computing performance to a single user which was unthinkable before. The system's real-time interfaces make it especially useful for embedded applications. PMID:18263299

  11. Automatic voice recognition using traditional and artificial neural network approaches

    NASA Technical Reports Server (NTRS)

    Botros, Nazeih M.

    1989-01-01

    The main objective of this research is to develop an algorithm for isolated-word recognition. This research is focused on digital signal analysis rather than linguistic analysis of speech. Features extraction is carried out by applying a Linear Predictive Coding (LPC) algorithm with order of 10. Continuous-word and speaker independent recognition will be considered in future study after accomplishing this isolated word research. To examine the similarity between the reference and the training sets, two approaches are explored. The first is implementing traditional pattern recognition techniques where a dynamic time warping algorithm is applied to align the two sets and calculate the probability of matching by measuring the Euclidean distance between the two sets. The second is implementing a backpropagation artificial neural net model with three layers as the pattern classifier. The adaptation rule implemented in this network is the generalized least mean square (LMS) rule. The first approach has been accomplished. A vocabulary of 50 words was selected and tested. The accuracy of the algorithm was found to be around 85 percent. The second approach is in progress at the present time.

  12. Automatic labeling and characterization of objects using artificial neural networks

    NASA Technical Reports Server (NTRS)

    Campbell, William J.; Hill, Scott E.; Cromp, Robert F.

    1989-01-01

    Existing NASA supported scientific data bases are usually developed, managed and populated in a tedious, error prone and self-limiting way in terms of what can be described in a relational Data Base Management System (DBMS). The next generation Earth remote sensing platforms, i.e., Earth Observation System, (EOS), will be capable of generating data at a rate of over 300 Mbs per second from a suite of instruments designed for different applications. What is needed is an innovative approach that creates object-oriented databases that segment, characterize, catalog and are manageable in a domain-specific context and whose contents are available interactively and in near-real-time to the user community. Described here is work in progress that utilizes an artificial neural net approach to characterize satellite imagery of undefined objects into high-level data objects. The characterized data is then dynamically allocated to an object-oriented data base where it can be reviewed and assessed by a user. The definition, development, and evolution of the overall data system model are steps in the creation of an application-driven knowledge-based scientific information system.

  13. A real time neural net estimator of fatigue life

    NASA Technical Reports Server (NTRS)

    Troudet, T.; Merrill, W.

    1990-01-01

    A neural network architecture is proposed to estimate, in real-time, the fatigue life of mechanical components, as part of the intelligent Control System for Reusable Rocket Engines. Arbitrary component loading values were used as input to train a two hidden-layer feedforward neural net to estimate component fatigue damage. The ability of the net to learn, based on a local strain approach, the mapping between load sequence and fatigue damage has been demonstrated for a uniaxial specimen. Because of its demonstrated performance, the neural computation may be extended to complex cases where the loads are biaxial or triaxial, and the geometry of the component is complex (e.g., turbopumps blades). The generality of the approach is such that load/damage mappings can be directly extracted from experimental data without requiring any knowledge of the stress/strain profile of the component. In addition, the parallel network architecture allows real-time life calculations even for high-frequency vibrations. Owing to its distributed nature, the neural implementation will be robust and reliable, enabling its use in hostile environments such as rocket engines.

  14. Adaptive Neurons For Artificial Neural Networks

    NASA Technical Reports Server (NTRS)

    Tawel, Raoul

    1990-01-01

    Training time decreases dramatically. In improved mathematical model of neural-network processor, temperature of neurons (in addition to connection strengths, also called weights, of synapses) varied during supervised-learning phase of operation according to mathematical formalism and not heuristic rule. Evidence that biological neural networks also process information at neuronal level.

  15. Medical image analysis with artificial neural networks.

    PubMed

    Jiang, J; Trundle, P; Ren, J

    2010-12-01

    Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging. PMID:20713305

  16. Optoelectronic analogs of self-programming neural nets - Architecture and methodologies for implementing fast stochastic learning by simulated annealing

    NASA Technical Reports Server (NTRS)

    Farhat, Nabil H.

    1987-01-01

    Self-organization and learning is a distinctive feature of neural nets and processors that sets them apart from conventional approaches to signal processing. It leads to self-programmability which alleviates the problem of programming complexity in artificial neural nets. In this paper architectures for partitioning an optoelectronic analog of a neural net into distinct layers with prescribed interconnectivity pattern to enable stochastic learning by simulated annealing in the context of a Boltzmann machine are presented. Stochastic learning is of interest because of its relevance to the role of noise in biological neural nets. Practical considerations and methodologies for appreciably accelerating stochastic learning in such a multilayered net are described. These include the use of parallel optical computing of the global energy of the net, the use of fast nonvolatile programmable spatial light modulators to realize fast plasticity, optical generation of random number arrays, and an adaptive noisy thresholding scheme that also makes stochastic learning more biologically plausible. The findings reported predict optoelectronic chips that can be used in the realization of optical learning machines.

  17. Wood Defect Identification Based on Artificial Neural Network

    NASA Astrophysics Data System (ADS)

    Zhu, Xiao-Dong; Cao, Jun; Wang, Feng-Hu; Sun, Jian-Ping; Liu, Yu

    Defects in wooden material reduce the value of timber. In order to save and improve the utilization of the timber, many studies are carried out on the ways to detect defects in wood. The recent development of computer technology, data processing technology and signal processing technology provides researchers with more damage identification problem solution ideas and methods. This article studies the vibration characteristics of wood. With an exploration of the wavelet analysis and artificial neural network for the wood composite material defects based on non-destructive testing, an artificial neural network model is established for wood-based composite materials non-destructive testing technology.

  18. Functional approximation using artificial neural networks in structural mechanics

    NASA Technical Reports Server (NTRS)

    Alam, Javed; Berke, Laszlo

    1993-01-01

    The artificial neural networks (ANN) methodology is an outgrowth of research in artificial intelligence. In this study, the feed-forward network model that was proposed by Rumelhart, Hinton, and Williams was applied to the mapping of functions that are encountered in structural mechanics problems. Several different network configurations were chosen to train the available data for problems in materials characterization and structural analysis of plates and shells. By using the recall process, the accuracy of these trained networks was assessed.

  19. Use of artificial neural networks in prostate cancer.

    PubMed

    Errejon, A; Crawford, E D; Dayhoff, J; O'Donnell, C; Tewari, A; Finkelstein, J; Gamito, E J

    2001-01-01

    Artificial neural networks (ANNs) are a type of artificial intelligence software inspired by biological neuronal systems that can be used for nonlinear statistical modeling. In recent years, these applications have played an increasing role in predictive and classification modeling in medical research. We review the basic concepts behind ANNs and examine the role of this technology in selected applications in prostate cancer research. PMID:11790276

  20. AUTOMATED DEFECT CLASSIFICATION USING AN ARTIFICIAL NEURAL NETWORK

    SciTech Connect

    Chady, T.; Caryk, M.; Piekarczyk, B.

    2009-03-03

    The automated defect classification algorithm based on artificial neural network with multilayer backpropagation structure was utilized. The selected features of flaws were used as input data. In order to train the neural network it is necessary to prepare learning data which is representative database of defects. Database preparation requires the following steps: image acquisition and pre-processing, image enhancement, defect detection and feature extraction. The real digital radiographs of welded parts of a ship were used for this purpose.

  1. Automated Defect Classification Using AN Artificial Neural Network

    NASA Astrophysics Data System (ADS)

    Chady, T.; Caryk, M.; Piekarczyk, B.

    2009-03-01

    The automated defect classification algorithm based on artificial neural network with multilayer backpropagation structure was utilized. The selected features of flaws were used as input data. In order to train the neural network it is necessary to prepare learning data which is representative database of defects. Database preparation requires the following steps: image acquisition and pre-processing, image enhancement, defect detection and feature extraction. The real digital radiographs of welded parts of a ship were used for this purpose.

  2. Using neural nets to measure ocular refractive errors: a proposal

    NASA Astrophysics Data System (ADS)

    Netto, Antonio V.; Ferreira de Oliveira, Maria C.

    2002-12-01

    We propose the development of a functional system for diagnosing and measuring ocular refractive errors in the human eye (astigmatism, hypermetropia and myopia) by automatically analyzing images of the human ocular globe acquired with the Hartmann-Schack (HS) technique. HS images are to be input into a system capable of recognizing the presence of a refractive error and outputting a measure of such an error. The system should pre-process and image supplied by the acquisition technique and then use artificial neural networks combined with fuzzy logic to extract the necessary information and output an automated diagnosis of the refractive errors that may be present in the ocular globe under exam.

  3. Face recognition: Eigenface, elastic matching, and neural nets

    SciTech Connect

    Zhang, J.; Yan, Y.; Lades, M.

    1997-09-01

    This paper is a comparative study of three recently proposed algorithms for face recognition: eigenface, autoassociation and classification neural nets, and elastic matching. After these algorithms were analyzed under a common statistical decision framework, they were evaluated experimentally on four individual data bases, each with a moderate subject size, and a combined data base with more than a hundred different subjects. Analysis and experimental results indicate that the eigenface algorithm, which is essentially a minimum distance classifier, works well when lighting variation is small. Its performance deteriorates significantly as lighting variation increases. The elastic matching algorithm, on the other hand, is insensitive to lighting, face position, and expression variations and therefore is more versatile. The performance of the autoassociation and classification nets is upper bounded by that of the eigenface but is more difficult to implement in practice.

  4. Artificial Neural Networks in Policy Research: A Current Assessment.

    ERIC Educational Resources Information Center

    Woelfel, Joseph

    1993-01-01

    Suggests that artificial neural networks (ANNs) exhibit properties that promise usefulness for policy researchers. Notes that ANNs have found extensive use in areas once reserved for multivariate statistical programs such as regression and multiple classification analysis and are developing an extensive community of advocates for processing text…

  5. Introducing Artificial Neural Networks through a Spreadsheet Model

    ERIC Educational Resources Information Center

    Rienzo, Thomas F.; Athappilly, Kuriakose K.

    2012-01-01

    Business students taking data mining classes are often introduced to artificial neural networks (ANN) through point and click navigation exercises in application software. Even if correct outcomes are obtained, students frequently do not obtain a thorough understanding of ANN processes. This spreadsheet model was created to illuminate the roles of…

  6. Artificial Neural Networks for Modeling Knowing and Learning in Science.

    ERIC Educational Resources Information Center

    Roth, Wolff-Michael

    2000-01-01

    Advocates artificial neural networks as models for cognition and development. Provides an example of how such models work in the context of a well-known Piagetian developmental task and school science activity: balance beam problems. (Contains 59 references.) (Author/WRM)

  7. Artificial-neural-network-based failure detection and isolation

    NASA Astrophysics Data System (ADS)

    Sadok, Mokhtar; Gharsalli, Imed; Alouani, Ali T.

    1998-03-01

    This paper presents the design of a systematic failure detection and isolation system that uses the concept of failure sensitive variables (FSV) and artificial neural networks (ANN). The proposed approach was applied to tube leak detection in a utility boiler system. Results of the experimental testing are presented in the paper.

  8. Recurrent Artificial Neural Networks and Finite State Natural Language Processing.

    ERIC Educational Resources Information Center

    Moisl, Hermann

    It is argued that pessimistic assessments of the adequacy of artificial neural networks (ANNs) for natural language processing (NLP) on the grounds that they have a finite state architecture are unjustified, and that their adequacy in this regard is an empirical issue. First, arguments that counter standard objections to finite state NLP on the…

  9. An artificial neural network controller for intelligent transportation systems applications

    SciTech Connect

    Vitela, J.E.; Hanebutte, U.R.; Reifman, J.

    1996-04-01

    An Autonomous Intelligent Cruise Control (AICC) has been designed using a feedforward artificial neural network, as an example for utilizing artificial neural networks for nonlinear control problems arising in intelligent transportation systems applications. The AICC is based on a simple nonlinear model of the vehicle dynamics. A Neural Network Controller (NNC) code developed at Argonne National Laboratory to control discrete dynamical systems was used for this purpose. In order to test the NNC, an AICC-simulator containing graphical displays was developed for a system of two vehicles driving in a single lane. Two simulation cases are shown, one involving a lead vehicle with constant velocity and the other a lead vehicle with varying acceleration. More realistic vehicle dynamic models will be considered in future work.

  10. HVAC pipe/duct sizing using artificial neural networks

    SciTech Connect

    Yeh, S.J.D.; Wong, K.F.V.

    1995-12-31

    The main objective of this study is to demonstrate that artificial neural networks (ANN`s) serve as useful aids to Heating, Ventilating and Air-Conditioning (HVAC) system design. In the present work, the design process for sizing fluid systems in HVAC is simulated by using ANN`S. Four ANN`s have been constructed in a personal computer, one for air duct sizing and three for pipe sizing. The air duct network was trained to output the friction rate and duct size. The three pipe sizing neural networks product pressure drops and pipe diameters. By using the trained artificial neural networks, data can be obtained instantly with errors less than 3%. Thus, ANN`s have been shown to simplify traditional methods and procedures in HVAC pipe and air duct sizing.

  11. Artificial Neural Networks Applied To Landslide Hazard Assessment

    NASA Astrophysics Data System (ADS)

    Casagli, N.; Catani, F.; Ermini, L.

    Landslide hazard mapping is often performed through the identification and analysis of hillslope instability factors. GIS techniques are widely applied for the manage- ment of hillslope factors as thematic data rated by the attribution of scores based on the assumed role played by each factor controlling the development of a sliding pro- cess. Other more refined methods, based on the principle that the present and the past are keys to the future, have been also developed, thus allowing to perform less sub- jective analyses, in which landslide susceptibility is assessed by statistical relation- ships between the past landslides and the hillslope instability factors. The objective of this research is to define a method able to foresee landslide susceptibility through the application of Artificial Neural Networks (ANN). The Riomaggiore catchment, a sub-watershed of the Reno River basin located in the Northern Apennine at half way between Florence and Bologna, was chosen as the test site. The utilized ANN (AiNet 1.25) was trained by vector-based GIS data corresponding to five hillslope factors: a) geology, b) slope, c), curvature, d) land cover e) contributing area. The intersection between the hillslope factors, all ranked in nominal scales, singled out 3263 homoge- neous domains (Unique Condition Unit) containing unique combinations of hillslope factors. The final model was formed by vectors in which the hillslope factors, once organized as Boolean variables, are represented by 20 binary numbers. The compari- son between the most recent inventory of the landslides in the Riomaggiore catchment and the hazardous areas, as predicted by the ANN, showed very satisfactory results and allowed us to validate the methodology.

  12. Artificial neural networks for decision-making in urologic oncology.

    PubMed

    Anagnostou, Theodore; Remzi, Mesut; Lykourinas, Michael; Djavan, Bob

    2003-06-01

    The authors are presenting a thorough introduction in Artificial Neural Networks (ANNs) and their contribution to modern Urologic Oncology. The article covers a description of Artificial Neural Network methodology and points out the differences of Artificial Intelligence to traditional statistic models in terms of serving patients and clinicians, in a different way than current statistical analysis. Since Artificial Intelligence is not yet fully understood by many practicing clinicians, the authors have reviewed a careful selection of articles in order to explore the clinical benefit of Artificial Intelligence applications in modern Urology questions and decision-making. The data are from real patients and reflect attempts to achieve more accurate diagnosis and prognosis, especially in prostate cancer that stands as a good example of difficult decision-making in everyday practice. Experience from current use of Artificial Intelligence is also being discussed, and the authors address future developments as well as potential problems such as medical record quality, precautions in using ANNs or resistance to system use, in an attempt to point out future demands and the need for common standards. The authors conclude that both methods should continue to be used in a complementary manner. ANNs still do not prove always better as to replace standard statistical analysis as the method of choice in interpreting medical data. PMID:12767358

  13. Data fusion with artificial neural networks (ANN) for classification of earth surface from microwave satellite measurements

    NASA Technical Reports Server (NTRS)

    Lure, Y. M. Fleming; Grody, Norman C.; Chiou, Y. S. Peter; Yeh, H. Y. Michael

    1993-01-01

    A data fusion system with artificial neural networks (ANN) is used for fast and accurate classification of five earth surface conditions and surface changes, based on seven SSMI multichannel microwave satellite measurements. The measurements include brightness temperatures at 19, 22, 37, and 85 GHz at both H and V polarizations (only V at 22 GHz). The seven channel measurements are processed through a convolution computation such that all measurements are located at same grid. Five surface classes including non-scattering surface, precipitation over land, over ocean, snow, and desert are identified from ground-truth observations. The system processes sensory data in three consecutive phases: (1) pre-processing to extract feature vectors and enhance separability among detected classes; (2) preliminary classification of Earth surface patterns using two separate and parallely acting classifiers: back-propagation neural network and binary decision tree classifiers; and (3) data fusion of results from preliminary classifiers to obtain the optimal performance in overall classification. Both the binary decision tree classifier and the fusion processing centers are implemented by neural network architectures. The fusion system configuration is a hierarchical neural network architecture, in which each functional neural net will handle different processing phases in a pipelined fashion. There is a total of around 13,500 samples for this analysis, of which 4 percent are used as the training set and 96 percent as the testing set. After training, this classification system is able to bring up the detection accuracy to 94 percent compared with 88 percent for back-propagation artificial neural networks and 80 percent for binary decision tree classifiers. The neural network data fusion classification is currently under progress to be integrated in an image processing system at NOAA and to be implemented in a prototype of a massively parallel and dynamically reconfigurable Modular

  14. A neutron spectrum unfolding computer code based on artificial neural networks

    NASA Astrophysics Data System (ADS)

    Ortiz-Rodríguez, J. M.; Reyes Alfaro, A.; Reyes Haro, A.; Cervantes Viramontes, J. M.; Vega-Carrillo, H. R.

    2014-02-01

    The Bonner Spheres Spectrometer consists of a thermal neutron sensor placed at the center of a number of moderating polyethylene spheres of different diameters. From the measured readings, information can be derived about the spectrum of the neutron field where measurements were made. Disadvantages of the Bonner system are the weight associated with each sphere and the need to sequentially irradiate the spheres, requiring long exposure periods. Provided a well-established response matrix and adequate irradiation conditions, the most delicate part of neutron spectrometry, is the unfolding process. The derivation of the spectral information is not simple because the unknown is not given directly as a result of the measurements. The drawbacks associated with traditional unfolding procedures have motivated the need of complementary approaches. Novel methods based on Artificial Intelligence, mainly Artificial Neural Networks, have been widely investigated. In this work, a neutron spectrum unfolding code based on neural nets technology is presented. This code is called Neutron Spectrometry and Dosimetry with Artificial Neural networks unfolding code that was designed in a graphical interface. The core of the code is an embedded neural network architecture previously optimized using the robust design of artificial neural networks methodology. The main features of the code are: easy to use, friendly and intuitive to the user. This code was designed for a Bonner Sphere System based on a 6LiI(Eu) neutron detector and a response matrix expressed in 60 energy bins taken from an International Atomic Energy Agency compilation. The main feature of the code is that as entrance data, for unfolding the neutron spectrum, only seven rate counts measured with seven Bonner spheres are required; simultaneously the code calculates 15 dosimetric quantities as well as the total flux for radiation protection purposes. This code generates a full report with all information of the unfolding in

  15. Chlorophyll a spatial inference using artificial neural network from multispectral images and in situ measurements.

    PubMed

    Ferreira, Monique S; Galo, Maria De Lourdes B T

    2013-01-01

    Considering the importance of monitoring the water quality parameters, remote sensing is a practicable alternative to limnological variables detection, which interacts with electromagnetic radiation, called optically active components (OAC). Among these, the phytoplankton pigment chlorophyll a is the most representative pigment of photosynthetic activity in all classes of algae. In this sense, this work aims to develop a method of spatial inference of chlorophyll a concentration using Artificial Neural Networks (ANN). To achieve this purpose, a multispectral image and fluorometric measurements were used as input data. The multispectral image was processed and the net training and validation dataset were carefully chosen. From this, the neural net architecture and its parameters were defined to model the variable of interest. In the end of training phase, the trained network was applied to the image and a qualitative analysis was done. Thus, it was noticed that the integration of fluorometric and multispectral data provided good results in the chlorophyll a inference, when combined in a structure of artificial neural networks. PMID:23828358

  16. Identification and interpretation of patterns in rocket engine data: Artificial intelligence and neural network approaches

    NASA Technical Reports Server (NTRS)

    Ali, Moonis; Whitehead, Bruce; Gupta, Uday K.; Ferber, Harry

    1989-01-01

    This paper describes an expert system which is designed to perform automatic data analysis, identify anomalous events, and determine the characteristic features of these events. We have employed both artificial intelligence and neural net approaches in the design of this expert system. The artificial intelligence approach is useful because it provides (1) the use of human experts' knowledge of sensor behavior and faulty engine conditions in interpreting data; (2) the use of engine design knowledge and physical sensor locations in establishing relationships among the events of multiple sensors; (3) the use of stored analysis of past data of faulty engine conditions; and (4) the use of knowledge-based reasoning in distinguishing sensor failure from actual faults. The neural network approach appears promising because neural nets (1) can be trained on extremely noisy data and produce classifications which are more robust under noisy conditions than other classification techniques; (2) avoid the necessity of noise removal by digital filtering and therefore avoid the need to make assumptions about frequency bands or other signal characteristics of anomalous behavior; (3) can, in effect, generate their own feature detectors based on the characteristics of the sensor data used in training; and (4) are inherently parallel and therefore are potentially implementable in special-purpose parallel hardware.

  17. Unsupervised classification of neural spikes with a hybrid multilayer artificial neural network.

    PubMed

    García, P; Suárez, C P; Rodríguez, J; Rodríguez, M

    1998-07-01

    The understanding of the brain structure and function and its computational style is one of the biggest challenges both in Neuroscience and Neural Computation. In order to reach this and to test the predictions of neural network modeling, it is necessary to observe the activity of neural populations. In this paper we propose a hybrid modular computational system for the spike classification of multiunits recordings. It works with no knowledge about the waveform, and it consists of two moduli: a Preprocessing (Segmentation) module, which performs the detection and centering of spike vectors using programmed computation; and a Processing (Classification) module, which implements the general approach of neural classification: feature extraction, clustering and discrimination, by means of a hybrid unsupervised multilayer artificial neural network (HUMANN). The operations of this artificial neural network on the spike vectors are: (i) compression with a Sanger Layer from 70 points vector to five principal component vector; (ii) their waveform is analyzed by a Kohonen layer; (iii) the electrical noise and overlapping spikes are rejected by a previously unreported artificial neural network named Tolerance layer; and (iv) finally the spikes are labeled into spike classes by a Labeling layer. Each layer of the system has a specific unsupervised learning rule that progressively modifies itself until the performance of the layer has been automatically optimized. The procedure showed a high sensitivity and specificity also when working with signals containing four spike types. PMID:10223516

  18. SU-F-BRD-11: Prediction of Dosimetric Endpoints From Patient Geometry Using Neural Nets

    SciTech Connect

    O'Connell, D; Chow, P; Agazaryan, N; Jani, S; Low, D; Lamb, J

    2014-06-15

    Purpose: The previously-published overlap volume histogram (OVH) technique lends itself naturally to prediction of the dose received by a given volume of tissue (e.g. D90) in intensity-modulated radiotherapy (IMRT) treatment plans. Here we extend the OVH technique using artificial neural networks in order to predict the volume of tissue receiving a given dose (e.g. V90) in both prostate IMRT and conventional breast radiotherapy. Methods: Twenty-nine prostate treatment plans and forty-three breast treatment plans were analyzed. The spatial relationships between the prostate and rectum and between the breast and ipsilateral lung were characterized using OVHs. The OVH is a cumulative histogram representing the fractional volume of the risk organ overlapped by a series of isotropic expansions of the planning target volume (PTV). Seven cases were identified as outliers and replanned. OVH points were used as inputs to a one hidden layer feed forward artificial neural network with quality parameters of the corresponding plan, such as the rectum V50, as targets. A 3-fold cross-validation was used to estimate the prediction error. Results: The root mean square (RMS) error between the predicted rectum V50s and the planned values was 2.3, which was 35% of the standard deviation of V50 for the twenty-nine plans. The RMS error of prediction of V20 of the ipsilateral lung in breast cases was 3.9, which was 90% of the standard deviation of the V20 values in the breast plan database. Conclusion: This study demonstrates that artificial neural nets can be used to extend the OVH technique to predict dosimetric endpoints taking the form of a volume receiving a given dose, rather than the minimum dose received by a given volume. Prediction of ipsilateral lung dose in breast radiotherapy using the OVH technique remains a work in progress.

  19. Neural networks: A versatile tool from artificial intelligence

    SciTech Connect

    Yama, B.R.; Lineberry, G.T.

    1996-12-31

    Artificial Intelligence research has produced several tools for commercial application in recent years. Artificial Neural Networks (ANNs), Fuzzy Logic, and Expert Systems are some of the techniques that are widely used today in various fields of engineering and business. Among these techniques, ANNs are gaining popularity due to their learning and other brain-like capabilities. Within the mining industry, ANN technology is being utilized with large payoffs for real-time process control applications. In this paper, a brief introduction to ANNs and the associated terminology is given. The neural network development process is outlined, followed by the back-propagation learning algorithm. Next, the development of two multi-layer, feed-forward neural networks is described and the results axe presented. One network is developed for prediction of strength of intact rock specimens, and another network is developed for prediction of mineral concentrations. Preliminary results indicate a predictive error less than 10% using cross-validation on a limited data set. The performance of the neural network for prediction of mineral concentrations was compared with kriging. It was found that the neural network performed not only satisfactorily, but in some cases performed better than, the kriging model.

  20. Spectral Classification of Unresolved Binary Stars with Artificial Neural Networks

    NASA Astrophysics Data System (ADS)

    Weaver, Wm. Bruce

    2000-09-01

    An artificial neural network technique has been developed to perform two-dimensional spectral classification of the components of binary stars. The spectra are based on the 15 Å resolution near-infrared (NIR) spectral classification system described by Torres-Dodgen & Weaver. Using the spectrum with no manual intervention except wavelength registration, a single artificial neural network (ANN) can classify these spectra with Morgan-Keenan types with an average accuracy of about 2.5 types (subclasses) in temperature and about 0.45 classes in luminosity for up to 3 mag of difference in luminosity. The error in temperature classification does not increase substantially until the secondary contributes less than 10% of the light of the system. By following the coarse-classification ANN with a specialist ANN, the mean absolute errors are reduced to about 0.5 types in temperature and 0.33 classes in luminosity. The resulting ANN network was applied to seven binary stars.

  1. Numerical solution of differential equations by artificial neural networks

    NASA Technical Reports Server (NTRS)

    Meade, Andrew J., Jr.

    1995-01-01

    Conventionally programmed digital computers can process numbers with great speed and precision, but do not easily recognize patterns or imprecise or contradictory data. Instead of being programmed in the conventional sense, artificial neural networks (ANN's) are capable of self-learning through exposure to repeated examples. However, the training of an ANN can be a time consuming and unpredictable process. A general method is being developed by the author to mate the adaptability of the ANN with the speed and precision of the digital computer. This method has been successful in building feedforward networks that can approximate functions and their partial derivatives from examples in a single iteration. The general method also allows the formation of feedforward networks that can approximate the solution to nonlinear ordinary and partial differential equations to desired accuracy without the need of examples. It is believed that continued research will produce artificial neural networks that can be used with confidence in practical scientific computing and engineering applications.

  2. Artificial neural networks technology for neutron spectrometry and dosimetry.

    PubMed

    Vega-Carrillo, H R; Hernández-Dávila, V M; Manzanares-Acuña, E; Gallego, E; Lorente, A; Iñiguez, M P

    2007-01-01

    Artificial Neural Network Technology has been applied to unfold neutron spectra and to calculate 13 dosimetric quantities using seven count rates from a Bonner Sphere Spectrometer with a (6)LiI(Eu). Two different networks, one for spectrometry and another for dosimetry, were designed. To train and test both networks, 177 neutron spectra from the IAEA compilation were utilised. Spectra were re-binned into 31 energy groups, and the dosimetric quantities were calculated using the MCNP code and the fluence-to-dose conversion coefficients from ICRP 74. Neutron spectra and UTA4 response matrix were used to calculate the expected count rates in the Bonner spectrometer. Spectra and H(10) of (239)PuBe and (241)AmBe were experimentally obtained and compared with those determined with the artificial neural networks. PMID:17522034

  3. Using Artificial Neural Networks to Assess Changes in Microbial Communities

    SciTech Connect

    Brandt, C.C.; Macnaughton, S.; Palumbo, A.V.; Pfiffner, S.M.; Schryver, J.C.

    1999-04-19

    We evaluated artificial neural networks (ANNs) as a technique for assessing changes in soil microbial communities following exposure to metals. We analyzed signature lipid biomarker (SLB) data collected from two soil microcosm experiments using traditional statistical techniques and ANN. Two phases of data analysis were done; pattern recognition and prediction. In general, the ANNs were better able to detect patterns and relationships in the SLB data than were the traditional statistical techniques.

  4. Artificial neural network modeling of p-cresol photodegradation

    PubMed Central

    2013-01-01

    Background The complexity of reactions and kinetic is the current problem of photodegradation processes. Recently, artificial neural networks have been widely used to solve the problems because of their reliable, robust, and salient characteristics in capturing the non-linear relationships between variables in complex systems. In this study, an artificial neural network was applied for modeling p-cresol photodegradation. To optimize the network, the independent variables including irradiation time, pH, photocatalyst amount and concentration of p-cresol were used as the input parameters, while the photodegradation% was selected as output. The photodegradation% was obtained from the performance of the experimental design of the variables under UV irradiation. The network was trained by Quick propagation (QP) and the other three algorithms as a model. To determine the number of hidden layer nodes in the model, the root mean squared error of testing set was minimized. After minimizing the error, the topologies of the algorithms were compared by coefficient of determination and absolute average deviation. Results The comparison indicated that the Quick propagation algorithm had minimum root mean squared error, 1.3995, absolute average deviation, 3.0478, and maximum coefficient of determination, 0.9752, for the testing data set. The validation test results of the artificial neural network based on QP indicated that the root mean squared error was 4.11, absolute average deviation was 8.071 and the maximum coefficient of determination was 0.97. Conclusion Artificial neural network based on Quick propagation algorithm with topology 4-10-1 gave the best performance in this study. PMID:23731706

  5. Classifying auroras using artificial neural networks

    NASA Astrophysics Data System (ADS)

    Rydesater, Peter; Brandstrom, Urban; Steen, Ake; Gustavsson, Bjorn

    1999-03-01

    In Auroral Large Imaging System (ALIS) there is need of stable methods for analysis and classification of auroral images and images with for example mother of pearl clouds. This part of ALIS is called Selective Imaging Techniques (SIT) and is intended to sort out images of scientific interest. It's also used to find out what and where in the images there is for example different auroral phenomena's. We will discuss some about the SIT units main functionality but this work is mainly concentrated on how to find auroral arcs and how they are placed in images. Special case have been taken to make the algorithm robust since it's going to be implemented in a SIT unit which will work automatic and often unsupervised and some extends control the data taking of ALIS. The method for finding auroral arcs is based on a local operator that detects intensity differens. This gives arc orientation values as a preprocessing which is fed to a neural network classifier. We will show some preliminary results and possibilities to use and improve this algorithm for use in the future SIT unit.

  6. Automated Wildfire Detection Through Artificial Neural Networks

    NASA Technical Reports Server (NTRS)

    Miller, Jerry; Borne, Kirk; Thomas, Brian; Huang, Zhenping; Chi, Yuechen

    2005-01-01

    Wildfires have a profound impact upon the biosphere and our society in general. They cause loss of life, destruction of personal property and natural resources and alter the chemistry of the atmosphere. In response to the concern over the consequences of wildland fire and to support the fire management community, the National Oceanic and Atmospheric Administration (NOAA), National Environmental Satellite, Data and Information Service (NESDIS) located in Camp Springs, Maryland gradually developed an operational system to routinely monitor wildland fire by satellite observations. The Hazard Mapping System, as it is known today, allows a team of trained fire analysts to examine and integrate, on a daily basis, remote sensing data from Geostationary Operational Environmental Satellite (GOES), Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite sensors and generate a 24 hour fire product for the conterminous United States. Although assisted by automated fire detection algorithms, N O M has not been able to eliminate the human element from their fire detection procedures. As a consequence, the manually intensive effort has prevented NOAA from transitioning to a global fire product as urged particularly by climate modelers. NASA at Goddard Space Flight Center in Greenbelt, Maryland is helping N O M more fully automate the Hazard Mapping System by training neural networks to mimic the decision-making process of the frre analyst team as well as the automated algorithms.

  7. The importance of artificial neural networks in biomedicine

    SciTech Connect

    Burke, H.B.

    1995-12-31

    The future explanatory power in biomedicine will be at the molecular-genetic level of analysis (rather than the epidemiologic-demographic or anatomic-cellular levels). This is the level of complex systems. Complex systems are characterized by nonlinearity and complex interactions. It is difficult for traditional statistical methods to capture complex systems because traditional methods attempt to find the model that best fits the statistician`s understanding of the phenomenon; complex systems are difficult to understand and therefore difficult to fit with a simple model. Artificial neural networks are nonparametric regression models. They can capture any phenomena, to any degree of accuracy (depending on the adequacy of the data and the power of the predictors), without prior knowledge of the phenomena. Further, artificial neural networks can be represented, not only as formulae, but also as graphical models. Graphical models can increase analytic power and flexibility. Artificial neural networks are a powerful method for capturing complex phenomena, but their use requires a paradigm shift, from exploratory analysis of the data to exploratory analysis of the model.

  8. Handwriting recognition using a reduced character method and neural nets

    NASA Astrophysics Data System (ADS)

    Bourbakis, Nikolaos G.; Koutsougeras, Cris; Jameel, A.

    1995-03-01

    This paper deals with the recognition of handwriting text character by using a reduced character methodology and neural nets (BNN, RNN). The reduced characters methodology is based on the representation (mapping) of the text characters on a small size 2-D array of 12 X 9. For the recognition process each character is considered as a composition of `main' and `secondary' features. The main features are the necessary and important parts of a character for its recognition. The secondary (or artistic) features are the parts of a character which contribute to its various representations. The reduced character methodology presented in this paper attempts to prove that the recognition of a reduced size character provides a robust approach for recognition of handwritten text. The RNN approach for handwritten character recognition is based upon recurrent neural networks. The recurrent networks have a feedback mechanism. The feedback mechanism acts to integrate new values of feature vector with their predecessors. The output is supervised according to a target function. These networks can deal with inputs and outputs that are explicit functions of time. A new way of associating shape information was used, which gives very consistent results for handwritten character recognition. In this scheme the `shadow' each character was considered to find was the distances between the margins of the character. The distances are normalized with respect to the maximum distance in the entire shape to minimize the effect of disproportionately formed characters. For this effort two neural networks and an attributed graph approach are used and their results are compared on a set of 5000 handwritten characters.

  9. A TLD dose algorithm using artificial neural networks

    SciTech Connect

    Moscovitch, M.; Rotunda, J.E.; Tawil, R.A.; Rathbone, B.A.

    1995-12-31

    An artificial neural network was designed and used to develop a dose algorithm for a multi-element thermoluminescence dosimeter (TLD). The neural network architecture is based on the concept of functional links network (FLN). Neural network is an information processing method inspired by the biological nervous system. A dose algorithm based on neural networks is fundamentally different as compared to conventional algorithms, as it has the capability to learn from its own experience. The neural network algorithm is shown the expected dose values (output) associated with given responses of a multi-element dosimeter (input) many times. The algorithm, being trained that way, eventually is capable to produce its own unique solution to similar (but not exactly the same) dose calculation problems. For personal dosimetry, the output consists of the desired dose components: deep dose, shallow dose and eye dose. The input consists of the TL data obtained from the readout of a multi-element dosimeter. The neural network approach was applied to the Harshaw Type 8825 TLD, and was shown to significantly improve the performance of this dosimeter, well within the U.S. accreditation requirements for personnel dosimeters.

  10. Artificial neural network modeling of dissolved oxygen in reservoir.

    PubMed

    Chen, Wei-Bo; Liu, Wen-Cheng

    2014-02-01

    The water quality of reservoirs is one of the key factors in the operation and water quality management of reservoirs. Dissolved oxygen (DO) in water column is essential for microorganisms and a significant indicator of the state of aquatic ecosystems. In this study, two artificial neural network (ANN) models including back propagation neural network (BPNN) and adaptive neural-based fuzzy inference system (ANFIS) approaches and multilinear regression (MLR) model were developed to estimate the DO concentration in the Feitsui Reservoir of northern Taiwan. The input variables of the neural network are determined as water temperature, pH, conductivity, turbidity, suspended solids, total hardness, total alkalinity, and ammonium nitrogen. The performance of the ANN models and MLR model was assessed through the mean absolute error, root mean square error, and correlation coefficient computed from the measured and model-simulated DO values. The results reveal that ANN estimation performances were superior to those of MLR. Comparing to the BPNN and ANFIS models through the performance criteria, the ANFIS model is better than the BPNN model for predicting the DO values. Study results show that the neural network particularly using ANFIS model is able to predict the DO concentrations with reasonable accuracy, suggesting that the neural network is a valuable tool for reservoir management in Taiwan. PMID:24078053

  11. Neural-Net Based Optical NDE Method for Structural Health Monitoring

    NASA Technical Reports Server (NTRS)

    Decker, Arthur J.; Weiland, Kenneth E.

    2003-01-01

    This paper answers some performance and calibration questions about a non-destructive-evaluation (NDE) procedure that uses artificial neural networks to detect structural damage or other changes from sub-sampled characteristic patterns. The method shows increasing sensitivity as the number of sub-samples increases from 108 to 6912. The sensitivity of this robust NDE method is not affected by noisy excitations of the first vibration mode. A calibration procedure is proposed and demonstrated where the output of a trained net can be correlated with the outputs of the point sensors used for vibration testing. The calibration procedure is based on controlled changes of fastener torques. A heterodyne interferometer is used as a displacement sensor for a demonstration of the challenges to be handled in using standard point sensors for calibration.

  12. Science of artificial neural networks; Proceedings of the Meeting, Orlando, FL, Apr. 21-24, 1992

    SciTech Connect

    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.

  13. Orbit-centered atmospheric density prediction using artificial neural networks

    NASA Astrophysics Data System (ADS)

    Pérez, David; Wohlberg, Brendt; Lovell, Thomas Alan; Shoemaker, Michael; Bevilacqua, Riccardo

    2014-05-01

    At low Earth orbits, drag force is a significant source of error for propagating the motion of a spacecraft. The main factor driving the changes on the drag force is neutral density. Global atmospheric models provide estimates for the density which are significantly affected by bias due to misrepresentations of the underlying physics and limitations on the statistical models. In this work a localized predictor based on artificial neural networks is presented. Localized refers to the focus being on a specific orbit, rather than a global prediction. The predictor uses density measurements or estimates on a given orbit and a set of proxies for solar and geomagnetic activities to predict the value of the density along the future orbit of the spacecraft. The performance of the localized predictor is studied for different neural network structures, testing periods of high and low solar and geomagnetic activities and different prediction windows. Comparison with previously developed methods show substantial benefits in using artificial neural networks, both in prediction accuracy and in the potential for spacecraft onboard implementation. In fact, the proposed neural networks are computationally efficient and would be straightforward to integrate into onboard software.

  14. Background considerations in the analysis of PIXE spectra by Artificial Neural Systems.

    NASA Astrophysics Data System (ADS)

    Correa, R.; Morales, J. R.; Requena, I.; Miranda, J.; Barrera, V. A.

    2016-05-01

    In order to study the importance of background in PIXE spectra to determine elemental concentrations in atmospheric aerosols using artificial neural systems ANS, two independently trained ANS were constructed, one which considered as input the net number of counts in the peak, and another which included the background. In the training and validation phases thirty eight spectra of aerosols collected in Santiago, Chile, were used. In both cases the elemental concentration values were similar. This fact was due to the intrinsic characteristic of ANS operating with normalized values of the net and total number of counts under the peaks, something that was verified in the analysis of 172 spectra obtained from aerosols collected in Mexico city. Therefore, networks operating under the mode which include background can reduce time and cost when dealing with large number of samples.

  15. Neural-net based coordinated stabilizing control for the exciter and governor loops of low head hydropower plants

    SciTech Connect

    Djukanovic, M.; Novicevic, M.; Dobrijevic, D.; Babic, B.; Sobajic, D.J.; Pao, Y.H. |

    1995-12-01

    This paper presents a design technique of a new adaptive optimal controller of the low head hydropower plant using artificial neural networks (ANN). The adaptive controller is to operate in real time to improve the generating unit transients through the exciter input, the guide vane position and the runner blade position. The new design procedure is based on self-organization and the predictive estimation capabilities of neural-nets implemented through the cluster-wise segmented associative memory scheme. The developed neural-net based controller (NNC) whose control signals are adjusted using the on-line measurements, can offer better damping effects for generator oscillations over a wide range of operating conditions than conventional controllers. Digital simulations of hydropower plant equipped with low head Kaplan turbine are performed and the comparisons of conventional excitation-governor control, state-space optimal control and neural-net based control are presented. Results obtained on the non-linear mathematical model demonstrate that the effects of the NNC closely agree with those obtained using the state-space multivariable discrete-time optimal controllers.

  16. Moiré fringe center determination using artificial neural network

    NASA Astrophysics Data System (ADS)

    Woo, W. H.; Yen, K. S.

    2015-07-01

    Moiré methods are commonly used in various engineering metrological practices such as deformation measurements and surface topography. In the past, most of the applications required human intervention in fringe pattern analysis and image processing development to analyze the moiré patterns. In a recent application of using circular gratings moiré pattern, researchers developed graphical analysis method to determine the in-plane (2-D) displacement change between the two circular gratings by analyzing the moiré pattern change. In this work, an artificial neural network approach was proposed to detect and locate moiré fringe centers of circular gratings without image preprocessing and curve fitting. The intensity values in columns of the transformed circular moiré pattern were extracted as the input to the neural network. Moiré fringe centers extracted using graphical analysis method were used as the target for the neural network training. The neural network produced reasonably accurate output with an average mean error of an average mean error of less than 1 unit pixel with standard deviation of less than 4 unit pixels in determining the location of the moiré fringe centers. The result showed that the neural network approach is applicable in moiré fringe centers determination and its feasibility in automating moiré pattern analysis with further improvement.

  17. Control of Wind Tunnel Operations Using Neural Net Interpretation of Flow Visualization Records

    NASA Technical Reports Server (NTRS)

    Buggele, Alvin E.; Decker, Arthur J.

    1994-01-01

    Neural net control of operations in a small subsonic/transonic/supersonic wind tunnel at Lewis Research Center is discussed. The tunnel and the layout for neural net control or control by other parallel processing techniques are described. The tunnel is an affordable, multiuser platform for testing instrumentation and components, as well as parallel processing and control strategies. Neural nets have already been tested on archival schlieren and holographic visualizations from this tunnel as well as recent supersonic and transonic shadowgraph. This paper discusses the performance of neural nets for interpreting shadowgraph images in connection with a recent exercise for tuning the tunnel in a subsonic/transonic cascade mode of operation. That mode was operated for performing wake surveys in connection with NASA's Advanced Subsonic Technology (AST) noise reduction program. The shadowgraph was presented to the neural nets as 60 by 60 pixel arrays. The outputs were tunnel parameters such as valve settings or tunnel state identifiers for selected tunnel operating points, conditions, or states. The neural nets were very sensitive, perhaps too sensitive, to shadowgraph pattern detail. However, the nets exhibited good immunity to variations in brightness, to noise, and to changes in contrast. The nets are fast enough so that ten or more can be combined per control operation to interpret flow visualization data, point sensor data, and model calculations. The pattern sensitivity of the nets will be utilized and tested to control wind tunnel operations at Mach 2.0 based on shock wave patterns.

  18. Evaluating the performance of two neutron spectrum unfolding codes based on iterative procedures and artificial neural networks

    SciTech Connect

    Ortiz-Rodriguez, J. M.; Reyes Alfaro, A.; Reyes Haro, A.; Solis Sanches, L. O.; Miranda, R. Castaneda; Cervantes Viramontes, J. M.; Vega-Carrillo, H. R.

    2013-07-03

    In this work the performance of two neutron spectrum unfolding codes based on iterative procedures and artificial neural networks is evaluated. The first one code based on traditional iterative procedures and called Neutron spectrometry and dosimetry from the Universidad Autonoma de Zacatecas (NSDUAZ) use the SPUNIT iterative algorithm and was designed to unfold neutron spectrum and calculate 15 dosimetric quantities and 7 IAEA survey meters. The main feature of this code is the automated selection of the initial guess spectrum trough a compendium of neutron spectrum compiled by the IAEA. The second one code known as Neutron spectrometry and dosimetry with artificial neural networks (NDSann) is a code designed using neural nets technology. The artificial intelligence approach of neural net does not solve mathematical equations. By using the knowledge stored at synaptic weights on a neural net properly trained, the code is capable to unfold neutron spectrum and to simultaneously calculate 15 dosimetric quantities, needing as entrance data, only the rate counts measured with a Bonner spheres system. Similarities of both NSDUAZ and NSDann codes are: they follow the same easy and intuitive user's philosophy and were designed in a graphical interface under the LabVIEW programming environment. Both codes unfold the neutron spectrum expressed in 60 energy bins, calculate 15 dosimetric quantities and generate a full report in HTML format. Differences of these codes are: NSDUAZ code was designed using classical iterative approaches and needs an initial guess spectrum in order to initiate the iterative procedure. In NSDUAZ, a programming routine was designed to calculate 7 IAEA instrument survey meters using the fluence-dose conversion coefficients. NSDann code use artificial neural networks for solving the ill-conditioned equation system of neutron spectrometry problem through synaptic weights of a properly trained neural network. Contrary to iterative procedures, in neural

  19. Evaluating the performance of two neutron spectrum unfolding codes based on iterative procedures and artificial neural networks

    NASA Astrophysics Data System (ADS)

    Ortiz-Rodríguez, J. M.; Reyes Alfaro, A.; Reyes Haro, A.; Solís Sánches, L. O.; Miranda, R. Castañeda; Cervantes Viramontes, J. M.; Vega-Carrillo, H. R.

    2013-07-01

    In this work the performance of two neutron spectrum unfolding codes based on iterative procedures and artificial neural networks is evaluated. The first one code based on traditional iterative procedures and called Neutron spectrometry and dosimetry from the Universidad Autonoma de Zacatecas (NSDUAZ) use the SPUNIT iterative algorithm and was designed to unfold neutron spectrum and calculate 15 dosimetric quantities and 7 IAEA survey meters. The main feature of this code is the automated selection of the initial guess spectrum trough a compendium of neutron spectrum compiled by the IAEA. The second one code known as Neutron spectrometry and dosimetry with artificial neural networks (NDSann) is a code designed using neural nets technology. The artificial intelligence approach of neural net does not solve mathematical equations. By using the knowledge stored at synaptic weights on a neural net properly trained, the code is capable to unfold neutron spectrum and to simultaneously calculate 15 dosimetric quantities, needing as entrance data, only the rate counts measured with a Bonner spheres system. Similarities of both NSDUAZ and NSDann codes are: they follow the same easy and intuitive user's philosophy and were designed in a graphical interface under the LabVIEW programming environment. Both codes unfold the neutron spectrum expressed in 60 energy bins, calculate 15 dosimetric quantities and generate a full report in HTML format. Differences of these codes are: NSDUAZ code was designed using classical iterative approaches and needs an initial guess spectrum in order to initiate the iterative procedure. In NSDUAZ, a programming routine was designed to calculate 7 IAEA instrument survey meters using the fluence-dose conversion coefficients. NSDann code use artificial neural networks for solving the ill-conditioned equation system of neutron spectrometry problem through synaptic weights of a properly trained neural network. Contrary to iterative procedures, in neural

  20. Neural net formulations for organically modified, hydrophobic silica aerogel

    SciTech Connect

    Noever, D.; Sibille, L.; Cronise, R.; Baskaran, S.; Hunt, A.

    1997-07-01

    Organic modification of aerogel chemical formulations is known to transfer desirable hydrophobicity to lightweight solids. However, the effects of chemical modification on other material constants such as elasticity, compliance, and sound dampening present a difficult optimization problem. Here a statistical treatment of a 9-variable optimization is accomplished with multiple regression and an artificial neural network (ANN). The ANN shows 95 percent prediction success for the entire data set of elasticity, compared to a multidimensional linear regression which shows a maximum correlation coefficient, R=0.782. In this case, using the Number of Categories Criterion for the standard multiple regression, traditional statistical methods can distinguish fewer than 1.83 categories (high and low elasticity) and cannot group or cluster the data to give more refined partitions. A non-linear surface requires at least 3 categories (high, low, and medium elasticities) to define its curvature. To predict best and worst gellation conditions, organic modification is most consistent with changed elasticity for sterically large groups and high hydroxyl concentrations per unit surface area. The isocontours for best silica and hydroxyl concentration have a complex saddle, the geometrical structure of which would elude a simple experimental design based on usual gradient descent methods for finding optimum. {copyright} {ital 1997 Materials Research Society.}

  1. Development of a neural net paradigm that predicts simulator sickness

    SciTech Connect

    Allgood, G.O.

    1993-03-01

    A disease exists that affects pilots and aircrew members who use Navy Operational Flight Training Systems. This malady, commonly referred to as simulator sickness and whose symptomatology closely aligns with that of motion sickness, can compromise the use of these systems because of a reduced utilization factor, negative transfer of training, and reduction in combat readiness. A report is submitted that develops an artificial neural network (ANN) and behavioral model that predicts the onset and level of simulator sickness in the pilots and aircrews who sue these systems. It is proposed that the paradigm could be implemented in real time as a biofeedback monitor to reduce the risk to users of these systems. The model captures the neurophysiological impact of use (human-machine interaction) by developing a structure that maps the associative and nonassociative behavioral patterns (learned expectations) and vestibular (otolith and semicircular canals of the inner ear) and tactile interaction, derived from system acceleration profiles, onto an abstract space that predicts simulator sickness for a given training flight.

  2. The Development of Animal Behavior: From Lorenz to Neural Nets

    NASA Astrophysics Data System (ADS)

    Bolhuis, Johan J.

    In the study of behavioral development both causal and functional approaches have been used, and they often overlap. The concept of ontogenetic adaptations suggests that each developmental phase involves unique adaptations to the environment of the developing animal. The functional concept of optimal outbreeding has led to further experimental evidence and theoretical models concerning the role of sexual imprinting in the evolutionary process of sexual selection. From a causal perspective it has been proposed that behavioral ontogeny involves the development of various kinds of perceptual, motor, and central mechanisms and the formation of connections among them. This framework has been tested for a number of complex behavior systems such as hunger and dustbathing. Imprinting is often seen as a model system for behavioral development in general. Recent advances in imprinting research have been the result of an interdisciplinary effort involving ethology, neuroscience, and experimental psychology, with a continual interplay between these approaches. The imprinting results are consistent with Lorenz' early intuitive suggestions and are also reflected in the architecture of recent neural net models.

  3. A brief overview and introduction to artificial neural networks.

    PubMed

    Buscema, Massimo

    2002-01-01

    This article is designed to acquaint professionals working in the field of substance use intervention with a range of artificial intelligence nonlinear, powerful tools, artificial neural networks, concepts, and paradigms. The family of ANNs, when appropriately selected and used, permits the maximization of what can be derived from available data as well as our studying and understanding the many people, processes, and phenomena which comprise substance use and its intervention. The latter represent complex, dynamic, multidimensional phenomena which are unpredictable and uncontrollable in the traditional "cause and effect" sense. As such they are likely to be nonlinear in their very essence. Using linear-based paradigms for planned intervention with nonlinear phenomena brooks the all-too-common possibility of using inappropriate intervention paradigms and/or drawing misleading conclusions about what is and/or has happened. PMID:12180558

  4. Fault Tolerant Characteristics of Artificial Neural Network Electronic Hardware

    NASA Technical Reports Server (NTRS)

    Zee, Frank

    1995-01-01

    The fault tolerant characteristics of analog-VLSI artificial neural network (with 32 neurons and 532 synapses) chips are studied by exposing them to high energy electrons, high energy protons, and gamma ionizing radiations under biased and unbiased conditions. The biased chips became nonfunctional after receiving a cumulative dose of less than 20 krads, while the unbiased chips only started to show degradation with a cumulative dose of over 100 krads. As the total radiation dose increased, all the components demonstrated graceful degradation. The analog sigmoidal function of the neuron became steeper (increase in gain), current leakage from the synapses progressively shifted the sigmoidal curve, and the digital memory of the synapses and the memory addressing circuits began to gradually fail. From these radiation experiments, we can learn how to modify certain designs of the neural network electronic hardware without using radiation-hardening techniques to increase its reliability and fault tolerance.

  5. Prediction of Universal Time using the artificial neural network

    NASA Astrophysics Data System (ADS)

    Richard, J. Y.; Lopes, P.; Barache, C.; Bizouard, C.; Gambis, D.

    2014-12-01

    The monitoring of the Earth Orientation Parameters (EOP) variations is the main task of the Earth orientation Center of the IERS. In addition, for various applications linked in particular to navigation, precise orbit determination or leap seconds announcements, short and long term predictions are required. The method which is currently applied for predictions is based on deterministic processes, Least Square fitting, autoregressive filtering (Gambis and Luzum 2011). We present an alternative method, the Artificial Neural Networks (ANN) which has have already been successfully applied for pattern recognition. It has been tested as well by various authors for EOP predictions but with so far no real improvement compared to the current methods (Schuh et. al. 2002). New formalisms recently developed allow reconsidering the use of neural networks for EOP predictions. Series of simulations were performed for both short and long term predictions. Statistics and comparisons with the current methods are presented.

  6. Application of Artificial Neural Networks for estimating index floods

    NASA Astrophysics Data System (ADS)

    Šimor, Viliam; Hlavčová, Kamila; Kohnová, Silvia; Szolgay, Ján

    2012-12-01

    This article presents an application of Artificial Neural Networks (ANNs) and multiple regression models for estimating mean annual maximum discharge (index flood) at ungauged sites. Both approaches were tested for 145 small basins in Slovakia in areas ranging from 20 to 300 km2. Using the objective clustering method, the catchments were divided into ten homogeneous pooling groups; for each pooling group, mutually independent predictors (catchment characteristics) were selected for both models. The neural network was applied as a simple multilayer perceptron with one hidden layer and with a back propagation learning algorithm. Hyperbolic tangents were used as an activation function in the hidden layer. Estimating index floods by the multiple regression models were based on deriving relationships between the index floods and catchment predictors. The efficiencies of both approaches were tested by the Nash-Sutcliffe and a correlation coefficients. The results showed the comparative applicability of both models with slightly better results for the index floods achieved using the ANNs methodology.

  7. Artificial neural network for location estimation in wireless communication systems.

    PubMed

    Chen, Chien-Sheng

    2012-01-01

    In a wireless communication system, wireless location is the technique used to estimate the location of a mobile station (MS). To enhance the accuracy of MS location prediction, we propose a novel algorithm that utilizes time of arrival (TOA) measurements and the angle of arrival (AOA) information to locate MS when three base stations (BSs) are available. Artificial neural networks (ANN) are widely used techniques in various areas to overcome the problem of exclusive and nonlinear relationships. When the MS is heard by only three BSs, the proposed algorithm utilizes the intersections of three TOA circles (and the AOA line), based on various neural networks, to estimate the MS location in non-line-of-sight (NLOS) environments. Simulations were conducted to evaluate the performance of the algorithm for different NLOS error distributions. The numerical analysis and simulation results show that the proposed algorithms can obtain more precise location estimation under different NLOS environments. PMID:22736978

  8. Design of Jetty Piles Using Artificial Neural Networks

    PubMed Central

    2014-01-01

    To overcome the complication of jetty pile design process, artificial neural networks (ANN) are adopted. To generate the training samples for training ANN, finite element (FE) analysis was performed 50 times for 50 different design cases. The trained ANN was verified with another FE analysis case and then used as a structural analyzer. The multilayer neural network (MBPNN) with two hidden layers was used for ANN. The framework of MBPNN was defined as the input with the lateral forces on the jetty structure and the type of piles and the output with the stress ratio of the piles. The results from the MBPNN agree well with those from FE analysis. Particularly for more complex modes with hundreds of different design cases, the MBPNN would possibly substitute parametric studies with FE analysis saving design time and cost. PMID:25177724

  9. Artificial neural network ensembles and their application in pooled flood frequency analysis

    NASA Astrophysics Data System (ADS)

    Shu, Chang; Burn, Donald H.

    2004-09-01

    Recent theoretical and empirical studies show that the generalization ability of artificial neural networks can be improved by combining several artificial neural networks in redundant ensembles. In this paper, a review is given of popular ensemble methods. Six approaches for creating artificial neural network ensembles are applied in pooled flood frequency analysis for estimating the index flood and the 10-year flood quantile. The results show that artificial neural network ensembles generate improved flood estimates and are less sensitive to the choice of initial parameters when compared with a single artificial neural network. Factors that may affect the generalization of an artificial neural network ensemble are analyzed. In terms of the methods for creating ensemble members, the model diversity introduced by varying the initial conditions of the base artificial neural networks to reduce the prediction error is comparable with more sophisticated methods, such as bagging and boosting. When the same method for creating ensemble members is used, combining member networks using stacking is generally better than using simple averaging. An ensemble size of at least 10 artificial neural networks is suggested to achieve sufficient generalization ability. In comparison with parametric regression methods, properly designed artificial neural network ensembles can significantly reduce the prediction error.

  10. Dynamic Modeling of time series using Artificial Neural Networks

    NASA Astrophysics Data System (ADS)

    Nair, A. D.; Principe, Jose C.

    1995-12-01

    Artificial Neural Networks (ANN) have the ability to adapt to and learn complex topologies, they represent new technology with which to explore dynamical systems. Multi-step prediction is used to capture the dynamics of the system that produced the time series. Multi-step prediction is implemented by a recurrent ANN trained with trajectory learning. Two separate memories are employed in training the ANN, the common tapped delay-line memory and the new gamma memory. This methodology has been applied to the time series of a white dwarf and to the quasar 3C 345.

  11. Artificial neural networks as a tool for galaxy classification.

    NASA Astrophysics Data System (ADS)

    Lahav, O.

    The author describes an Artificial Neural Network (ANN) approach to classification of galaxy images and spectra. ANNs can replicate the classification of galaxy images by a human expert to the same degree of agreement as that between two human experts. Similar methods are applied to classification of galaxy spectra. In particular, Principal Component Analysis of galaxy spectra can be used to compress the data, to suppress noise and to provide input to the ANNs. These and other classification methods will soon be applied to the Anglo-Australian 2-degree-Field redshift survey of 250,000 galaxies.

  12. Video data compression using artificial neural network differential vector quantization

    NASA Technical Reports Server (NTRS)

    Krishnamurthy, Ashok K.; Bibyk, Steven B.; Ahalt, Stanley C.

    1991-01-01

    An artificial neural network vector quantizer is developed for use in data compression applications such as Digital Video. Differential Vector Quantization is used to preserve edge features, and a new adaptive algorithm, known as Frequency-Sensitive Competitive Learning, is used to develop the vector quantizer codebook. To develop real time performance, a custom Very Large Scale Integration Application Specific Integrated Circuit (VLSI ASIC) is being developed to realize the associative memory functions needed in the vector quantization algorithm. By using vector quantization, the need for Huffman coding can be eliminated, resulting in superior performance against channel bit errors than methods that use variable length codes.

  13. Using Artificial Neural Networks to Assess Microbial Communities

    SciTech Connect

    Almeida, J.S.; Brand, C.C.; Palumbo, A.V.; Pfiffner, S.M.; Schryver, J.C.

    1998-09-08

    We are evaluating artificial neural networks (ANNs) as tools for assessing changes in soil microbial communities following exposure to metals. We analyzed signature lipid biomarker data collected from two soil microcosm experiments using an autoassociative ANN. In one experiment, the microcosms were exposed to O, 100, or 250 ppm of metals, and in the other experiment the microcosms were exposed to O or 500 ppm of metals. The ANNs were able to distinguish between microcosms exposed and not exposed to metals in both experiments.

  14. Stress calculation of crankshaft using artificial neural network

    SciTech Connect

    Shiomi, Kazuyuki; Watanabe, Sei

    1995-12-31

    A system that calculates the stress concentration factor of the crankpin fillet from six characteristic dimensions of the crankshaft was developed using an artificial neural network. The learning database was constructed based on the finite element analysis, and an ``adaptive transfer function algorithm`` was used for the learning calculations. The calculation errors of the stress concentration factors applied to crankshafts of small utility engines and outboard motors were found to be within {minus}6.9 to +6.3% of the measured values. With this system, designers can calculate the stress concentrated at crankpin fillets precisely in a short time.

  15. Communication: Separable potential energy surfaces from multiplicative artificial neural networks

    SciTech Connect

    Koch, Werner Zhang, Dong H.

    2014-07-14

    We present a potential energy surface fitting scheme based on multiplicative artificial neural networks. It has the sum of products form required for efficient computation of the dynamics of multidimensional quantum systems with the multi configuration time dependent Hartree method. Moreover, it results in analytic potential energy matrix elements when combined with quantum dynamics methods using Gaussian basis functions, eliminating the need for a local harmonic approximation. Scaling behavior with respect to the complexity of the potential as well as the requested accuracy is discussed.

  16. Artificial neural network approaches for fluorescence lifetime imaging techniques.

    PubMed

    Wu, Gang; Nowotny, Thomas; Zhang, Yongliang; Yu, Hong-Qi; Li, David Day-Uei

    2016-06-01

    A novel high-speed fluorescence lifetime imaging (FLIM) analysis method based on artificial neural networks (ANN) has been proposed. In terms of image generation, the proposed ANN-FLIM method does not require iterative searching procedures or initial conditions, and it can generate lifetime images at least 180-fold faster than conventional least squares curve-fitting software tools. The advantages of ANN-FLIM were demonstrated on both synthesized and experimental data, showing that it has great potential to fuel current revolutions in rapid FLIM technologies. PMID:27244414

  17. Adaptive conventional power system stabilizer based on artificial neural network

    SciTech Connect

    Kothari, M.L.; Segal, R.; Ghodki, B.K.

    1995-12-31

    This paper deals with an artificial neural network (ANN) based adaptive conventional power system stabilizer (PSS). The ANN comprises an input layer, a hidden layer and an output layer. The input vector to the ANN comprises real power (P) and reactive power (Q), while the output vector comprises optimum PSS parameters. A systematic approach for generating training set covering wide range of operating conditions, is presented. The ANN has been trained using back-propagation training algorithm. Investigations reveal that the dynamic performance of ANN based adaptive conventional PSS is quite insensitive to wide variations in loading conditions.

  18. Neural coding of passive lump detection in compliant artificial tissue

    PubMed Central

    Gwilliam, James C.; Yoshioka, Takashi; Hsiao, Steven S.

    2014-01-01

    Here, we investigate the neural mechanisms of detecting lumps embedded in artificial compliant tissues. We performed a combined psychophysical study of humans performing a passive lump detection task with a neurophysiological study in nonhuman primates (Macaca mulatta) where we recorded the responses of peripheral mechanoreceptive afferents to lumps embedded at various depths in intermediates (rubbers) of varying compliance. The psychophysical results reveal that human lump detection is greatly degraded by both lump depth and decreased compliance of the intermediate. The neurophysiology results reveal that only the slowly adapting type 1 (SA1) afferents provide a clear spatial representation of lumps at all depths and that the representation is affected by lump size, depth, and compliance of the intermediate. The rapidly adapting afferents are considerably less sensitive to the lump. We defined eight neural response measures that we hypothesized could explain the psychophysical behavior, including peak firing rate, spatial spread of neural activity, and additional parameters derived from these measures. We find that peak firing rate encodes the depth of the lump, and the neural spatial spread of the SA1 response encodes for lump size but not lump shape. We also find that the perception of lump size may be affected by the compliance of the intermediate. The results show that lump detection is based on a spatial population code of the SA1 afferents, which is distorted by the depth of the lump and compliance of the tissue. PMID:24805077

  19. Application of artificial neural networks to eating disorders.

    PubMed

    Buscema, M; Mazzetti di Pietralata, M; Salvemini, V; Intraligi, M; Indrimi, M

    1998-02-01

    An experimental application of Artificial Neural Networks to Eating Disorders is presented. The sample, composed of 172 cases (all women) collected at the Centre for the Diagnosis and Treatment of Eating Disorders of the 1st Medical Division of the St. Eugenio Hospital of Rome, was subdivided, on the basis of the diagnosis made by the specialist of the St. Eugenio, into four classes: Anorexia Nervosa (AN), Nervous Bulimia (NB), Binge Eating Disorders (BED) and Psychogenic Eating Disorders that are Not Otherwise Specified (PED-NOS). The data base was composed of 124 different variables: generic information, alimentary behavior, eventual treatment and hospitalization, substance use, menstrual cycles, weight and height, hematochemical and instrumental examinations, psychodiagnostic tests, etc. The goal of this experiment was to verify the accuracy of the Neural Networks in recognising anorexic and bulimic patients. This article describes 6 experiments, using a Feed Forward Neural Network, each one using different variables. Starting from only the generic variables (life styles, family environment, etc.) and hematoclinical and instrumental examinations, a Neural Networks provided 86.94% of the prediction precision. This work is meant to be a first contribution to creating diagnostic procedures for Eating Disorders, that would be simple and easy-to-use by professionals who are neither psychologists nor psychiatrists nor psychotherapists but who are, however, among the first to meet these patients and who are therefore called upon to give such patients the very first pieces of advice on seeking proper treatment. PMID:9533740

  20. Evolutionary artificial neural networks for hydrological systems forecasting

    NASA Astrophysics Data System (ADS)

    Chen, Yung-hsiang; Chang, Fi-John

    2009-03-01

    SummaryThe conventional ways of constructing artificial neural network (ANN) for a problem generally presume a specific architecture and do not automatically discover network modules appropriate for specific training data. Evolutionary algorithms are used to automatically adapt the network architecture and connection weights according to the problem environment without substantial human intervention. To improve on the drawbacks of the conventional optimal process, this study presents a novel evolutionary artificial neural network (EANN) for time series forecasting. The EANN has a hybrid procedure, including the genetic algorithm and the scaled conjugate gradient algorithm, where the feedforward ANN architecture and its connection weights of neurons are simultaneously identified and optimized. We first explored the performance of the proposed EANN for the Mackey-Glass chaotic time series. The performance of the different networks was evaluated. The excellent performance in forecasting of the chaotic series shows that the proposed algorithm concurrently possesses efficiency, effectiveness, and robustness. We further explored the applicability and reliability of the EANN in a real hydrological time series. Again, the results indicate the EANN can effectively and efficiently construct a viable forecast module for the 10-day reservoir inflow, and its accuracy is superior to that of the AR and ARMAX models.

  1. Prediction aluminum corrosion inhibitor efficiency using artificial neural network (ANN)

    NASA Astrophysics Data System (ADS)

    Ebrahimi, Sh; Kalhor, E. G.; Nabavi, S. R.; Alamiparvin, L.; Pogaku, R.

    2016-06-01

    In this study, activity of some Schiff bases as aluminum corrosion inhibitor was investigated using artificial neural network (ANN). Hence, corrosion inhibition efficiency of Schiff bases (in any type) were gathered from different references. Then these molecules were drawn and optimized in Hyperchem software. Molecular descriptors generating and descriptors selection were fulfilled by Dragon software and principal component analysis (PCA) method, respectively. These structural descriptors along with environmental descriptors (ambient temperature, time of exposure, pH and the concentration of inhibitor) were used as input variables. Furthermore, aluminum corrosion inhibition efficiency was used as output variable. Experimental data were split into three sets: training set (for model building) and test set (for model validation) and simulation (for general model). Modeling was performed by Multiple linear regression (MLR) methods and artificial neural network (ANN). The results obtained in linear models showed poor correlation between experimental and theoretical data. However nonlinear model presented satisfactory results. Higher correlation coefficient of ANN (R > 0.9) revealed that ANN can be successfully applied for prediction of aluminum corrosion inhibitor efficiency of Schiff bases in different environmental conditions.

  2. Adaptive evolutionary artificial neural networks for pattern classification.

    PubMed

    Oong, Tatt Hee; Isa, Nor Ashidi Mat

    2011-11-01

    This paper presents a new evolutionary approach called the hybrid evolutionary artificial neural network (HEANN) for simultaneously evolving an artificial neural networks (ANNs) topology and weights. Evolutionary algorithms (EAs) with strong global search capabilities are likely to provide the most promising region. However, they are less efficient in fine-tuning the search space locally. HEANN emphasizes the balancing of the global search and local search for the evolutionary process by adapting the mutation probability and the step size of the weight perturbation. This is distinguishable from most previous studies that incorporate EA to search for network topology and gradient learning for weight updating. Four benchmark functions were used to test the evolutionary framework of HEANN. In addition, HEANN was tested on seven classification benchmark problems from the UCI machine learning repository. Experimental results show the superior performance of HEANN in fine-tuning the network complexity within a small number of generations while preserving the generalization capability compared with other algorithms. PMID:21968733

  3. Neural-Net Processed Characteristic Patterns for Measurement of Structural Integrity of Pressure Cycled Components

    NASA Technical Reports Server (NTRS)

    Decker, A. J.

    2001-01-01

    A neural-net inspection process has been combined with a bootstrap training procedure and electronic holography to detect changes or damage in a pressure-cycled International Space Station cold plate to be used for cooling instrumentation. The cold plate was excited to vibrate in a normal mode at low amplitude, and the neural net was trained by example to flag small changes in the mode shape. The NDE (nondestructive-evaluation) technique is straightforward but in its infancy; its applications are ad-hoc and uncalibrated. Nevertheless previous research has shown that the neural net can detect displacement changes to better than 1/100 the maximum displacement amplitude. Development efforts that support the NDE technique are mentioned briefly, followed by descriptions of electronic holography and neural-net processing. The bootstrap training procedure and its application to detection of damage in a pressure-cycled cold plate are discussed. Suggestions for calibrating and quantifying the NDE procedure are presented.

  4. The use of neural nets for matching compressors with diesel engines

    SciTech Connect

    Nelson, S.A. II; Filipi, Z.S.; Assanis, D.N.

    1996-12-31

    A technique which uses trained neural nets to model the compressor in the context of a turbocharged diesel engine simulation is introduced. This technique replaces the usual interpolation of compressor maps with the evaluation of a smooth mathematical function, thus providing engine simulations with greater robustness and flexibility. Following presentation of the methodology, the proposed neural net technique is validated against data from a truck type, 6-cylinder, 14 liter diesel engine. Furthermore, with the introduction of an additional parameter, the proposed neural net can be trained to simulate an entire family of compressors. As a demonstration, five compressors of different sizes are represented with the neural net model, and used for matching calculations with intercooled and non-intercooled engine configurations at different speeds. This novel approach readily allows for evaluation of various options prior to prototype production, and is thus a powerful design tool for selection of the best compressor for a given diesel engine system.

  5. Neural nets in information retrieval: a case study of the 1987 Pravda

    NASA Astrophysics Data System (ADS)

    Scholtes, Jan C.

    1992-07-01

    This paper presents an implemented neural method for free-text information filtering. A specific interest (or `query') is taught to a Kohonen feature map. By using this network as a neural filter on a dynamic free-text data base, only associated subjects are selected from this data base. The method is compared with some classical statistical information-retrieval algorithms. Various simulations show that the neural net indeed converges toward a proper representation of the query. The algorithm seems well scalable (linear complexity in time and space) resulting in high speeds, little memory needs, and easy maintainability. By combining research results from connectionist natural language processing (NLP) and information retrieval (IR), a better understanding of neural nets in NLP, a clearer view of the relation between neural nets and statistical pattern recognition, and an increased information retrieval quality are obtained.

  6. NETS - A NEURAL NETWORK DEVELOPMENT TOOL, VERSION 3.0 (MACINTOSH VERSION)

    NASA Technical Reports Server (NTRS)

    Phillips, T. A.

    1994-01-01

    NETS, A Tool for the Development and Evaluation of Neural Networks, provides a simulation of Neural Network algorithms plus an environment for developing such algorithms. Neural Networks are a class of systems modeled after the human brain. Artificial Neural Networks are formed from hundreds or thousands of simulated neurons, connected to each other in a manner similar to brain neurons. Problems which involve pattern matching readily fit the class of problems which NETS is designed to solve. NETS uses the back propagation learning method for all of the networks which it creates. The nodes of a network are usually grouped together into clumps called layers. Generally, a network will have an input layer through which the various environment stimuli are presented to the network, and an output layer for determining the network's response. The number of nodes in these two layers is usually tied to some features of the problem being solved. Other layers, which form intermediate stops between the input and output layers, are called hidden layers. NETS allows the user to customize the patterns of connections between layers of a network. NETS also provides features for saving the weight values of a network during the learning process, which allows for more precise control over the learning process. NETS is an interpreter. Its method of execution is the familiar "read-evaluate-print" loop found in interpreted languages such as BASIC and LISP. The user is presented with a prompt which is the simulator's way of asking for input. After a command is issued, NETS will attempt to evaluate the command, which may produce more prompts requesting specific information or an error if the command is not understood. The typical process involved when using NETS consists of translating the problem into a format which uses input/output pairs, designing a network configuration for the problem, and finally training the network with input/output pairs until an acceptable error is reached. NETS

  7. NETS - A NEURAL NETWORK DEVELOPMENT TOOL, VERSION 3.0 (MACHINE INDEPENDENT VERSION)

    NASA Technical Reports Server (NTRS)

    Baffes, P. T.

    1994-01-01

    NETS, A Tool for the Development and Evaluation of Neural Networks, provides a simulation of Neural Network algorithms plus an environment for developing such algorithms. Neural Networks are a class of systems modeled after the human brain. Artificial Neural Networks are formed from hundreds or thousands of simulated neurons, connected to each other in a manner similar to brain neurons. Problems which involve pattern matching readily fit the class of problems which NETS is designed to solve. NETS uses the back propagation learning method for all of the networks which it creates. The nodes of a network are usually grouped together into clumps called layers. Generally, a network will have an input layer through which the various environment stimuli are presented to the network, and an output layer for determining the network's response. The number of nodes in these two layers is usually tied to some features of the problem being solved. Other layers, which form intermediate stops between the input and output layers, are called hidden layers. NETS allows the user to customize the patterns of connections between layers of a network. NETS also provides features for saving the weight values of a network during the learning process, which allows for more precise control over the learning process. NETS is an interpreter. Its method of execution is the familiar "read-evaluate-print" loop found in interpreted languages such as BASIC and LISP. The user is presented with a prompt which is the simulator's way of asking for input. After a command is issued, NETS will attempt to evaluate the command, which may produce more prompts requesting specific information or an error if the command is not understood. The typical process involved when using NETS consists of translating the problem into a format which uses input/output pairs, designing a network configuration for the problem, and finally training the network with input/output pairs until an acceptable error is reached. NETS

  8. Artificial Neural Network Analysis in Preclinical Breast Cancer

    PubMed Central

    Motalleb, Gholamreza

    2014-01-01

    Objective: In this study, artificial neural network (ANN) analysis of virotherapy in preclinical breast cancer was investigated. Materials and Methods: In this research article, a multilayer feed-forward neural network trained with an error back-propagation algorithm was incorporated in order to develop a predictive model. The input parameters of the model were virus dose, week and tamoxifen citrate, while tumor weight was included in the output parameter. Two different training algorithms, namely quick propagation (QP) and Levenberg-Marquardt (LM), were used to train ANN. Results: The results showed that the LM algorithm, with 3-9-1 arrangement is more efficient compared to QP. Using LM algorithm, the coefficient of determination (R2) between the actual and predicted values was determined as 0.897118 for all data. Conclusion: It can be concluded that this ANN model may provide good ability to predict the biometry information of tumor in preclinical breast cancer virotherapy. The results showed that the LM algorithm employed by Neural Power software gave the better performance compared with the QP and virus dose, and it is more important factor compared to tamoxifen and time (week). PMID:24381857

  9. NETS - A NEURAL NETWORK DEVELOPMENT TOOL, VERSION 3.0 (MACHINE INDEPENDENT VERSION)

    NASA Technical Reports Server (NTRS)

    Baffes, P. T.

    1994-01-01

    NETS, A Tool for the Development and Evaluation of Neural Networks, provides a simulation of Neural Network algorithms plus an environment for developing such algorithms. Neural Networks are a class of systems modeled after the human brain. Artificial Neural Networks are formed from hundreds or thousands of simulated neurons, connected to each other in a manner similar to brain neurons. Problems which involve pattern matching readily fit the class of problems which NETS is designed to solve. NETS uses the back propagation learning method for all of the networks which it creates. The nodes of a network are usually grouped together into clumps called layers. Generally, a network will have an input layer through which the various environment stimuli are presented to the network, and an output layer for determining the network's response. The number of nodes in these two layers is usually tied to some features of the problem being solved. Other layers, which form intermediate stops between the input and output layers, are called hidden layers. NETS allows the user to customize the patterns of connections between layers of a network. NETS also provides features for saving the weight values of a network during the learning process, which allows for more precise control over the learning process. NETS is an interpreter. Its method of execution is the familiar "read-evaluate-print" loop found in interpreted languages such as BASIC and LISP. The user is presented with a prompt which is the simulator's way of asking for input. After a command is issued, NETS will attempt to evaluate the command, which may produce more prompts requesting specific information or an error if the command is not understood. The typical process involved when using NETS consists of translating the problem into a format which uses input/output pairs, designing a network configuration for the problem, and finally training the network with input/output pairs until an acceptable error is reached. NETS

  10. NETS - A NEURAL NETWORK DEVELOPMENT TOOL, VERSION 3.0 (MACINTOSH VERSION)

    NASA Technical Reports Server (NTRS)

    Phillips, T. A.

    1994-01-01

    NETS, A Tool for the Development and Evaluation of Neural Networks, provides a simulation of Neural Network algorithms plus an environment for developing such algorithms. Neural Networks are a class of systems modeled after the human brain. Artificial Neural Networks are formed from hundreds or thousands of simulated neurons, connected to each other in a manner similar to brain neurons. Problems which involve pattern matching readily fit the class of problems which NETS is designed to solve. NETS uses the back propagation learning method for all of the networks which it creates. The nodes of a network are usually grouped together into clumps called layers. Generally, a network will have an input layer through which the various environment stimuli are presented to the network, and an output layer for determining the network's response. The number of nodes in these two layers is usually tied to some features of the problem being solved. Other layers, which form intermediate stops between the input and output layers, are called hidden layers. NETS allows the user to customize the patterns of connections between layers of a network. NETS also provides features for saving the weight values of a network during the learning process, which allows for more precise control over the learning process. NETS is an interpreter. Its method of execution is the familiar "read-evaluate-print" loop found in interpreted languages such as BASIC and LISP. The user is presented with a prompt which is the simulator's way of asking for input. After a command is issued, NETS will attempt to evaluate the command, which may produce more prompts requesting specific information or an error if the command is not understood. The typical process involved when using NETS consists of translating the problem into a format which uses input/output pairs, designing a network configuration for the problem, and finally training the network with input/output pairs until an acceptable error is reached. NETS

  11. Neuron-Glia Interactions in Neural Plasticity: Contributions of Neural Extracellular Matrix and Perineuronal Nets

    PubMed Central

    Dzyubenko, Egor; Gottschling, Christine

    2016-01-01

    Synapses are specialized structures that mediate rapid and efficient signal transmission between neurons and are surrounded by glial cells. Astrocytes develop an intimate association with synapses in the central nervous system (CNS) and contribute to the regulation of ion and neurotransmitter concentrations. Together with neurons, they shape intercellular space to provide a stable milieu for neuronal activity. Extracellular matrix (ECM) components are synthesized by both neurons and astrocytes and play an important role in the formation, maintenance, and function of synapses in the CNS. The components of the ECM have been detected near glial processes, which abut onto the CNS synaptic unit, where they are part of the specialized macromolecular assemblies, termed perineuronal nets (PNNs). PNNs have originally been discovered by Golgi and represent a molecular scaffold deposited in the interface between the astrocyte and subsets of neurons in the vicinity of the synapse. Recent reports strongly suggest that PNNs are tightly involved in the regulation of synaptic plasticity. Moreover, several studies have implicated PNNs and the neural ECM in neuropsychiatric diseases. Here, we highlight current concepts relating to neural ECM and PNNs and describe an in vitro approach that allows for the investigation of ECM functions for synaptogenesis. PMID:26881114

  12. DeepNet: An Ultrafast Neural Learning Code for Seismic Imaging

    SciTech Connect

    Barhen, J.; Protopopescu, V.; Reister, D.

    1999-07-10

    A feed-forward multilayer neural net is trained to learn the correspondence between seismic data and well logs. The introduction of a virtual input layer, connected to the nominal input layer through a special nonlinear transfer function, enables ultrafast (single iteration), near-optimal training of the net using numerical algebraic techniques. A unique computer code, named DeepNet, has been developed, that has achieved, in actual field demonstrations, results unattainable to date with industry standard tools.

  13. [The Identification of the Origin of Chinese Wolfberry Based on Infrared Spectral Technology and the Artificial Neural Network].

    PubMed

    Li, Zhong; Liu, Ming-de; Ji, Shou-xiang

    2016-03-01

    The Fourier Transform Infrared Spectroscopy (FTIR) is established to find the geographic origins of Chinese wolfberry quickly. In the paper, the 45 samples of Chinese wolfberry from different places of Qinghai Province are to be surveyed by FTIR. The original data matrix of FTIR is pretreated with common preprocessing and wavelet transform. Compared with common windows shifting smoothing preprocessing, standard normal variation correction and multiplicative scatter correction, wavelet transform is an effective spectrum data preprocessing method. Before establishing model through the artificial neural networks, the spectra variables are compressed by means of the wavelet transformation so as to enhance the training speed of the artificial neural networks, and at the same time the related parameters of the artificial neural networks model are also discussed in detail. The survey shows even if the infrared spectroscopy data is compressed to 1/8 of its original data, the spectral information and analytical accuracy are not deteriorated. The compressed spectra variables are used for modeling parameters of the backpropagation artificial neural network (BP-ANN) model and the geographic origins of Chinese wolfberry are used for parameters of export. Three layers of neural network model are built to predict the 10 unknown samples by using the MATLAB neural network toolbox design error back propagation network. The number of hidden layer neurons is 5, and the number of output layer neuron is 1. The transfer function of hidden layer is tansig, while the transfer function of output layer is purelin. Network training function is trainl and the learning function of weights and thresholds is learngdm. net. trainParam. epochs=1 000, while net. trainParam. goal = 0.001. The recognition rate of 100% is to be achieved. It can be concluded that the method is quite suitable for the quick discrimination of producing areas of Chinese wolfberry. The infrared spectral analysis technology

  14. The application of neural networks with artificial intelligence technique in the modeling of industrial processes

    SciTech Connect

    Saini, K. K.; Saini, Sanju

    2008-10-07

    Neural networks are a relatively new artificial intelligence technique that emulates the behavior of biological neural systems in digital software or hardware. These networks can 'learn', automatically, complex relationships among data. This feature makes the technique very useful in modeling processes for which mathematical modeling is difficult or impossible. The work described here outlines some examples of the application of neural networks with artificial intelligence technique in the modeling of industrial processes.

  15. ANNz: Estimating Photometric Redshifts Using Artificial Neural Networks

    NASA Astrophysics Data System (ADS)

    Collister, Adrian A.; Lahav, Ofer

    2004-04-01

    We introduce ANNz, a freely available software package for photometric redshift estimation using artificial neural networks. ANNz learns the relation between photometry and redshift from an appropriate training set of galaxies for which the redshift is already known. Where a large and representative training set is available, ANNz is a highly competitive tool when compared with traditional template-fitting methods. The ANNz package is demonstrated on the Sloan Digital Sky Survey Data Release 1, and for this particular data set the rms redshift error in the range 0<~z<~0.7 is σrms=0.023. Nonideal conditions (spectroscopic sets that are small or brighter than the photometric set for which redshifts are required) are simulated, and the impact on the photometric redshift accuracy is assessed.2

  16. Applications of Artificial Neural Networks (ANNs) in Food Science

    SciTech Connect

    HUang, Yiqun; Kangas, Lars J.; Rasco, Barbara A.

    2007-02-01

    Abstract Artificial neural networks (ANNs) have been applied in almost every aspect of food science over the past two decade, although most applications are in the development stage. ANNs are useful tools for food safety and quality analyses, which include modeling of microbial growth and from this predicting food safety, interpreting spectroscopic data, and predicting physical, chemical, functional and sensory properties of various food products during processing and distribution. ANNs have a great deal of promise for modeling complex tasks in process control and simulation, and in applications of machine perception including machine vision and the electronic nose for food safety and quality control. This review discusses the basic theory of the ANN technology and its applications in food science, providing food scientists and the research community an overview of the current research and future trend of the applications of ANN technology in this field.

  17. The application of artificial neural networks in indirect cost estimation

    NASA Astrophysics Data System (ADS)

    Leśniak, Agnieszka

    2013-10-01

    Estimating of the costs of construction project is one of the most important task in the management of the project. The total costs can be divided into direct costs that are related to executing the works, and indirect costs that accompany delivery. A precise costs estimation is usually a highly labour and time-intensive task especially when using manual calculation methods. This paper presents Artificial Neural Network (ANN) approach to predicting index of indirect cost of construction projects in Poland. A quantitative study was undertaken on the factors conditioning indirect costs of polish construction projects and a determination was made of the actual costs incurred by enterprises during project implementation. As a result of these studies, a data set was assembled covering 72 real-life cases of building projects constructed in Poland.

  18. CCD Image Identification: An Artificial Neural Networks Approach

    NASA Astrophysics Data System (ADS)

    El-Bassuny Alawy, A.; et al.

    An Artificial Neural Network (ANN) technique in supervised mode has been developed to classify stellar, cosmic and noise identities on CCD frames. It has been implemented and coded in the C language for Personal Computers users. Its learning factors and training (cumulative, rms and decision) errors have been investigated. Two sets comprising a few hundred images of stars, cosmic rays and noise of different levels were adopted to train and test the algorithm developed. The present approach has been applied on a CCD frame of the star cluster M67. The results were discussed in comparison with those obtained from DAOPHOTII code out of the same frame. It has been shown that the present approach is fast, precise, efficient and reliable as well as requiring no prior input data for identification.

  19. Automatic segmentation of cerebral MR images using artificial neural networks

    SciTech Connect

    Alirezaie, J.; Jernigan, M.E.; Nahmias, C.

    1996-12-31

    In this paper we present an unsupervised clustering technique for multispectral segmentation of magnetic resonance (MR) images of the human brain. Our scheme utilizes the Self Organizing Feature Map (SOFM) artificial neural network for feature mapping and generates a set of codebook vectors. By extending the network with an additional layer the map will be classified and each tissue class will be labelled. An algorithm has been developed for extracting the cerebrum from the head scan prior to the segmentation. Extracting the cerebrum is performed by stripping away the skull pixels from the T2 image. Three tissue types of the brain: white matter, gray matter and cerebral spinal fluid (CSF) are segmented accurately. To compare the results with other conventional approaches we applied the c-means algorithm to the problem.

  20. Modeling biodegradation and kinetics of glyphosate by artificial neural network.

    PubMed

    Nourouzi, Mohsen M; Chuah, Teong G; Choong, Thomas S Y; Rabiei, F

    2012-01-01

    An artificial neural network (ANN) model was developed to simulate the biodegradation of herbicide glyphosate [2-(Phosphonomethylamino) acetic acid] in a solution with varying parameters pH, inoculum size and initial glyphosate concentration. The predictive ability of ANN model was also compared with Monod model. The result showed that ANN model was able to accurately predict the experimental results. A low ratio of self-inhibition and half saturation constants of Haldane equations (< 8) exhibited the inhibitory effect of glyphosate on bacteria growth. The value of K(i)/K(s) increased when the mixed inoculum size was increased from 10(4) to 10(6) bacteria/mL. It was found that the percentage of glyphosate degradation reached a maximum value of 99% at an optimum pH 6-7 while for pH values higher than 9 or lower than 4, no degradation was observed. PMID:22424071

  1. Incomplete fuzzy data processing systems using artificial neural network

    NASA Technical Reports Server (NTRS)

    Patyra, Marek J.

    1992-01-01

    In this paper, the implementation of a fuzzy data processing system using an artificial neural network (ANN) is discussed. The binary representation of fuzzy data is assumed, where the universe of discourse is decartelized into n equal intervals. The value of a membership function is represented by a binary number. It is proposed that incomplete fuzzy data processing be performed in two stages. The first stage performs the 'retrieval' of incomplete fuzzy data, and the second stage performs the desired operation on the retrieval data. The method of incomplete fuzzy data retrieval is proposed based on the linear approximation of missing values of the membership function. The ANN implementation of the proposed system is presented. The system was computationally verified and showed a relatively small total error.

  2. Magnesium degradation as determined by artificial neural networks.

    PubMed

    Willumeit, Regine; Feyerabend, Frank; Huber, Norbert

    2013-11-01

    Magnesium degradation under physiological conditions is a highly complex process in which temperature, the use of cell culture growth medium and the presence of CO2, O2 and proteins can influence the corrosion rate and the composition of the resulting corrosion layer. Due to the complexity of this process it is almost impossible to predict the parameters that are most important and whether some parameters have a synergistic effect on the corrosion rate. Artificial neural networks are a mathematical tool that can be used to approximate and analyse non-linear problems with multiple inputs. In this work we present the first analysis of corrosion data obtained using this method, which reveals that CO2 and the composition of the buffer system play a crucial role in the corrosion of magnesium, whereas O2, proteins and temperature play a less prominent role. PMID:23470548

  3. Practical application of artificial neural networks in the neurosciences

    NASA Astrophysics Data System (ADS)

    Pinti, Antonio

    1995-04-01

    This article presents a practical application of artificial multi-layer perceptron (MLP) neural networks in neurosciences. The data that are processed are labeled data from the visual analysis of electrical signals of human sleep. The objective of this work is to automatically classify into sleep stages the electrophysiological signals recorded from electrodes placed on a sleeping patient. Two large data bases were designed by experts in order to realize this study. One data base was used to train the network and the other to test its generalization capacity. The classification results obtained with the MLP network were compared to a type K nearest neighbor Knn non-parametric classification method. The MLP network gave a better result in terms of classification than the Knn method. Both classification techniques were implemented on a transputer system. With both networks in their final configuration, the MLP network was 160 times faster than the Knn model in classifying a sleep period.

  4. Artificial neural network modeling of plasmonic transmission lines.

    PubMed

    Andrawis, Robert R; Swillam, Mohamed A; El-Gamal, Mohamed A; Soliman, Ezzeldin A

    2016-04-01

    In this paper, new models based on an artificial neural network (ANN) are developed to predict the propagation characteristics of plasmonic nanostrip and coupled nanostrips transmission lines. The trained ANNs are capable of providing the required propagation characteristics with good accuracy and almost instantaneously. The nonlinear mapping performed by the trained ANNs is written as closed-form expressions, which facilitate the direct use of the results obtained in this research. The propagation characteristics of the investigated transmission lines include the effective refractive index and the characteristic impedance. The time needed to simulate 1000 different versions of the transmission line structure is about 48 h, using a full-wave electromagnetic solver compared to 3 s using the developed ANN model. PMID:27139685

  5. Artificial neural networks for document analysis and recognition.

    PubMed

    Marinai, Simone; Gori, Marco; Soda, Giovanni; Society, Computer

    2005-01-01

    Artificial neural networks have been extensively applied to document analysis and recognition. Most efforts have been devoted to the recognition of isolated handwritten and printed characters with widely recognized successful results. However, many other document processing tasks, like preprocessing, layout analysis, character segmentation, word recognition, and signature verification, have been effectively faced with very promising results. This paper surveys the most significant problems in the area of offline document image processing, where connectionist-based approaches have been applied. Similarities and differences between approaches belonging to different categories are discussed. A particular emphasis is given on the crucial role of prior knowledge for the conception of both appropriate architectures and learning algorithms. Finally, the paper provides a critical analysis on the reviewed approaches and depicts the most promising research guidelines in the field. In particular, a second generation of connectionist-based models are foreseen which are based on appropriate graphical representations of the learning environment. PMID:15628266

  6. Inflow forecasting using Artificial Neural Networks for reservoir operation

    NASA Astrophysics Data System (ADS)

    Chiamsathit, Chuthamat; Adeloye, Adebayo J.; Bankaru-Swamy, Soundharajan

    2016-05-01

    In this study, multi-layer perceptron (MLP) artificial neural networks have been applied to forecast one-month-ahead inflow for the Ubonratana reservoir, Thailand. To assess how well the forecast inflows have performed in the operation of the reservoir, simulations were carried out guided by the systems rule curves. As basis of comparison, four inflow situations were considered: (1) inflow known and assumed to be the historic (Type A); (2) inflow known and assumed to be the forecast (Type F); (3) inflow known and assumed to be the historic mean for month (Type M); and (4) inflow is unknown with release decision only conditioned on the starting reservoir storage (Type N). Reservoir performance was summarised in terms of reliability, resilience, vulnerability and sustainability. It was found that Type F inflow situation produced the best performance while Type N was the worst performing. This clearly demonstrates the importance of good inflow information for effective reservoir operation.

  7. Modelling of a fluidized bed dryer using artificial neural network

    SciTech Connect

    Balasubramanian, A.; Ramachandra Rao, V.S.; Panda, R.C.

    1996-10-01

    Proper modelling of a fluidized bed dryer (FBD) is important to design model based control strategies. A FBD is a nonlinear multivariable system with nonminimum phase characteristics. Due to the complexities in FBD conventional Modelling techniques are cumbersome. Artificial neural network (ANN) with its inherent ability to learn and absorb nonlinearities, presents itself as a convenient tool for modelling such systems. In this work, an ANN model for a continuous drying FBD is presented. A three layer fully connected feedforward network with three inputs and two outputs is used. A back propagation learning algorithm is employed to train the network. The training data is obtained from computer simulation of a FBD model from published literature. The trained network is evaluated using randomly generated data as input and observed to predict the behavior of FBD adequately.

  8. Representation and learning of nonlinear compliance using neural nets

    SciTech Connect

    Asada, Haruhiko . Dept. of Mechanical Engineering)

    1993-12-01

    A new approach to compliant motion control using neural networks is presented. In the paper, compliance is treated as a nonlinear mapping from a measured force to a corrected motion. The nonlinear mapping by a multilayer neural network is represented, which allows us to deal with complex control strategies that cannot be represented by linear compliance, such as in stiffness and damping control.

  9. Simulation of an array-based neural net model

    NASA Technical Reports Server (NTRS)

    Barnden, John A.

    1987-01-01

    Research in cognitive science suggests that much of cognition involves the rapid manipulation of complex data structures. However, it is very unclear how this could be realized in neural networks or connectionist systems. A core question is: how could the interconnectivity of items in an abstract-level data structure be neurally encoded? The answer appeals mainly to positional relationships between activity patterns within neural arrays, rather than directly to neural connections in the traditional way. The new method was initially devised to account for abstract symbolic data structures, but it also supports cognitively useful spatial analogue, image-like representations. As the neural model is based on massive, uniform, parallel computations over 2D arrays, the massively parallel processor is a convenient tool for simulation work, although there are complications in using the machine to the fullest advantage. An MPP Pascal simulation program for a small pilot version of the model is running.

  10. A novel technology for fabricating customizable VLSI artificial neural network chips

    SciTech Connect

    Fu, C.Y.; Law, B.; Chapline, G.; Swenson, D.

    1992-02-05

    This paper describes an implementation of hardware neural networks using highly linear thin-film resistor technology and an 8-bit binary weight circuit to produce customizable artificial neural network chips and systems. These neural networks are programmed using precision laser cutting and deposition. The fast turnaround of laser-based customization allows us to explore different neural network architectures and to rapidly program the synaptic weights. Our customizable chip allows us to expand an artificial network laterally and vertically. This flexibility permits us to build very large neural network systems.

  11. Prediction of Austenite Formation Temperatures Using Artificial Neural Networks

    NASA Astrophysics Data System (ADS)

    Schulze, P.; Schmidl, E.; Grund, T.; Lampke, T.

    2016-03-01

    For the modeling and design of heat treatments, in consideration of the development/ transformation of the microstructure, different material data depending on the chemical composition, the respective microstructure/phases and the temperature are necessary. Material data are, e.g. the thermal conductivity, heat capacity, thermal expansion and transformation data etc. The quality of thermal simulations strongly depends on the accuracy of the material data. For many materials, the required data - in particular for different microstructures and temperatures - are rare in the literature. In addition, a different chemical composition within the permitted limits of the considered steel alloy cannot be predicted. A solution for this problem is provided by the calculation of material data using Artificial Neural Networks (ANN). In the present study, the start and finish temperatures of the transformation from the bcc lattice to the fcc lattice structure of hypoeutectoid steels are calculated using an Artificial Neural Network. An appropriate database containing different transformation temperatures (austenite formation temperatures) to train the ANN is selected from the literature. In order to find a suitable feedforward network, the network topologies as well as the activation functions of the hidden layers are varied and subsequently evaluated in terms of the prediction accuracy. The transformation temperatures calculated by the ANN exhibit a very good compliance compared to the experimental data. The results show that the prediction performance is even higher compared to classical empirical equations such as Andrews or Brandis. Therefore, it can be assumed that the presented ANN is a convenient tool to distinguish between bcc and fcc phases in hypoeutectoid steels.

  12. Bootstrapped neural nets versus regression kriging in the digital mapping of pedological attributes: the automatic and time-consuming perspectives

    NASA Astrophysics Data System (ADS)

    Langella, Giuliano; Basile, Angelo; Bonfante, Antonello; Manna, Piero; Terribile, Fabio

    2013-04-01

    Digital soil mapping procedures are widespread used to build two-dimensional continuous maps about several pedological attributes. Our work addressed a regression kriging (RK) technique and a bootstrapped artificial neural network approach in order to evaluate and compare (i) the accuracy of prediction, (ii) the susceptibility of being included in automatic engines (e.g. to constitute web processing services), and (iii) the time cost needed for calibrating models and for making predictions. Regression kriging is maybe the most widely used geostatistical technique in the digital soil mapping literature. Here we tried to apply the EBLUP regression kriging as it is deemed to be the most statistically sound RK flavor by pedometricians. An unusual multi-parametric and nonlinear machine learning approach was accomplished, called BAGAP (Bootstrap aggregating Artificial neural networks with Genetic Algorithms and Principal component regression). BAGAP combines a selected set of weighted neural nets having specified characteristics to yield an ensemble response. The purpose of applying these two particular models is to ascertain whether and how much a more cumbersome machine learning method could be much promising in making more accurate/precise predictions. Being aware of the difficulty to handle objects based on EBLUP-RK as well as BAGAP when they are embedded in environmental applications, we explore the susceptibility of them in being wrapped within Web Processing Services. Two further kinds of aspects are faced for an exhaustive evaluation and comparison: automaticity and time of calculation with/without high performance computing leverage.

  13. Training Spiking Neural Models Using Artificial Bee Colony

    PubMed Central

    Vazquez, Roberto A.; Garro, Beatriz A.

    2015-01-01

    Spiking neurons are models designed to simulate, in a realistic manner, the behavior of biological neurons. Recently, it has been proven that this type of neurons can be applied to solve pattern recognition problems with great efficiency. However, the lack of learning strategies for training these models do not allow to use them in several pattern recognition problems. On the other hand, several bioinspired algorithms have been proposed in the last years for solving a broad range of optimization problems, including those related to the field of artificial neural networks (ANNs). Artificial bee colony (ABC) is a novel algorithm based on the behavior of bees in the task of exploring their environment to find a food source. In this paper, we describe how the ABC algorithm can be used as a learning strategy to train a spiking neuron aiming to solve pattern recognition problems. Finally, the proposed approach is tested on several pattern recognition problems. It is important to remark that to realize the powerfulness of this type of model only one neuron will be used. In addition, we analyze how the performance of these models is improved using this kind of learning strategy. PMID:25709644

  14. Training spiking neural models using artificial bee colony.

    PubMed

    Vazquez, Roberto A; Garro, Beatriz A

    2015-01-01

    Spiking neurons are models designed to simulate, in a realistic manner, the behavior of biological neurons. Recently, it has been proven that this type of neurons can be applied to solve pattern recognition problems with great efficiency. However, the lack of learning strategies for training these models do not allow to use them in several pattern recognition problems. On the other hand, several bioinspired algorithms have been proposed in the last years for solving a broad range of optimization problems, including those related to the field of artificial neural networks (ANNs). Artificial bee colony (ABC) is a novel algorithm based on the behavior of bees in the task of exploring their environment to find a food source. In this paper, we describe how the ABC algorithm can be used as a learning strategy to train a spiking neuron aiming to solve pattern recognition problems. Finally, the proposed approach is tested on several pattern recognition problems. It is important to remark that to realize the powerfulness of this type of model only one neuron will be used. In addition, we analyze how the performance of these models is improved using this kind of learning strategy. PMID:25709644

  15. Didactic Strategy Discussion Based on Artificial Neural Networks Results.

    NASA Astrophysics Data System (ADS)

    Andina, D.; Bermúdez-Valbuena, R.

    2009-04-01

    Artificial Neural Networks (ANNs) are a mathematical model of the main known characteristics of biological brian dynamics. ANNs inspired in biological reality have been useful to design machines that show some human-like behaviours. Based on them, many experimentes have been succesfully developed emulating several biologial neurons characteristics, as learning how to solve a given problem. Sometimes, experimentes on ANNs feedback to biology and allow advances in understanding the biological brian behaviour, allowing the proposal of new therapies for medical problems involving neurons performing. Following this line, the author present results on artificial learning on ANN, and interpret them aiming to reinforce one of this two didactic estrategies to learn how to solve a given difficult task: a) To train with clear, simple, representative examples and feel confidence in brian generalization capabilities to achieve succes in more complicated cases. b) To teach with a set of difficult cases of the problem feeling confidence that the brian will efficiently solve the rest of cases if it is able to solve the difficult ones. Results may contribute in the discussion of how to orientate the design innovative succesful teaching strategies in the education field.

  16. Application of an artificial neural network to pump card diagnosis

    SciTech Connect

    Ashenayi, K. ); Lea, J.F. ); Kemp, F. , Dallas, TX ); Nazi, G.A.

    1994-12-01

    Beam pumping is the most frequently used artificial-lift technique for oil production. Downhole pump cards are used to evaluate performance of the pumping unit. Pump cards can be generated from surface dynamometer cards using a 1D wave equation with viscous damping, as suggested by Gibbs and Neely. Pump cards contain significant information describing the behavior of the pump. However, interpretation of these cards is tedious and time-consuming; hence, an automated system capable of interpreting these cards could speed interpretation and warn of pump failures. This work presents the results of a DOS-based computer program capable of correctly classifying pump cards. The program uses a hybrid artificial neural network (ANN) to identify significant features of the pump card. The hybrid ANN uses classical and sinusoidal perceptrons. The network is trained using an error-back-propagation technique. The program correctly identified pump problems for more than 180 different training and test pump cards. The ANN takes a total of 80 data points as input. Sixty data points are collected from the pump card perimeter, and the remaining 20 data points represent the slope at selected points on the pump card perimeter. Pump problem conditions are grouped into 11 distinct classes. The network is capable of identifying one or more of these problem conditions for each pump card. Eight examples are presented and discussed.

  17. An artificial neural network based matching metric for iris identification

    NASA Astrophysics Data System (ADS)

    Broussard, Randy P.; Kennell, Lauren R.; Ives, Robert W.; Rakvic, Ryan N.

    2008-02-01

    The iris is currently believed to be the most accurate biometric for human identification. The majority of fielded iris identification systems are based on the highly accurate wavelet-based Daugman algorithm. Another promising recognition algorithm by Ives et al uses Directional Energy features to create the iris template. Both algorithms use Hamming distance to compare a new template to a stored database. Hamming distance is an extremely fast computation, but weights all regions of the iris equally. Work from multiple authors has shown that different regions of the iris contain varying levels of discriminatory information. This research evaluates four post-processing similarity metrics for accuracy impacts on the Directional Energy and wavelets based algorithms. Each metric builds on the Hamming distance method in an attempt to use the template information in a more salient manner. A similarity metric extracted from the output stage of a feed-forward multi-layer perceptron artificial neural network demonstrated the most promise. Accuracy tables and ROC curves of tests performed on the publicly available Chinese Academy of Sciences Institute of Automation database show that the neural network based distance achieves greater accuracy than Hamming distance at every operating point, while adding less than one percent computational overhead.

  18. Detection of interplanetary activity using artificial neural networks

    NASA Astrophysics Data System (ADS)

    Gothoskar, Pradeep; Khobragade, Shyam

    1995-12-01

    Early detection of interplanetary activity is important when attempting to associate, with better accuracy, interplanetary phenomena with solar activity and geomagnetic disturbances. However, for a large number of interplanetary observations to be done every day, extensive data analysis is required, leading to a delay in the detection of transient interplanetary activity. In particular, the interplanetary scintillation (IPS) observations done with Ooty Radio Telescope (ORT) need extensive human effort to reduce the data and to model, often subjectively, the scintillation power spectra. We have implemented an artificial neural network (ANN) to detect interplanetary activity using the power spectrum scintillation. The ANN was trained to detect the disturbed power spectra, used as an indicator of the interplanetary activity, and to recognize normal and strong scattering spectra from a large data base of IPS spectra. The coincidence efficiency of classification by the network compared with the experts' judgement to detect the normal, disturbed and strong scattering spectra was found to be greater than 80 per cent. The neural network, when applied during the IPS mapping programme to provide early indication of interplanetary activity, would significantly help the ongoing efforts to predict geomagnetic disturbances.

  19. Classification of Images Acquired with Colposcopy Using Artificial Neural Networks

    PubMed Central

    Simões, Priscyla W; Izumi, Narjara B; Casagrande, Ramon S; Venson, Ramon; Veronezi, Carlos D; Moretti, Gustavo P; da Rocha, Edroaldo L; Cechinel, Cristian; Ceretta, Luciane B; Comunello, Eros; Martins, Paulo J; Casagrande, Rogério A; Snoeyer, Maria L; Manenti, Sandra A

    2014-01-01

    OBJECTIVE To explore the advantages of using artificial neural networks (ANNs) to recognize patterns in colposcopy to classify images in colposcopy. PURPOSE Transversal, descriptive, and analytical study of a quantitative approach with an emphasis on diagnosis. The training test e validation set was composed of images collected from patients who underwent colposcopy. These images were provided by a gynecology clinic located in the city of Criciúma (Brazil). The image database (n = 170) was divided; 48 images were used for the training process, 58 images were used for the tests, and 64 images were used for the validation. A hybrid neural network based on Kohonen self-organizing maps and multilayer perceptron (MLP) networks was used. RESULTS After 126 cycles, the validation was performed. The best results reached an accuracy of 72.15%, a sensibility of 69.78%, and a specificity of 68%. CONCLUSION Although the preliminary results still exhibit an average efficiency, the present approach is an innovative and promising technique that should be deeply explored in the context of the present study. PMID:25374454

  20. Clustering proteins into families using artificial neural networks.

    PubMed

    Ferrán, E A; Ferrara, P

    1992-02-01

    An artificial neural network was used to cluster proteins into families. The network, composed of 7 x 7 neurons, was trained with the Kohonen unsupervised learning algorithm using, as inputs, matrix patterns derived from the bipeptide composition of 447 proteins, belonging to 13 different families. As a result of the training, and without any a priori indication of the number or composition of the expected families, the network self-organized the activation of its neurons into topologically ordered maps in which almost all the proteins (96.7%) were correctly clustered into the corresponding families. In a second computational experiment, a similar network was trained with one family of the previous learning set (76 cytochrome c sequences). The new neural map clustered these proteins into 25 different neurons (five in the first experiment), wherein phylogenetically related sequences were positioned close to each other. This result shows that the network can adapt the clustering resolution to the complexity of the learning set, a useful feature when working with an unknown number of clusters. Although the learning stage is time consuming, once the topological map is obtained, the classification of new proteins is very fast. Altogether, our results suggest that this novel approach may be a useful tool to organize the search for homologies in large macromolecular databases. PMID:1314686

  1. Reliability and risk analysis using artificial neural networks

    SciTech Connect

    Robinson, D.G.

    1995-12-31

    This paper discusses preliminary research at Sandia National Laboratories into the application of artificial neural networks for reliability and risk analysis. The goal of this effort is to develop a reliability based methodology that captures the complex relationship between uncertainty in material properties and manufacturing processes and the resulting uncertainty in life prediction estimates. The inputs to the neural network model are probability density functions describing system characteristics and the output is a statistical description of system performance. The most recent application of this methodology involves the comparison of various low-residue, lead-free soldering processes with the desire to minimize the associated waste streams with no reduction in product reliability. Model inputs include statistical descriptions of various material properties such as the coefficients of thermal expansion of solder and substrate. Consideration is also given to stochastic variation in the operational environment to which the electronic components might be exposed. Model output includes a probabilistic characterization of the fatigue life of the surface mounted component.

  2. A radial basis function neural network based on artificial immune systems for remote sensing image classification

    NASA Astrophysics Data System (ADS)

    Yan, Qin; Zhong, Yanfei

    2008-12-01

    The radial basis function (RBF) neural network is a powerful method for remote sensing image classification. It has a simple architecture and the learning algorithm corresponds to the solution of a linear regression problem, resulting in a fast training process. The main drawback of this strategy is the requirement of an efficient algorithm to determine the number, position, and dispersion of the RBF. Traditional methods to determine the centers are: randomly choose input vectors from the training data set; vectors obtained from unsupervised clustering algorithms, such as k-means, applied to the input data. These conduce that traditional RBF neural network is sensitive to the center initialization. In this paper, the artificial immune network (aiNet) model, a new computational intelligence based on artificial immune networks (AIN), is applied to obtain appropriate centers for remote sensing image classification. In the aiNet-RBF algorihtm, each input pattern corresonds to an antigenic stimulus, while each RBF candidate center is considered to be an element, or cell, of the immune network model. The steps are as follows: A set of candidate centers is initialized at random, where the initial number of candidates and their positions is not crucial to the performance. Then, the clonal selection principle will control which candidates will be selected and how they will be upadated. Note that the clonal selection principle will be responsible for how the centers will represent the training data set. Finally, the immune network will identify and eliminate or suppress self-recognizing individuals to control the number of candidate centers. After the above learning phase, the aiNet network centers represent internal images of the inuput patterns presented to it. The algorithm output is taken to be the matrix of memory cells' coordinates that represent the final centers to be adopted by the RBF network. The stopping criterion of the proposed algorithm is given by a pre

  3. The utilization of neural nets in populating an object-oriented database

    NASA Technical Reports Server (NTRS)

    Campbell, William J.; Hill, Scott E.; Cromp, Robert F.

    1989-01-01

    Existing NASA supported scientific data bases are usually developed, managed and populated in a tedious, error prone and self-limiting way in terms of what can be described in a relational Data Base Management System (DBMS). The next generation Earth remote sensing platforms (i.e., Earth Observation System, (EOS), will be capable of generating data at a rate of over 300 Mbs per second from a suite of instruments designed for different applications. What is needed is an innovative approach that creates object-oriented databases that segment, characterize, catalog and are manageable in a domain-specific context and whose contents are available interactively and in near-real-time to the user community. Described here is work in progress that utilizes an artificial neural net approach to characterize satellite imagery of undefined objects into high-level data objects. The characterized data is then dynamically allocated to an object-oriented data base where it can be reviewed and assessed by a user. The definition, development, and evolution of the overall data system model are steps in the creation of an application-driven knowledge-based scientific information system.

  4. Automatic Recognition of Ocean Structures from Satellite Images by Means of Neural Nets and Expert Systems

    NASA Astrophysics Data System (ADS)

    Guindos-Rojas, F.; Cantón-Garbín, M.; Torres-Arriaza, J. A.; Peralta-López, M.; Piedra-Fernández, J. A.; Molina-Martínez, A.

    2004-09-01

    Images received from satellites have became a great source of information about our environment. This is raw information that needs experts to make the most of it, but there are not many experts and the work is too much. The solution to this problem is the compilation of human experience into automatic systems that could do the same work. We depict here the structure for a knowledge based system capable of taking the place of human experts when it is properly trained. This structure has been used to build an automatic recognition system that process AVHRR images from NOAA satellites to detect and locate ocean phenomena of interest like upwellings, eddies and island wakes. The model covers every phase of the process from the source image, once it is corrected and geocoded, to the final features map. In the most delicate phase of the process pipeline, artificial neural nets and rule-based expert systems are used in a parallel redundant way so results can be validated by comparing the outcome of both subsystems. The automatic knowledge driven image processing system has been trained with ubiquitous and localized information and has proved his qualities with images of Canary Island, Mediterranean Sea and Cantabric and Portuguese coasts.

  5. Larger bases and mixed analog/digital neural nets

    SciTech Connect

    Beiu, V.

    1998-12-31

    The paper overviews results dealing with the approximation capabilities of neural networks, and bounds on the size of threshold gate circuits. Based on an explicit numerical algorithm for Kolmogorov`s superpositions the authors show that minimum size neural networks--for implementing any Boolean function--have the identity function as the activation function. Conclusions and several comments on the required precision are ending the paper.

  6. Communications and control for electric power systems: Power system stability applications of artificial neural networks

    NASA Technical Reports Server (NTRS)

    Toomarian, N.; Kirkham, Harold

    1994-01-01

    This report investigates the application of artificial neural networks to the problem of power system stability. The field of artificial intelligence, expert systems, and neural networks is reviewed. Power system operation is discussed with emphasis on stability considerations. Real-time system control has only recently been considered as applicable to stability, using conventional control methods. The report considers the use of artificial neural networks to improve the stability of the power system. The networks are considered as adjuncts and as replacements for existing controllers. The optimal kind of network to use as an adjunct to a generator exciter is discussed.

  7. Hybrid neural net and rule based system for boiler monitoring and diagnosis

    SciTech Connect

    Kraft, T.; Okagaki, K.; Ishii, R.; Surko, P. ); Brandon, A.; DeWeese, A.; Peterson, S.; Bjordal, R. )

    1991-01-01

    A fully recurrent neural net is coupled with a rule-based expert system in this operator adviser system. The neural net has been trained to recognize normal high-efficiency operating behavior of the power plant boiler, and the rule-based expert system diagnoses problems and suggests maintenance and/or operator actions when the boiler strays outside the envelope of normal operating conditions. The fully recurrent neural net provides an accurate model of a boiler even when the load demand is changing rapidly and the boiler operating conditions varying over a wide range. The hybrid system has been quicker and easier to generate than a strictly rule-based one, and has been designed to be more easily portable to other units This paper describes the ongoing development work for monitoring SDGE and E's South Bay Plant, Unit. 1.

  8. Time series predictions with neural nets: Application to airborne pollen forecasting

    NASA Astrophysics Data System (ADS)

    Arizmendi, C. M.; Sanchez, J. R.; Ramos, N. E.; Ramos, G. I.

    1993-09-01

    Pollen allergy is a common disease causing rhinoconjunctivitis (hay fever) in 5 10% of the population. Medical studies have indicated that pollen related diseases could be highly reduced if future pollen contents in the air could be predicted. In this work we have developed a new forecasting method that applies the ability of neural nets to predict the future behaviour of chaotic systems in order to make accurate predictions of the airborne pollen concentration. The method requires that the neural net be fed with non-zero values, which restricts the method predictions to the period following the start of pollen flight. The operational method outlined here constitutes a different point of view with respect to the more generally used forecasts of time series analysis, which require input of many meteorological parameters. Excellent forecasts were obtained training a neural net by using only the time series pollen concentration values.

  9. Surrogate modeling of deformable joint contact using artificial neural networks.

    PubMed

    Eskinazi, Ilan; Fregly, Benjamin J

    2015-09-01

    Deformable joint contact models can be used to estimate loading conditions for cartilage-cartilage, implant-implant, human-orthotic, and foot-ground interactions. However, contact evaluations are often so expensive computationally that they can be prohibitive for simulations or optimizations requiring thousands or even millions of contact evaluations. To overcome this limitation, we developed a novel surrogate contact modeling method based on artificial neural networks (ANNs). The method uses special sampling techniques to gather input-output data points from an original (slow) contact model in multiple domains of input space, where each domain represents a different physical situation likely to be encountered. For each contact force and torque output by the original contact model, a multi-layer feed-forward ANN is defined, trained, and incorporated into a surrogate contact model. As an evaluation problem, we created an ANN-based surrogate contact model of an artificial tibiofemoral joint using over 75,000 evaluations of a fine-grid elastic foundation (EF) contact model. The surrogate contact model computed contact forces and torques about 1000 times faster than a less accurate coarse grid EF contact model. Furthermore, the surrogate contact model was seven times more accurate than the coarse grid EF contact model within the input domain of a walking motion. For larger input domains, the surrogate contact model showed the expected trend of increasing error with increasing domain size. In addition, the surrogate contact model was able to identify out-of-contact situations with high accuracy. Computational contact models created using our proposed ANN approach may remove an important computational bottleneck from musculoskeletal simulations or optimizations incorporating deformable joint contact models. PMID:26220591

  10. Nighttime cloud properties retrieval using MODIS and artificial neural networks

    NASA Astrophysics Data System (ADS)

    Pérez, J. C.; Cerdeña, A.; González, A.

    The aim of this work is to develop a methodology for inferring water cloud macro and microphysical properties from nighttime MODIS imagery This method is based on the inversion of a theoretical radiative transfer model that simulates the radiances detected in each of the sensor infrared bands In this case LibRadtran package Mayer and Kylling 2005 was used which allows us the calculation of the radiation field in the Earth s atmosphere given a specified set of atmospheric and cloud parameters However due to the complexity of this forward model its inversion cannot be performed in an analytical way To accomplish this task we propose an operational technique based on artificial neural networks ANNs whose main characteristic is the ability to retrieve cloud properties much faster than conventional methods Platnick et al 2003 Gonzalez et al 2002 Thus the procedure is as follows Using the theoretical radiative model a Look Up Table LUT is generated for a great variety of surface cloud and atmospheric conditions This dataset is divided randomly into a training set two-thirds of the items and a test set one third of the items which are used to train the neural network in order to fit the inversion problem In this study multilayer perceptrons MLPs with two hidden layers are used and the backpropagation with momentum method is used in the training process Furthermore to accelerate the convergence of ANN s evolutionary techniques are used to search the ANN configuration that provides the best fit Furthermore in order to check the

  11. Non-Lipschitzian dynamics for neural net modelling

    NASA Technical Reports Server (NTRS)

    Zak, Michail

    1989-01-01

    Failure of the Lipschitz condition in unstable equilibrium points of dynamical systems leads to a multiple-choice response to an initial deterministic input. The evolution of such systems is characterized by a special type of unpredictability measured by unbounded Liapunov exponents. Possible relation of these systems to future neural networks is discussed.

  12. Goal-seeking neural net for recall and recognition

    NASA Astrophysics Data System (ADS)

    Omidvar, Omid M.

    1990-07-01

    Neural networks have been used to mimic cognitive processes which take place in animal brains. The learning capability inherent in neural networks makes them suitable candidates for adaptive tasks such as recall and recognition. The synaptic reinforcements create a proper condition for adaptation, which results in memorization, formation of perception, and higher order information processing activities. In this research a model of a goal seeking neural network is studied and the operation of the network with regard to recall and recognition is analyzed. In these analyses recall is defined as retrieval of stored information where little or no matching is involved. On the other hand recognition is recall with matching; therefore it involves memorizing a piece of information with complete presentation. This research takes the generalized view of reinforcement in which all the signals are potential reinforcers. The neuronal response is considered to be the source of the reinforcement. This local approach to adaptation leads to the goal seeking nature of the neurons as network components. In the proposed model all the synaptic strengths are reinforced in parallel while the reinforcement among the layers is done in a distributed fashion and pipeline mode from the last layer inward. A model of complex neuron with varying threshold is developed to account for inhibitory and excitatory behavior of real neuron. A goal seeking model of a neural network is presented. This network is utilized to perform recall and recognition tasks. The performance of the model with regard to the assigned tasks is presented.

  13. Vector control of wind turbine on the basis of the fuzzy selective neural net*

    NASA Astrophysics Data System (ADS)

    Engel, E. A.; Kovalev, I. V.; Engel, N. E.

    2016-04-01

    An article describes vector control of wind turbine based on fuzzy selective neural net. Based on the wind turbine system’s state, the fuzzy selective neural net tracks an maximum power point under random perturbations. Numerical simulations are accomplished to clarify the applicability and advantages of the proposed vector wind turbine’s control on the basis of the fuzzy selective neuronet. The simulation results show that the proposed intelligent control of wind turbine achieves real-time control speed and competitive performance, as compared to a classical control model with PID controllers based on traditional maximum torque control strategy.

  14. A neural net based architecture for the segmentation of mixed gray-level and binary pictures

    NASA Technical Reports Server (NTRS)

    Tabatabai, Ali; Troudet, Terry P.

    1991-01-01

    A neural-net-based architecture is proposed to perform segmentation in real time for mixed gray-level and binary pictures. In this approach, the composite picture is divided into 16 x 16 pixel blocks, which are identified as character blocks or image blocks on the basis of a dichotomy measure computed by an adaptive 16 x 16 neural net. For compression purposes, each image block is further divided into 4 x 4 subblocks; a one-bit nonparametric quantizer is used to encode 16 x 16 character and 4 x 4 image blocks; and the binary map and quantizer levels are obtained through a neural net segmentor over each block. The efficiency of the neural segmentation in terms of computational speed, data compression, and quality of the compressed picture is demonstrated. The effect of weight quantization is also discussed. VLSI implementations of such adaptive neural nets in CMOS technology are described and simulated in real time for a maximum block size of 256 pixels.

  15. Designing Artificial Neural Networks Using Particle Swarm Optimization Algorithms

    PubMed Central

    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

  16. Honey characterization using computer vision system and artificial neural networks.

    PubMed

    Shafiee, Sahameh; Minaei, Saeid; Moghaddam-Charkari, Nasrollah; Barzegar, Mohsen

    2014-09-15

    This paper reports the development of a computer vision system (CVS) for non-destructive characterization of honey based on colour and its correlated chemical attributes including ash content (AC), antioxidant activity (AA), and total phenolic content (TPC). Artificial neural network (ANN) models were applied to transform RGB values of images to CIE L*a*b* colourimetric measurements and to predict AC, TPC and AA from colour features of images. The developed ANN models were able to convert RGB values to CIE L*a*b* colourimetric parameters with low generalization error of 1.01±0.99. In addition, the developed models for prediction of AC, TPC and AA showed high performance based on colour parameters of honey images, as the R(2) values for prediction were 0.99, 0.98, and 0.87, for AC, AA and TPC, respectively. The experimental results show the effectiveness and possibility of applying CVS for non-destructive honey characterization by the industry. PMID:24767037

  17. Designing Artificial Neural Networks Using Particle Swarm Optimization Algorithms.

    PubMed

    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

  18. Signal processing using artificial neural network for BOTDA sensor system.

    PubMed

    Azad, Abul Kalam; Wang, Liang; Guo, Nan; Tam, Hwa-Yaw; Lu, Chao

    2016-03-21

    We experimentally demonstrate the use of artificial neural network (ANN) to process sensing signals obtained from Brillouin optical time domain analyzer (BOTDA). The distributed temperature information is extracted directly from the local Brillouin gain spectra (BGSs) along the fiber under test without the process of determination of Brillouin frequency shift (BFS) and hence conversion from BFS to temperature. Unlike our previous work for short sensing distance where ANN is trained by measured BGSs, here we employ ideal BGSs with different linewidths to train the ANN in order to take the linewidth variation due to different conditions from the training and testing phases into account, making it feasible for long distance sensing. Moreover, the performance of ANN is compared with other two techniques, Lorentzian curve fitting and cross-correlation method, and our results show that ANN has higher accuracy and larger tolerance to measurement error, especially at large frequency scanning step. We also show that the temperature extraction from BOTDA measurements employing ANN is significantly faster than the other two approaches. Hence ANN can be an excellent alternative tool to process BGSs measured by BOTDA and obtain temperature distribution along the fiber, especially when large frequency scanning step is adopted to significantly reduce the measurement time but without sacrifice of sensing accuracy. PMID:27136863

  19. Automatic classification of DMSA scans using an artificial neural network

    NASA Astrophysics Data System (ADS)

    Wright, J. W.; Duguid, R.; Mckiddie, F.; Staff, R. T.

    2014-04-01

    DMSA imaging is carried out in nuclear medicine to assess the level of functional renal tissue in patients. This study investigated the use of an artificial neural network to perform diagnostic classification of these scans. Using the radiological report as the gold standard, the network was trained to classify DMSA scans as positive or negative for defects using a representative sample of 257 previously reported images. The trained network was then independently tested using a further 193 scans and achieved a binary classification accuracy of 95.9%. The performance of the network was compared with three qualified expert observers who were asked to grade each scan in the 193 image testing set on a six point defect scale, from ‘definitely normal’ to ‘definitely abnormal’. A receiver operating characteristic analysis comparison between a consensus operator, generated from the scores of the three expert observers, and the network revealed a statistically significant increase (α < 0.05) in performance between the network and operators. A further result from this work was that when suitably optimized, a negative predictive value of 100% for renal defects was achieved by the network, while still managing to identify 93% of the negative cases in the dataset. These results are encouraging for application of such a network as a screening tool or quality assurance assistant in clinical practice.

  20. Altimeter waveform parameters retrieval using Artificial Neural Networks

    NASA Astrophysics Data System (ADS)

    Swain, Debadatta; Sasamal, S. K.

    2012-07-01

    Waveform retracking and analysis methods form an integral part of any altimeter data processing to derive useable information. This is significant owing to the strong heterogeneity in the waveforms resulting of returns of altimeter pulses from uneven geographical features. The waveforms consisting of altimeter return pulses follow the Brown model over the deep oceans. However, the waveforms become rather complex when received over coastal and land regions owing to large scale inhomogeneities. The present work attempts to characterize altimeter return pulses (consisting of slope, amplitude and range) on the basis of the surface responsible for the echo followed by estimation of these waveform parameters based on an Artificial Neural Network Technique (ANN). An ANN is a non-linear parallel-distributed computer model highly effective for classification type of problems. ANNs are widely applied for pattern recognition since their non-linear characteristics makes them very suitable for application to processes with internal inhomogeneities. To demonstrate the technique, we have utilized JASON-2 high resolution waveform data over multiple passes spanning varied geographical topography covering open ocean, coasts, and in-land water bodies. The ANN model is formulated by first training and testing with data sets identified for various topography classifications. Following this, the model estimations are validated with actual altimeter returns forming the waveform, and that have not been used during the ANN model formulation process. The work aims to demonstrate the ANN technique for high resolution altimeter waveform analysis.

  1. Predicting concrete corrosion of sewers using artificial neural network.

    PubMed

    Jiang, Guangming; Keller, Jurg; Bond, Philip L; Yuan, Zhiguo

    2016-04-01

    Corrosion is often a major failure mechanism for concrete sewers and under such circumstances the sewer service life is largely determined by the progression of microbially induced concrete corrosion. The modelling of sewer processes has become possible due to the improved understanding of in-sewer transformation. Recent systematic studies about the correlation between the corrosion processes and sewer environment factors should be utilized to improve the prediction capability of service life by sewer models. This paper presents an artificial neural network (ANN)-based approach for modelling the concrete corrosion processes in sewers. The approach included predicting the time for the corrosion to initiate and then predicting the corrosion rate after the initiation period. The ANN model was trained and validated with long-term (4.5 years) corrosion data obtained in laboratory corrosion chambers, and further verified with field measurements in real sewers across Australia. The trained model estimated the corrosion initiation time and corrosion rates very close to those measured in Australian sewers. The ANN model performed better than a multiple regression model also developed on the same dataset. Additionally, the ANN model can serve as a prediction framework for sewer service life, which can be progressively improved and expanded by including corrosion rates measured in different sewer conditions. Furthermore, the proposed methodology holds promise to facilitate the construction of analytical models associated with corrosion processes of concrete sewers. PMID:26841228

  2. Predicting the intracellular water compartment using artificial neural network analysis.

    PubMed

    Mohamed, E I; Maiolo, C; Linder, R; Pöppl, S J; De Lorenzo, A

    2003-10-01

    Artificial neural networks (ANN) are used for a wide variety of data-processing applications such as predicting medical outcomes and classifying clinical data and patients. We investigated the applicability of an ANN for estimating the intracellular water compartment for a population of 104 healthy Italians ranging in age from 19 to 68 years. Anthropometric variables, bioelectric impedance analysis (BIA) variables, and reference values for intracellular water, measured using whole-body (40)K counting (ICW(K40)), were measured for all study participants. The anthropometric variables and the impedance index (height(2)/resistance) were fed to the ANN input layer, which produced as output the estimated values for intracellular water (ICW(ANN)). We also estimated intracellular water using a BIA formula for the same population (ICW(DeLorenzo)) and another for Caucasians (ICW(Gudivaka)). Errors in the estimations generated by ANN and the BIA equations were calculated as the root mean square error (RMSE). The mean (+/-SD) reference value (ICWK40) was 25.01+/-4.50 l, whereas the mean estimated value was 15.20+/-1.79 l (RMSE=11.06 l) when calculated using ICW(DeLorenzo), 18.07+/-1.14 l (RMSE=8.72 l) when using ICW(Gudivaka), and 25.01+/-2.74 l (RMSE=3.22 l) when using ICW(ANN). Based on these results, we deduce that the ANN algorithm is a more accurate predictor for reference ICW(K40) than BIA equations. PMID:14618426

  3. Artificial neural networks in laboratory medicine and medical outcome prediction.

    PubMed

    Tafeit, E; Reibnegger, G

    1999-09-01

    Since the early nineties the number of scientific papers reporting on artificial neural network (ANN) applications in medicine has been quickly increasing. In the present paper, we describe in some detail the architecture of network types used most frequently in ANN applications in the broad field of laboratory medicine and clinical chemistry, present a technique-structured review about the recent ANN applications in the field, and give information about the improvements of available ANN software packages. ANN applications are divided into two main classes: supervised and unsupervised methods. Most of the described supervised applications belong to the fields of medical diagnosis (n = 7) and outcome prediction (n = 9). Laboratory and clinical data are presented to multilayer feed-forward ANNs which are trained by the back propagation algorithm. Results are often better than those of traditional techniques such as linear discriminant analysis, classification and regression trees (CART), Cox regression analysis, logistic regression, clinical judgement or expert systems. Unsupervised ANN applications provide the ability of reducing the dimensionality of a dataset. Low-dimensional plots can be generated and visually understood and compared. Results are very similar to that of cluster analysis and factor analysis. The ability of Kohonen's self-organizing maps to generate 2D maps of molecule surface properties was successfully applied in drug design. PMID:10596951

  4. Superiority of artificial neural networks for a genetic classification procedure.

    PubMed

    Sant'Anna, I C; Tomaz, R S; Silva, G N; Nascimento, M; Bhering, L L; Cruz, C D

    2015-01-01

    The correct classification of individuals is extremely important for the preservation of genetic variability and for maximization of yield in breeding programs using phenotypic traits and genetic markers. The Fisher and Anderson discriminant functions are commonly used multivariate statistical techniques for these situations, which allow for the allocation of an initially unknown individual to predefined groups. However, for higher levels of similarity, such as those found in backcrossed populations, these methods have proven to be inefficient. Recently, much research has been devoted to developing a new paradigm of computing known as artificial neural networks (ANNs), which can be used to solve many statistical problems, including classification problems. The aim of this study was to evaluate the feasibility of ANNs as an evaluation technique of genetic diversity by comparing their performance with that of traditional methods. The discriminant functions were equally ineffective in discriminating the populations, with error rates of 23-82%, thereby preventing the correct discrimination of individuals between populations. The ANN was effective in classifying populations with low and high differentiation, such as those derived from a genetic design established from backcrosses, even in cases of low differentiation of the data sets. The ANN appears to be a promising technique to solve classification problems, since the number of individuals classified incorrectly by the ANN was always lower than that of the discriminant functions. We envisage the potential relevant application of this improved procedure in the genomic classification of markers to distinguish between breeds and accessions. PMID:26345924

  5. Potential energy surfaces fitted by artificial neural networks.

    PubMed

    Handley, Chris M; Popelier, Paul L A

    2010-03-18

    Molecular mechanics is the tool of choice for the modeling of systems that are so large or complex that it is impractical or impossible to model them by ab initio methods. For this reason there is a need for accurate potentials that are able to quickly reproduce ab initio quality results at the fraction of the cost. The interactions within force fields are represented by a number of functions. Some interactions are well understood and can be represented by simple mathematical functions while others are not so well understood and their functional form is represented in a simplistic manner or not even known. In the last 20 years there have been the first examples of a new design ethic, where novel and contemporary methods using machine learning, in particular, artificial neural networks, have been used to find the nature of the underlying functions of a force field. Here we appraise what has been achieved over this time and what requires further improvements, while offering some insight and guidance for the development of future force fields. PMID:20131763

  6. Statistical downscaling rainfall using artificial neural network: significantly wetter Bangkok?

    NASA Astrophysics Data System (ADS)

    Vu, Minh Tue; Aribarg, Thannob; Supratid, Siriporn; Raghavan, Srivatsan V.; Liong, Shie-Yui

    2015-08-01

    Artificial neural network (ANN) is an established technique with a flexible mathematical structure that is capable of identifying complex nonlinear relationships between input and output data. The present study utilizes ANN as a method of statistically downscaling global climate models (GCMs) during the rainy season at meteorological site locations in Bangkok, Thailand. The study illustrates the applications of the feed forward back propagation using large-scale predictor variables derived from both the ERA-Interim reanalyses data and present day/future GCM data. The predictors are first selected over different grid boxes surrounding Bangkok region and then screened by using principal component analysis (PCA) to filter the best correlated predictors for ANN training. The reanalyses downscaled results of the present day climate show good agreement against station precipitation with a correlation coefficient of 0.8 and a Nash-Sutcliffe efficiency of 0.65. The final downscaled results for four GCMs show an increasing trend of precipitation for rainy season over Bangkok by the end of the twenty-first century. The extreme values of precipitation determined using statistical indices show strong increases of wetness. These findings will be useful for policy makers in pondering adaptation measures due to flooding such as whether the current drainage network system is sufficient to meet the changing climate and to plan for a range of related adaptation/mitigation measures.

  7. Using artificial neural network tools to analyze microbial biomarker data

    SciTech Connect

    Brandt, C.C.; Schryver, J.C.; Almeida, J.S.; Pfiffner, S.M.; Palumbo, A.V.

    2004-03-17

    A major challenge in the successful implementation of bioremediation is understanding the structure of the indigenous microbial community and how this structure is affected by environmental conditions. Culture-independent approaches that use biomolecular markers have become the key to comparative microbial community analysis. However, the analysis of biomarkers from environmental samples typically generates a large number of measurements. The large number and complex nonlinear relationships among these measurements makes conventional linear statistical analysis of the data difficult. New data analysis tools are needed to help understand these data. We adapted artificial neural network (ANN) tools for relating changes in microbial biomarkers to geochemistry. ANNs are nonlinear pattern recognition methods that can learn from experience to improve their performance. We have successfully applied these techniques to the analysis of membrane lipids and nucleic acid biomarker data from both laboratory and field studies. Although ANNs typically outperform linear data analysis techniques, the user must be aware of several considerations and issues to ensure that analysis results are not misleading: (1) Overfitting, especially in small sample size data sets; (2) Model selection; (3) Interpretation of analysis results; and (4) Availability of tools (code). This poster summarizes approaches for addressing each of these issues. The objectives are: (1) Develop new nonlinear data analysis tools for relating microbial biomolecular markers to geochemical conditions; (2) Apply these nonlinear tools to field and laboratory studies relevant to the NABIR Program; and (3) Provide these tools and guidance in their use to other researchers.

  8. Classification of breast abnormalities using artificial neural network

    NASA Astrophysics Data System (ADS)

    Zaman, Nur Atiqah Kamarul; Rahman, Wan Eny Zarina Wan Abdul; Jumaat, Abdul Kadir; Yasiran, Siti Salmah

    2015-05-01

    Classification is the process of recognition, differentiation and categorizing objects into groups. Breast abnormalities are calcifications which are tumor markers that indicate the presence of cancer in the breast. The aims of this research are to classify the types of breast abnormalities using artificial neural network (ANN) classifier and to evaluate the accuracy performance using receiver operating characteristics (ROC) curve. The methods used in this research are ANN for breast abnormalities classifications and Canny edge detector as a feature extraction method. Previously the ANN classifier provides only the number of benign and malignant cases without providing information for specific cases. However in this research, the type of abnormality for each image can be obtained. The existing MIAS MiniMammographic database classified the mammogram images into three features only namely characteristic of background tissues, class of abnormality and radius of abnormality. However, in this research three other features are added-in. These three features are number of spots, area and shape of abnormalities. Lastly the performance of the ANN classifier is evaluated using ROC curve. It is found that ANN has an accuracy of 97.9% which is considered acceptable.

  9. Multiobjective analysis of a public wellfield using artificial neural networks

    USGS Publications Warehouse

    Coppola, E.A., Jr.; Szidarovszky, F.; Davis, D.; Spayd, S.; Poulton, M.M.; Roman, E.

    2007-01-01

    As competition for increasingly scarce ground water resources grows, many decision makers may come to rely upon rigorous multiobjective techniques to help identify appropriate and defensible policies, particularly when disparate stakeholder groups are involved. In this study, decision analysis was conducted on a public water supply wellfield to balance water supply needs with well vulnerability to contamination from a nearby ground water contaminant plume. With few alternative water sources, decision makers must balance the conflicting objectives of maximizing water supply volume from noncontaminated wells while minimizing their vulnerability to contamination from the plume. Artificial neural networks (ANNs) were developed with simulation data from a numerical ground water flow model developed for the study area. The ANN-derived state transition equations were embedded into a multiobjective optimization model, from which the Pareto frontier or trade-off curve between water supply and wellfield vulnerability was identified. Relative preference values and power factors were assigned to the three stakeholders, namely the company whose waste contaminated the aquifer, the community supplied by the wells, and the water utility company that owns and operates the wells. A compromise pumping policy that effectively balances the two conflicting objectives in accordance with the preferences of the three stakeholder groups was then identified using various distance-based methods. ?? 2006 National Ground Water Association.

  10. Prediction of Soil Deformation in Tunnelling Using Artificial Neural Networks

    PubMed Central

    Lai, Jinxing

    2016-01-01

    In the past few decades, as a new tool for analysis of the tough geotechnical problems, artificial neural networks (ANNs) have been successfully applied to address a number of engineering problems, including deformation due to tunnelling in various types of rock mass. Unlike the classical regression methods in which a certain form for the approximation function must be presumed, ANNs do not require the complex constitutive models. Additionally, it is traced that the ANN prediction system is one of the most effective ways to predict the rock mass deformation. Furthermore, it could be envisaged that ANNs would be more feasible for the dynamic prediction of displacements in tunnelling in the future, especially if ANN models are combined with other research methods. In this paper, we summarized the state-of-the-art and future research challenges of ANNs on the tunnel deformation prediction. And the application cases as well as the improvement of ANN models were also presented. The presented ANN models can serve as a benchmark for effective prediction of the tunnel deformation with characters of nonlinearity, high parallelism, fault tolerance, learning, and generalization capability. PMID:26819587

  11. Design The Cervical Cancer Detector Use The Artificial Neural Network

    NASA Astrophysics Data System (ADS)

    Intan Af'idah, Dwi; Didik Widianto, Eko; Setyawan, Budi

    2013-06-01

    Cancer is one of the contagious diseases that become a public health issue, both in the world and in Indonesia. In the world, 12% of all deaths caused by cancer and is the second killer after cardiovascular disease. Early detection using the IVA is a practical and inexpensive (only requiring acetic acid). However, the accuracy of the method is quite low, as it can not detect the stage of the cancer. While other methods have a better sensitivity than the IVA method, is a method of PAP smear. However, this method is relatively expensive, and requires an experienced pathologist-cytologist. According to the case above, Considered important to make the cancer cervics detector that is used to detect the abnormality and cervical cancer stage and consists of a digital microscope, as well as a computer application based on artificial neural network. The use of cervical cancer detector software and hardware are integrated each other. After the specifications met, the steps to design the cervical cancer detection are: Modifying a conventional microscope by adding a lens, image recording, and the lights, Programming the tools, designing computer applications, Programming features abnormality detection and staging of cancer.

  12. Application of artificial neural networks for prediction of photocatalytic reactor.

    PubMed

    Delnavaz, Mohammad

    2015-02-01

    In this paper, forecasting of kinetic constant and efficiency of photocatalytic process of TiO2 nano powder immobilized on light expanded clay aggregates (LECA) was investigated. Synthetic phenolic wastewater, which is toxic and not easily biodegradable, was selected as the pollutant. The efficiency of the process in various operation conditions, including initial phenol concentration, pH, TiO2 concentration, retention time, and UV lamp intensity, was then measured. The TiO2 nano powder was immobilized on LECA using slurry and sol-gel methods. Kinetics of photocatalytic reactions has been proposed to follow the Langmuir-Hinshelwood model in different initial phenol concentration and pH. Several steps of training and testing of the models were used to determine the appropriate architecture of the artificial neural network models (ANNs). The ANN-based models were found to provide an efficient and robust tool in predicting photocatalytic reactor efficiency and kinetic constant for treating phenolic compounds. PMID:25790514

  13. Spatiotemporal modeling of monthly soil temperature using artificial neural networks

    NASA Astrophysics Data System (ADS)

    Wu, Wei; Tang, Xiao-Ping; Guo, Nai-Jia; Yang, Chao; Liu, Hong-Bin; Shang, Yue-Feng

    2013-08-01

    Soil temperature data are critical for understanding land-atmosphere interactions. However, in many cases, they are limited at both spatial and temporal scales. In the current study, an attempt was made to predict monthly mean soil temperature at a depth of 10 cm using artificial neural networks (ANNs) over a large region with complex terrain. Gridded independent variables, including latitude, longitude, elevation, topographic wetness index, and normalized difference vegetation index, were derived from a digital elevation model and remote sensing images with a resolution of 1 km. The good performance and robustness of the proposed ANNs were demonstrated by comparisons with multiple linear regressions. On average, the developed ANNs presented a relative improvement of about 44 % in root mean square error, 70 % in mean absolute percentage error, and 18 % in coefficient of determination over classical linear models. The proposed ANN models were then applied to predict soil temperatures at unsampled locations across the study area. Spatiotemporal variability of soil temperature was investigated based on the obtained database. Future work will be needed to test the applicability of ANNs for estimating soil temperature at finer scales.

  14. A movement pattern generator model using artificial neural networks.

    PubMed

    Srinivasan, S; Gander, R E; Wood, H C

    1992-07-01

    Artificial neural networks (ANN's) allow a new approach to biological modeling. The main applications of ANN's have been geared towards the modeling of the association and learning mechanisms of the brain; only a few researchers have explored them for motor control. The fact that ANN's are based on biological systems indicates their potential application for a biological act such as locomotion. Towards this goal, we have developed a "movement pattern generator," using an ANN for generating periodic movement trajectories. This model is based on the concept of "central pattern generators." Jordan's sequential network, which is capable of learning sequences of patterns, was modified and used to generate several bipedal trajectories (or gaits), coded in task space, at different frequencies. The network model successfully learned all of the trajectories presented to it. The model has many attractive properties such as limit cycle behavior, generalization of trajectories and frequencies, phase maintenance, and fault tolerance. The movement pattern generator model is potentially applicable for improved understanding of animal locomotion and for use in legged robots and rehabilitation medicine. PMID:1516938

  15. A new evolutionary system for evolving artificial neural networks.

    PubMed

    Yao, X; Liu, Y

    1997-01-01

    This paper presents a new evolutionary system, i.e., EPNet, for evolving artificial neural networks (ANNs). The evolutionary algorithm used in EPNet is based on Fogel's evolutionary programming (EP). Unlike most previous studies on evolving ANN's, this paper puts its emphasis on evolving ANN's behaviors. Five mutation operators proposed in EPNet reflect such an emphasis on evolving behaviors. Close behavioral links between parents and their offspring are maintained by various mutations, such as partial training and node splitting. EPNet evolves ANN's architectures and connection weights (including biases) simultaneously in order to reduce the noise in fitness evaluation. The parsimony of evolved ANN's is encouraged by preferring node/connection deletion to addition. EPNet has been tested on a number of benchmark problems in machine learning and ANNs, such as the parity problem, the medical diagnosis problems, the Australian credit card assessment problem, and the Mackey-Glass time series prediction problem. The experimental results show that EPNet can produce very compact ANNs with good generalization ability in comparison with other algorithms. PMID:18255671

  16. Atmospheric controls on Puerto Rico precipitation using artificial neural networks

    NASA Astrophysics Data System (ADS)

    Ramseyer, Craig A.; Mote, Thomas L.

    2016-01-01

    The growing need for local climate change scenarios has given rise to a wide range of empirical climate downscaling techniques. One of the most critical decisions in these methodologies is the selection of appropriate predictor variables for the downscaled surface predictand. A systematic approach to selecting predictor variables should be employed to ensure that the most important variables are utilized for the study site where the climate change scenarios are being developed. Tropical study areas have been far less examined than mid- and high-latitudes in the climate downscaling literature. As a result, studies analyzing optimal predictor variables for tropics are limited. The objectives of this study include developing artificial neural networks for six sites around Puerto Rico to develop nonlinear functions between 37 atmospheric predictor variables and local rainfall. The relative importance of each predictor is analyzed to determine the most important inputs in the network. Randomized ANNs are produced to determine the statistical significance of the relative importance of each predictor variable. Lower tropospheric moisture and winds are shown to be the most important variables at all sites. Results show inter-site variability in u- and v-wind importance depending on the unique geographic situation of the site. Lower tropospheric moisture and winds are physically linked to variability in sea surface temperatures (SSTs) and the strength and position of the North Atlantic High Pressure cell (NAHP). The changes forced by anthropogenic climate change in regional SSTs and the NAHP will impact rainfall variability in Puerto Rico.

  17. Neural-Net Processing of Characteristic Patterns From Electronic Holograms of Vibrating Blades

    NASA Technical Reports Server (NTRS)

    Decker, Arthur J.

    1999-01-01

    Finite-element-model-trained artificial neural networks can be used to process efficiently the characteristic patterns or mode shapes from electronic holograms of vibrating blades. The models used for routine design may not yet be sufficiently accurate for this application. This document discusses the creation of characteristic patterns; compares model generated and experimental characteristic patterns; and discusses the neural networks that transform the characteristic patterns into strain or damage information. The current potential to adapt electronic holography to spin rigs, wind tunnels and engines provides an incentive to have accurate finite element models lor training neural networks.

  18. Confidence intervals in Flow Forecasting by using artificial neural networks

    NASA Astrophysics Data System (ADS)

    Panagoulia, Dionysia; Tsekouras, George

    2014-05-01

    One of the major inadequacies in implementation of Artificial Neural Networks (ANNs) for flow forecasting is the development of confidence intervals, because the relevant estimation cannot be implemented directly, contrasted to the classical forecasting methods. The variation in the ANN output is a measure of uncertainty in the model predictions based on the training data set. Different methods for uncertainty analysis, such as bootstrap, Bayesian, Monte Carlo, have already proposed for hydrologic and geophysical models, while methods for confidence intervals, such as error output, re-sampling, multi-linear regression adapted to ANN have been used for power load forecasting [1-2]. The aim of this paper is to present the re-sampling method for ANN prediction models and to develop this for flow forecasting of the next day. The re-sampling method is based on the ascending sorting of the errors between real and predicted values for all input vectors. The cumulative sample distribution function of the prediction errors is calculated and the confidence intervals are estimated by keeping the intermediate value, rejecting the extreme values according to the desired confidence levels, and holding the intervals symmetrical in probability. For application of the confidence intervals issue, input vectors are used from the Mesochora catchment in western-central Greece. The ANN's training algorithm is the stochastic training back-propagation process with decreasing functions of learning rate and momentum term, for which an optimization process is conducted regarding the crucial parameters values, such as the number of neurons, the kind of activation functions, the initial values and time parameters of learning rate and momentum term etc. Input variables are historical data of previous days, such as flows, nonlinearly weather related temperatures and nonlinearly weather related rainfalls based on correlation analysis between the under prediction flow and each implicit input

  19. Artificial Neural Networks Applications: from Aircraft Design Optimization to Orbiting Spacecraft On-board Environment Monitoring

    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.

  20. A neural net-based approach to software metrics

    NASA Technical Reports Server (NTRS)

    Boetticher, G.; Srinivas, Kankanahalli; Eichmann, David A.

    1992-01-01

    Software metrics provide an effective method for characterizing software. Metrics have traditionally been composed through the definition of an equation. This approach is limited by the fact that all the interrelationships among all the parameters be fully understood. This paper explores an alternative, neural network approach to modeling metrics. Experiments performed on two widely accepted metrics, McCabe and Halstead, indicate that the approach is sound, thus serving as the groundwork for further exploration into the analysis and design of software metrics.

  1. An artificial neural network system for diagnosing gas turbine engine fuel faults

    SciTech Connect

    Illi, O.J. Jr.; Greitzer, F.L.; Kangas, L.J.; Reeve, T.

    1994-04-01

    The US Army Ordnance Center & School and Pacific Northwest Laboratories are developing a turbine engine diagnostic system for the M1A1 Abrams tank. This system employs Artificial Neural Network (AN) technology to perform diagnosis and prognosis of the tank`s AGT-1500 gas turbine engine. This paper describes the design and prototype development of the ANN component of the diagnostic system, which we refer to as ``TEDANN`` for Turbine Engine Diagnostic Artificial Neural Networks.

  2. Sensitivity and Calibration of Non-Destructive Evaluation Method That Uses Neural-Net Processing of Characteristic Fringe Patterns

    NASA Technical Reports Server (NTRS)

    Decker, Arthur J.; Weiland, Kenneth E.

    2003-01-01

    This paper answers some performance and calibration questions about a non-destructive-evaluation (NDE) procedure that uses artificial neural networks to detect structural damage or other changes from sub-sampled characteristic patterns. The method shows increasing sensitivity as the number of sub-samples increases from 108 to 6912. The sensitivity of this robust NDE method is not affected by noisy excitations of the first vibration mode. A calibration procedure is proposed and demonstrated where the output of a trained net can be correlated with the outputs of the point sensors used for vibration testing. The calibration procedure is based on controlled changes of fastener torques. A heterodyne interferometer is used as a displacement sensor for a demonstration of the challenges to be handled in using standard point sensors for calibration.

  3. Modeling Career Counselor Decisions with Artificial Neural Networks: Predictions of Fit across a Comprehensive Occupational Map.

    ERIC Educational Resources Information Center

    Carson, Andrew D.; Bizot, Elizabeth B.; Hendershot, Peggy E.; Barton, Margaret G.; Garvin, Mary K.; Kraemer, Barbara

    1999-01-01

    Career recommendations were made based on aptitude scores of 335 high school freshmen. Artificial neural networks were used to map recommendations to 12 occupational clusters. Overall accuracy of neural networks (.80) approached that of discriminant function analysis (.84). The two methods had different strengths and weaknesses. (SK)

  4. S.E.U. experiments on an artificial neural network implemented by means of digital processors

    SciTech Connect

    Velazco, R.; Assoum, A.; Cheynet, P.; Olmos, M.

    1996-12-01

    The SEU sensitivity of an Artificial Neural Network intended to be used in space to detect protonic whistlers is investigated. The authors evaluate its behavior in the presence of SEU-like faults for a hardware implementation, associating a general purpose microprocessor to a dedicated neural processor. Experimental results (SEU simulations and heavy ion ground tests) show the robustness of this implementation.

  5. Using Artificial Neural Networks in Educational Research: Some Comparisons with Linear Statistical Models.

    ERIC Educational Resources Information Center

    Everson, Howard T.; And Others

    This paper explores the feasibility of neural computing methods such as artificial neural networks (ANNs) and abductory induction mechanisms (AIM) for use in educational measurement. ANNs and AIMS methods are contrasted with more traditional statistical techniques, such as multiple regression and discriminant function analyses, for making…

  6. Neural Nets for Generalization and Classification: Comment on Staddon and Reid (1990).

    ERIC Educational Resources Information Center

    Shepard, Roger N.

    1990-01-01

    The neural net model of J. E. R. Staddon and A. K. Reid (1990) explains exponential and Gaussian generalization gradients in the same way as the diffusion model of R. N. Shepard (1958). The cognitive generalization theory of Shepard (1987), also implemented as a connectionist network, goes beyond both models in accounting for classification…

  7. A hybrid architecture for the implementation of the Athena neural net model

    NASA Technical Reports Server (NTRS)

    Koutsougeras, C.; Papachristou, C.

    1989-01-01

    The implementation of an earlier introduced neural net model for pattern classification is considered. Data flow principles are employed in the development of a machine that efficiently implements the model and can be useful for real time classification tasks. Further enhancement with optical computing structures is also considered.

  8. ALINET: neural net automatic alignment of high-energy laser resonator optical elements

    NASA Astrophysics Data System (ADS)

    Hart, George A.; Bailey, Adam W.; Palumbo, Louis J.; Kuperstein, Michael

    1993-10-01

    A novel neural net approach has successfully solved the time consuming practical problem of aligning the many optical elements used in the resonator of high power chemical lasers. Moreover, because the neural net can achieve optimal performance in only 2 - 4 steps, as compared with 50 for other techniques, the important ability to effect real time control is gained. This represents a significant experimental breakthrough because of the difficulty previously associated with this alignment process. Use of either near or far field image information produces excellent performance. The method is very robust in the presence of noise. For cases where the initial misalignment falls outside the regime encompassed by the training set, a hybrid approach utilizing an advanced conventional method can bring the optical system within the capture range of the neural net. This reported use of a neural net to rapidly convert imagery information into high precision control information is of broad applicability to optical, acoustic, or electromagnetic alignment, positioning, and control problems.

  9. Locus minimization in breed prediction using artificial neural network approach.

    PubMed

    Iquebal, M A; Ansari, M S; Sarika; Dixit, S P; Verma, N K; Aggarwal, R A K; Jayakumar, S; Rai, A; Kumar, D

    2014-12-01

    Molecular markers, viz. microsatellites and single nucleotide polymorphisms, have revolutionized breed identification through the use of small samples of biological tissue or germplasm, such as blood, carcass samples, embryos, ova and semen, that show no evident phenotype. Classical tools of molecular data analysis for breed identification have limitations, such as the unavailability of referral breed data, causing increased cost of collection each time, compromised computational accuracy and complexity of the methodology used. We report here the successful use of an artificial neural network (ANN) in background to decrease the cost of genotyping by locus minimization. The webserver is freely accessible (http://nabg.iasri.res.in/bisgoat) to the research community. We demonstrate that the machine learning (ANN) approach for breed identification is capable of multifold advantages such as locus minimization, leading to a drastic reduction in cost, and web availability of reference breed data, alleviating the need for repeated genotyping each time one investigates the identity of an unknown breed. To develop this model web implementation based on ANN, we used 51,850 samples of allelic data of microsatellite-marker-based DNA fingerprinting on 25 loci covering 22 registered goat breeds of India for training. Minimizing loci to up to nine loci through the use of a multilayer perceptron model, we achieved 96.63% training accuracy. This server can be an indispensable tool for identification of existing breeds and new synthetic commercial breeds, leading to protection of intellectual property in case of sovereignty and bio-piracy disputes. This server can be widely used as a model for cost reduction by locus minimization for various other flora and fauna in terms of variety, breed and/or line identification, especially in conservation and improvement programs. PMID:25183434

  10. Artificial neural network to search for metal-poor galaxies

    NASA Astrophysics Data System (ADS)

    Shi, Fei; Liu, Yu-Yan; Kong, Xu; Chen, Yang

    2014-02-01

    Aims: To find a fast and reliable method for selecting metal-poor galaxies (MPGs), especially in large surveys and huge databases, an artificial neural network (ANN) method is applied to a sample of star-forming galaxies from the Sloan Digital Sky Survey (SDSS) data release 9 (DR9) provided by the Max Planck Institute and the Johns Hopkins University (MPA/JHU). Methods: A two-step approach is adopted: (i) The ANN network must be trained with a subset of objects that are known to be either MPGs or metal rich galaxies (MRGs), treating the strong emission line flux measurements as input feature vectors in n-dimensional space, where n is the number of strong emission line flux ratios. (ii) After the network is trained on a sample of star-forming galaxies, the remaining galaxies are classified in the automatic test analysis as either MPGs or MRGs. We consider several random divisions of the data into training and testing sets; for instance, for our sample, a total of 70 percent of the data are involved in training the algorithm, 15 percent are involved in validating the algorithm, and the remaining 15 percent are used for blind testing the resulting classifier. Results: For target selection, we have achieved an acquisition rate for MPGs of 96 percent and 92 percent for an MPGs threshold of 12 + log (O/H) = 8.00 and 12 + log (O/H) = 8.39, respectively. Running the code takes minutes in most cases under the Matlab 2013a software environment. The ANN method can easily be extended to any MPGs target selection task when the physical property of the target can be expressed as a quantitative variable. The code in the paper is available on the web (http://fshi5388.blog.163.com).

  11. Classification of rainfall variability by using artificial neural networks

    NASA Astrophysics Data System (ADS)

    Michaelides, Silas Chr.; Pattichis, Constantinos S.; Kleovoulou, Georgia

    2001-09-01

    In this paper, the usefulness of artificial neural networks (ANNs) as a suitable tool for the study of the medium and long-term climatic variability is examined. A method for classifying the inherent variability of climatic data, as represented by the rainfall regime, is investigated. The rainfall recorded at a climatological station in Cyprus over a long time period has been used in this paper as the input for various ANN and cluster analysis models. The analysed rainfall data cover the time span 1917-1995. Using these values, two different procedures were followed for structuring the input vectors for training the ANN models: (a) each 1-year subset consisting of the 12 monthly elements, and (b) each 2-year subset consisting of the 24 monthly elements. Several ANN models with a varying number of output nodes have been trained, using an unsupervised learning paradigm, namely, the Kohonen's self-organizing feature maps algorithm. For both the 1- and 2-year subsets, 16 classes were empirically considered as the optimum for computing the prototype classes of weather variability for this meteorological parameter. The classification established by using the ANN methodology is subsequently compared with the classification generated by using cluster analysis, based on the agglomerative hierarchical clustering algorithm. To validate the classification results, the rainfall distributions for the more recent years 1996, 1997 and 1998 were utilized. The respective 1- and 2-year distributions for these years were assigned to particular classes for both the ANN and cluster analysis procedures. Compared with cluster analysis, the ANN models were more capable of detecting even minor characteristics in the rainfall waveshapes investigated, and they also performed a more realistic categorization of the available data. It is suggested that the proposed ANN methodology can be applied to more climatological parameters, and with longer cycles.

  12. A new neural net approach to robot 3D perception and visuo-motor coordination

    NASA Technical Reports Server (NTRS)

    Lee, Sukhan

    1992-01-01

    A novel neural network approach to robot hand-eye coordination is presented. The approach provides a true sense of visual error servoing, redundant arm configuration control for collision avoidance, and invariant visuo-motor learning under gazing control. A 3-D perception network is introduced to represent the robot internal 3-D metric space in which visual error servoing and arm configuration control are performed. The arm kinematic network performs the bidirectional association between 3-D space arm configurations and joint angles, and enforces the legitimate arm configurations. The arm kinematic net is structured by a radial-based competitive and cooperative network with hierarchical self-organizing learning. The main goal of the present work is to demonstrate that the neural net representation of the robot 3-D perception net serves as an important intermediate functional block connecting robot eyes and arms.

  13. Forecasting jet fuel prices using artificial neural networks. Master`s thesis

    SciTech Connect

    Kasprzak, M.A.

    1995-03-01

    Artificial neural networks provide a new approach to commodity forecasting that does not require algorithm or rule development. Neural networks have been deemed successful in applications involving optimization, classification, identification, pattern recognition and time series forecasting. With the advent of user friendly, commercially available software packages that work in a spreadsheet environment, such as Neural Works Predict by NeuralWare, more people can take advantage of the power of artificial neural networks. This thesis provides an introduction to neural networks, and reviews two recent studies of forecasting commodities prices. This study also develops a neural network model using Neural Works Predict that forecasts jet fuel prices for the Defense Fuel Supply Center (DFSC). In addition, the results developed are compared to the output of an econometric regression model, specifically, the Department of Energy`s Short-Term Integrated Forecasting System (STWS) model. The Predict artificial neural network model produced more accurate results and reduced the contribution of outliers more effectively than the STIFS model, thus producing a more robust model.

  14. Calibration of a shock wave position sensor using artificial neural networks

    NASA Technical Reports Server (NTRS)

    Decker, Arthur J.; Weiland, Kenneth E.

    1993-01-01

    This report discusses the calibration of a shock wave position sensor. The position sensor works by using artificial neural networks to map cropped CCD frames of the shadows of the shock wave into the value of the shock wave position. This project was done as a tutorial demonstration of method and feasibility. It used a laboratory shadowgraph, nozzle, and commercial neural network package. The results were quite good, indicating that artificial neural networks can be used efficiently to automate the semi-quantitative applications of flow visualization.

  15. [Artificial neural network parameters optimization software and its application in the design of sustained release tablets].

    PubMed

    Zhang, Xing-Yi; Chen, Da-Wei; Jin, Jie; Lu, Wei

    2009-10-01

    Artificial neural network (ANN) is a multi-objective optimization method that needs mathematic and statistic knowledge which restricts its application in the pharmaceutical research area. An artificial neural network parameters optimization software (ANNPOS) programmed by the Visual Basic language was developed to overcome this shortcoming. In the design of a sustained release formulation, the suitable parameters of ANN were estimated by the ANNPOS. And then the Matlab 5.0 Neural Network Toolbox was used to determine the optimal formulation. It showed that the ANNPOS reduced the complexity and difficulty in the ANN's application. PMID:20055142

  16. Fault diagnosis of an air-handling unit using artificial neural networks

    SciTech Connect

    Lee, W.Y.; House, J.M.; Park, C.; Kelly, G.E.

    1996-11-01

    The objective of this study is to describe the application of artificial neural networks to the problem of fault diagnosis in an air-handling unit. Initially, residuals of system variables that can be used to quantify the dominant symptoms of fault modes of operation are selected. Idealized steady-state patterns of the residuals are then defined for each fault mode of operation. The steady-state relationship between the dominant symptoms and the faults is learned by an artificial neural network using the backpropagation algorithm. The trained neural network is applied to experimental data for various faults and successfully identifies each fault.

  17. Intelligent control based on fuzzy logic and neural net theory

    NASA Technical Reports Server (NTRS)

    Lee, Chuen-Chien

    1991-01-01

    In the conception and design of intelligent systems, one promising direction involves the use of fuzzy logic and neural network theory to enhance such systems' capability to learn from experience and adapt to changes in an environment of uncertainty and imprecision. Here, an intelligent control scheme is explored by integrating these multidisciplinary techniques. A self-learning system is proposed as an intelligent controller for dynamical processes, employing a control policy which evolves and improves automatically. One key component of the intelligent system is a fuzzy logic-based system which emulates human decision making behavior. It is shown that the system can solve a fairly difficult control learning problem. Simulation results demonstrate that improved learning performance can be achieved in relation to previously described systems employing bang-bang control. The proposed system is relatively insensitive to variations in the parameters of the system environment.

  18. Geometrical approach to neural net control of movements and posture

    NASA Technical Reports Server (NTRS)

    Pellionisz, A. J.; Ramos, C. F.

    1993-01-01

    In one approach to modeling brain function, sensorimotor integration is described as geometrical mapping among coordinates of non-orthogonal frames that are intrinsic to the system; in such a case sensors represent (covariant) afferents and motor effectors represent (contravariant) motor efferents. The neuronal networks that perform such a function are viewed as general tensor transformations among different expressions and metric tensors determining the geometry of neural functional spaces. Although the non-orthogonality of a coordinate system does not impose a specific geometry on the space, this "Tensor Network Theory of brain function" allows for the possibility that the geometry is non-Euclidean. It is suggested that investigation of the non-Euclidean nature of the geometry is the key to understanding brain function and to interpreting neuronal network function. This paper outlines three contemporary applications of such a theoretical modeling approach. The first is the analysis and interpretation of multi-electrode recordings. The internal geometries of neural networks controlling external behavior of the skeletomuscle system is experimentally determinable using such multi-unit recordings. The second application of this geometrical approach to brain theory is modeling the control of posture and movement. A preliminary simulation study has been conducted with the aim of understanding the control of balance in a standing human. The model appears to unify postural control strategies that have previously been considered to be independent of each other. Third, this paper emphasizes the importance of the geometrical approach for the design and fabrication of neurocomputers that could be used in functional neuromuscular stimulation (FNS) for replacing lost motor control.

  19. Geometrical approach to neural net control of movements and posture.

    PubMed

    Pellionisz, A J; Ramos, C F

    1993-01-01

    In one approach to modeling brain function, sensorimotor integration is described as geometrical mapping among coordinates of non-orthogonal frames that are intrinsic to the system; in such a case sensors represent (covariant) afferents and motor effectors represent (contravariant) motor efferents. The neuronal networks that perform such a function are viewed as general tensor transformations among different expressions and metric tensors determining the geometry of neural functional spaces. Although the non-orthogonality of a coordinate system does not impose a specific geometry on the space, this "Tensor Network Theory of brain function" allows for the possibility that the geometry is non-Euclidean. It is suggested that investigation of the non-Euclidean nature of the geometry is the key to understanding brain function and to interpreting neuronal network function. This paper outlines three contemporary applications of such a theoretical modeling approach. The first is the analysis and interpretation of multi-electrode recordings. The internal geometries of neural networks controlling external behavior of the skeletomuscle system is experimentally determinable using such multi-unit recordings. The second application of this geometrical approach to brain theory is modeling the control of posture and movement. A preliminary simulation study has been conducted with the aim of understanding the control of balance in a standing human. The model appears to unify postural control strategies that have previously been considered to be independent of each other. Third, this paper emphasizes the importance of the geometrical approach for the design and fabrication of neurocomputers that could be used in functional neuromuscular stimulation (FNS) for replacing lost motor control. PMID:8234751

  20. Latent Heat and Sensible Heat Fluxes Simulation in Maize Using Artificial Neural Networks

    NASA Astrophysics Data System (ADS)

    Safa, B.

    2015-12-01

    flux net radiation, wind speed, air temperature, leaf area index and vapor pressure deficit. This study reveals that artificial neural networks are not only a powerful technique for estimation of LE and H fluxes, but also can identify the effectiveness of each input on the fluxes.

  1. The application of artificial neural networks in astronomy

    NASA Astrophysics Data System (ADS)

    Li, Li-Li; Zhang, Yan-Xia; Zhao, Yong-Heng; Yang, Da-Wei

    2006-12-01

    Artificial Neural Networks (ANNs) are computer algorithms inspired from simple models of human central nervous system activity. They can be roughly divided into two main kinds: supervised and unsupervised. The supervised approach lays the stress on "teaching" a machine to do the work of a mention human expert, usually by showing examples for which the true answer is supplied by the expert. The unsupervised one is aimed at learning new things from the data, and most useful when the data cannot easily be plotted in a two or three dimensional space. ANNs have been used widely and successfully in various fields, for instance, pattern recognition, financial analysis, biology, engineering and so on, because they have many merits such as self-learning, self-adapting, good robustness and dynamically rapid response as well as strong capability of dealing with non-linear problems. In the last few years there has been an increasing interest toward the astronomical applications of ANNs. In this paper, the authors firstly introduce the fundamental principle of ANNs together with the architecture of the network and outline various kinds of learning algorithms and network toplogies. The specific aspects of the applications of ANNs in astronomical problems are also listed, which contain the strong capabilities of approximating to arbitrary accuracy, any nonlinear functional mapping, parallel and distributed storage, tolerance of faulty and generalization of results. They summarize the advantages and disadvantages of main ANN models available to the astronomical community. Furthermore, the application cases of ANNs in astronomy are mainly described in detail. Here, the focus is on some of the most interesting fields of its application, for example: object detection, star/galaxy classification, spectral classification, galaxy morphology classification, the estimation of photometric redshifts of galaxies and time series analysis. In addition, other kinds of applications have been

  2. Artificial Neural Networks for Earthquake Early-Warning

    NASA Astrophysics Data System (ADS)

    Boese, M.; Erdik, M.; Wenzel, F.

    2003-12-01

    recognition task. The seismic patterns are defined by the shape and frequency content of the parts of seismograms that are available at each time step. From these, parameters that are relevant to seismic damage, such as peak ground acceleration (PGA), peak ground velocity (PGV), response spectral amplitudes at certain periods and macroseismic intensity, are estimated using Artificial Neural Networks (ANN). We combine pattern recognition with an additional rule-based system in order to detect inconsistencies between ground motion estimations and measurements. This combination provides a reliable and accurate system for early-warning that is demanded by its huge social and economic impact.

  3. Designing laboratory wind simulations using artificial neural networks

    NASA Astrophysics Data System (ADS)

    Križan, Josip; Gašparac, Goran; Kozmar, Hrvoje; Antonić, Oleg; Grisogono, Branko

    2015-05-01

    While experiments in boundary layer wind tunnels remain to be a major research tool in wind engineering and environmental aerodynamics, designing the modeling hardware required for a proper atmospheric boundary layer (ABL) simulation can be costly and time consuming. Hence, possibilities are sought to speed-up this process and make it more time-efficient. In this study, two artificial neural networks (ANNs) are developed to determine an optimal design of the Counihan hardware, i.e., castellated barrier wall, vortex generators, and surface roughness, in order to simulate the ABL flow developing above urban, suburban, and rural terrains, as previous ANN models were created for one terrain type only. A standard procedure is used in developing those two ANNs in order to further enhance best-practice possibilities rather than to improve existing ANN designing methodology. In total, experimental results obtained using 23 different hardware setups are used when creating ANNs. In those tests, basic barrier height, barrier castellation height, spacing density, and height of surface roughness elements are the parameters that were varied to create satisfactory ABL simulations. The first ANN was used for the estimation of mean wind velocity, turbulent Reynolds stress, turbulence intensity, and length scales, while the second one was used for the estimation of the power spectral density of velocity fluctuations. This extensive set of studied flow and turbulence parameters is unmatched in comparison to the previous relevant studies, as it includes here turbulence intensity and power spectral density of velocity fluctuations in all three directions, as well as the Reynolds stress profiles and turbulence length scales. Modeling results agree well with experiments for all terrain types, particularly in the lower ABL within the height range of the most engineering structures, while exhibiting sensitivity to abrupt changes and data scattering in profiles of wind-tunnel results. The

  4. Optimization of Training Sets For Neural-Net Processing of Characteristic Patterns From Vibrating Solids

    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.

  5. Neural Net-Based Redesign of Transonic Turbines for Improved Unsteady Aerodynamic Performance

    NASA Technical Reports Server (NTRS)

    Madavan, Nateri K.; Rai, Man Mohan; Huber, Frank W.

    1998-01-01

    A recently developed neural net-based aerodynamic design procedure is used in the redesign of a transonic turbine stage to improve its unsteady aerodynamic performance. The redesign procedure used incorporates the advantages of both traditional response surface methodology (RSM) and neural networks by employing a strategy called parameter-based partitioning of the design space. Starting from the reference design, a sequence of response surfaces based on both neural networks and polynomial fits are constructed to traverse the design space in search of an optimal solution that exhibits improved unsteady performance. The procedure combines the power of neural networks and the economy of low-order polynomials (in terms of number of simulations required and network training requirements). A time-accurate, two-dimensional, Navier-Stokes solver is used to evaluate the various intermediate designs and provide inputs to the optimization procedure. The optimization procedure yields a modified design that improves the aerodynamic performance through small changes to the reference design geometry. The computed results demonstrate the capabilities of the neural net-based design procedure, and also show the tremendous advantages that can be gained by including high-fidelity unsteady simulations that capture the relevant flow physics in the design optimization process.

  6. Neural net controller for inlet pressure control of rocket engine testing

    NASA Technical Reports Server (NTRS)

    Trevino, Luis C.

    1994-01-01

    Many dynamic systems operate in select operating regions, each exhibiting characteristic modes of behavior. It is traditional to employ standard adjustable gain proportional-integral-derivative (PID) loops in such systems where no apriori model information is available. However, for controlling inlet pressure for rocket engine testing, problems in fine tuning, disturbance accommodation, and control gains for new profile operating regions (for research and development) are typically encountered. Because of the capability of capturing I/O peculiarities, using NETS, a back propagation trained neural network is specified. For select operating regions, the neural network controller is simulated to be as robust as the PID controller. For a comparative analysis, the higher order moment neural array (HOMNA) method is used to specify a second neural controller by extracting critical exemplars from the I/O data set. Furthermore, using the critical exemplars from the HOMNA method, a third neural controller is developed using NETS back propagation algorithm. All controllers are benchmarked against each other.

  7. An artificial neural network approach to estimate evapotranspiration from remote sensing and AmeriFlux data

    NASA Astrophysics Data System (ADS)

    Chen, Zhuoqi; Shi, Runhe; Zhang, Shupeng

    2013-03-01

    A simple and accurate method to estimate evapotranspiration (ET) is essential for dynamic monitoring of the Earth system at a large scale. In this paper, we developed an artificial neural network (ANN) model forced by remote sensing and AmeriFlux data to estimate ET. First, the ANN was trained with ET measurements made at 13 AmeriFlux sites and land surface products derived from satellite remotely sensed data (normalized difference vegetation index, land surface temperature and surface net radiation) for the period 2002-2006. ET estimated with the ANN was then validated by ET observed at five AmeriFlux sites during the same period. The validation sites covered five different vegetation types and were not involved in the ANN training. The coefficient of determination ( R 2) value for comparison between estimated and measured ET was 0.77, the root-mean-square error was 0.62 mm/d, and the mean residual was - 0.28. The simple model developed in this paper captured the seasonal and interannual variation features of ET on the whole. However, the accuracy of estimated ET depended on the vegetation types, among which estimated ET showed the best result for deciduous broadleaf forest compared to the other four vegetation types.

  8. Detection of apnea using a short-window FFT technique and an artificial neural network

    NASA Astrophysics Data System (ADS)

    Waldemark, Karina E.; Agehed, Kenneth I.; Lindblad, Thomas; Waldemark, Joakim T. A.

    1998-03-01

    Sleep apnea is characterized by frequent prolonged interruptions of breathing during sleep. This syndrome causes severe sleep disorders and is often responsible for development of other diseases such as heart problems, high blood pressure and daytime fatigue, etc. After diagnosis, sleep apnea is often successfully treated by applying positive air pressure (CPAP) to the mouth and nose. Although effective, the (CPAP) equipment takes up a lot of space and the connected mask causes a lot of inconvenience for the patients. This raised interest in developing new techniques for treatment of sleep apnea syndrome. Several studies have indicated that electrical stimulation of the hypoglossal nerve and muscle in the tongue may be a useful method for treating patients with severe sleep apnea. In order to be able to successfully prevent the occurrence of apnea it is necessary to have some technique for early and fast on-line detection or prediction of the apnea events. This paper suggests using measurements of respiratory airflow (mouth temperature). The signal processing for this task includes the use of a short window FFT technique and uses an artificial back propagation neural net to model or predict the occurrence of apneas. The results show that early detection of respiratory interruption is possible and that the delay time for this is small.

  9. A comparative study of biological production in eastern boundary upwelling systems using an artificial neural network

    NASA Astrophysics Data System (ADS)

    Lachkar, Z.; Gruber, N.

    2012-01-01

    Eastern Boundary Upwelling Systems (EBUS) are highly productive ocean regions. Yet, substantial differences in net primary production (NPP) exist within and between these systems for reasons that are still not fully understood. Here, we explore the leading physical processes and environmental factors controlling NPP in EBUS through a comparative study of the California, Canary, Benguela, and Humboldt Current systems. The NPP drivers are identified with the aid of an artificial neural network analysis based on self-organizing-maps (SOM). Our results suggest that in addition to the expected NPP enhancing effect of stronger equatorward alongshore wind, three factors have an inhibiting effect: (1) strong eddy activity, (2) narrow continental shelf, and (3) deep mixed layer. The co-variability of these 4 drivers defines in the context of the SOM a continuum of 100 patterns of NPP regimes in EBUS. These are grouped into 4 distinct classes using a Hierarchical Agglomerative Clustering (HAC) method. Our objective classification of EBUS reveals important variations of NPP regimes within each of the four EBUS, particularly in the Canary and Benguela Current systems. Our results show that the Atlantic EBUS are generally more productive and more sensitive to upwelling favorable winds because of weaker factors inhibiting NPP. Perturbations of alongshore winds associated with climate change may therefore lead to contrasting biological responses in the Atlantic and the Pacific EBUS.

  10. A comparative study of biological production in eastern boundary upwelling systems using an artificial neural network

    NASA Astrophysics Data System (ADS)

    Lachkar, Z.; Gruber, N.

    2011-10-01

    Eastern Boundary Upwelling Systems (EBUS) are highly productive ocean regions. Yet, substantial differences in net primary production (NPP) exist within and between these systems for reasons that are still not fully understood. Here, we explore the leading physical processes and environmental factors controlling NPP in EBUS through a comparative study of the California, Canary, Benguela, and Humboldt Current systems. The identification of NPP drivers is done with the aid of an artificial neural network analysis based on self-organizing-maps (SOMs). We show that in addition to the expected NPP enhancing effect of stronger alongshore wind, three factors have an inhibiting effect: (1) strong eddy activity, (2) narrow continental shelf, and (3) deep mixed layer. The co-variability of these 4 drivers defines in the context of the SOM a continuum of 100 patterns of NPP regimes in EBUS. These are grouped into 4 distinct classes using a Hierarchical Agglomerative Clustering (HAC) method. Our objective classification of EBUS reveals important variations of NPP regimes within each of the four EBUS, particularly in the Canary and Benguela Current systems. Our results show that the Atlantic EBUS are generally more productive and more sensitive to upwelling favorable winds because of a weaker factors inhibiting NPP. Perturbations of alongshore winds associated with climate change may therefore lead to contrasting biological responses in the Atlantic and the Pacific EBUS.

  11. Artificial Neural Network Test Support Development for the Space Shuttle PRCS Thrusters

    NASA Technical Reports Server (NTRS)

    Lehr, Mark E.

    2005-01-01

    A significant anomaly, Fuel Valve Pilot Seal Extrusion, is affecting the Shuttle Primary Reaction Control System (PRCS) Thrusters, and has caused 79 to fail. To help address this problem, a Shuttle PRCS Thruster Process Evaluation Team (TPET) was formed. The White Sands Test Facility (WSTF) and Boeing members of the TPET have identified many discrete valve current trace characteristics that are predictive of the problem. However, these are difficult and time consuming to identify and trend by manual analysis. Based on this exhaustive analysis over months, 22 thrusters previously delivered by the Depot were identified as high risk for flight failures. Although these had only recently been installed, they had to be removed from Shuttles OV103 and OV104 for reprocessing, by directive of the Shuttle Project Office. The resulting impact of the thruster removal, replacement, and valve replacement was significant (months of work and hundreds of thousands of dollars). Much of this could have been saved had the proposed Neural Network (NN) tool described in this paper been in place. In addition to the significant benefits to the Shuttle indicated above, the development and implementation of this type of testing will be the genesis for potential Quality improvements across many areas of WSTF test data analysis and will be shared with other NASA centers. Future tests can be designed to incorporate engineering experience via Artificial Neural Nets (ANN) into depot level acceptance of hardware. Additionally, results were shared with a NASA Engineering and Safety Center (NESC) Super Problem Response Team (SPRT). There was extensive interest voiced among many different personnel from several centers. There are potential spin-offs of this effort that can be directly applied to other data acquisition systems as well as vehicle health management for current and future flight vehicles.

  12. Resource constrained design of artificial neural networks using comparator neural network

    NASA Technical Reports Server (NTRS)

    Wah, Benjamin W.; Karnik, Tanay S.

    1992-01-01

    We present a systematic design method executed under resource constraints for automating the design of artificial neural networks using the back error propagation algorithm. Our system aims at finding the best possible configuration for solving the given application with proper tradeoff between the training time and the network complexity. The design of such a system is hampered by three related problems. First, there are infinitely many possible network configurations, each may take an exceedingly long time to train; hence, it is impossible to enumerate and train all of them to completion within fixed time, space, and resource constraints. Second, expert knowledge on predicting good network configurations is heuristic in nature and is application dependent, rendering it difficult to characterize fully in the design process. A learning procedure that refines this knowledge based on examples on training neural networks for various applications is, therefore, essential. Third, the objective of the network to be designed is ill-defined, as it is based on a subjective tradeoff between the training time and the network cost. A design process that proposes alternate configurations under different cost-performance tradeoff is important. We have developed a Design System which schedules the available time, divided into quanta, for testing alternative network configurations. Its goal is to select/generate and test alternative network configurations in each quantum, and find the best network when time is expended. Since time is limited, a dynamic schedule that determines the network configuration to be tested in each quantum is developed. The schedule is based on relative comparison of predicted training times of alternative network configurations using comparator network paradigm. The comparator network has been trained to compare training times for a large variety of traces of TSSE-versus-time collected during back-propagation learning of various applications.

  13. Fast radiative transfer of dust reprocessing in semi-analytic models with artificial neural networks

    NASA Astrophysics Data System (ADS)

    Silva, Laura; Fontanot, Fabio; Granato, Gian Luigi

    2012-06-01

    A serious concern for semi-analytical galaxy formation models, aiming to simulate multiwavelength surveys and to thoroughly explore the model parameter space, is the extremely time-consuming numerical solution of the radiative transfer of stellar radiation through dusty media. To overcome this problem, we have implemented an artificial neural network (ANN) algorithm in the radiative transfer code GRASIL, in order to significantly speed up the computation of the infrared (IR) spectral energy distribution (SED). The ANN we have implemented is of general use, in that its input neurons are defined as those quantities effectively determining the shape of the IR SED. Therefore, the training of the ANN can be performed with any model and then applied to other models. We made a blind test to check the algorithm, by applying a net trained with a standard chemical evolution model (i.e. CHE_EVO) to a mock catalogue extracted from the semi-analytic model MORGANA, and compared galaxy counts and evolution of the luminosity functions in several near-IR to sub-millimetre (sub-mm) bands, and also the spectral differences for a large subset of randomly extracted models. The ANN is able to excellently approximate the full computation, but with a gain in CPU time by ˜2 orders of magnitude. It is only advisable that the training covers reasonably well the range of values of the input neurons in the application. Indeed in the sub-mm at high redshift, a tiny fraction of models with some sensible input neurons out of the range of the trained net cause wrong answer by the ANN. These are extreme starbursting models with high optical depths, favourably selected by sub-mm observations, and are difficult to predict a priori.

  14. Classification of land cover using optimized neural nets on SPOT data

    SciTech Connect

    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.

  15. The concept models and implementations of multiport neural net associative memory for 2D patterns

    NASA Astrophysics Data System (ADS)

    Krasilenko, Vladimir G.; Nikolskyy, Aleksandr I.; Yatskovskaya, Rimma A.; Yatskovsky, Victor I.

    2011-04-01

    The paper considers neural net models and training and recognizing algorithms with base neurobiologic operations: p-step autoequivalence and non-equivalenc The Modified equivalently models (MEMs) of multiport neural net associative memory (MNNAM) are offered with double adaptive - equivalently weighing (DAEW) for recognition of 2D-patterns (images). It is shown, the computing process in MNNAM under using the proposed MEMs, is reduced to two-step and multi-step algorithms and step-by-step matrix-matrix (tensor-tensor) procedures. The given results of computer simulations confirmed the perspective of such models. Besides the result was received when MNNAM capacity on base of MEMs exceeded the amount of neurons.

  16. Macrobenthos habitat potential mapping using GIS-based artificial neural network models.

    PubMed

    Lee, Saro; Park, Inhye; Koo, Bon Joo; Ryu, Joo-Hyung; Choi, Jong-Kuk; Woo, Han Jun

    2013-02-15

    This paper proposes and tests a method of producing macrobenthos habitat potential maps in Hwangdo tidal flat, Korea based on an artificial neural network. Samples of macrobenthos were collected during field work, and eight control factors were compiled as a spatial database from remotely sensed data and GIS analysis. The macrobenthos habitat potential maps were produced using an artificial neural network model. Macrobenthos habitat potential maps were made for Macrophthalmus dilatatus, Cerithideopsilla cingulata, and Armandia lanceolata. The maps were validated by compared with the surveyed habitat locations. A strong correlation between the potential maps and species locations was revealed. The validation result showed average accuracies of 74.9%, 78.32%, and 73.27% for M. dilatatus, C. cingulata, and A. lanceolata, respectively. A GIS-based artificial neural network model combined with remote sensing techniques is an effective tool for mapping the areas of macrobenthos habitat potential in tidal flats. PMID:23260647

  17. Simultaneous determination of aniline and cyclohexylamine by principal component artificial neural networks.

    PubMed

    Absalan, Ghodratollah; Soleimani, Mohammad

    2004-05-01

    A specterophotometric method for simultaneous determination of aniline and cyclohexylamine using principal component artificial neural networks is proposed. This method is based on the reactions involving aniline and/or cyclohexylamine, with bis(acetylacetoneethylendiamine)tributylphosphine cobalt(III) perchlorate as a complexing reagent. A nonionic surfactant, Triton X-100, was used for dissolving the complexes and intensifying the signals. The absorption data were based on the spectra registered in the range of 350 - 550 nm. An artificial neural network consisting of three layers of nodes was trained by applying a back-propagation learning rule. Sigmoid transfer functions were used in the hidden and output layers to facilitate nonlinear calibration. The predictive ability of artificial neural networks was examined for the determination of aniline and cyclohexylamine in synthetic mixtures. PMID:15171298

  18. Estimation of Resonant Frequency of a Circular Microstrip Antenna Using Artificial Neural Network

    NASA Astrophysics Data System (ADS)

    Singh, Jagtar; Singh, A. P.; Kamal, T. S.

    2012-03-01

    In recent years the art of using artificial neural networks for wireless communication engineers has been gaining momentum. In this paper a general procedure is suggested for estimating the resonant frequency of circular microstrip patch antenna using artificial neural networks. The method of moments (MOM) based IE3D software was used to generate data dictionary for training and validation set of ANN. The proposed technique uses multilayer feed-forward back-propagation artificial neural network with one hidden layers for estimating the resonant frequency of a circular microstrip antenna. A relative performance of the different training algorithms is carried out for estimating the resonant frequency with particular attention paid to the speed of computation and accuracy achieved. This type of performance comparison has not been attempted so far.

  19. Measurement of relative density of tissue using wavelet analysis and neural nets

    NASA Astrophysics Data System (ADS)

    Suyatinov, Sergey I.; Kolentev, Sergey V.; Buldakova, Tatyana I.

    2001-01-01

    Development of methods for indirect measurement of substance's consistence and characteristics is highly actual problem of medical diagnostics. Many diseases bring about changes of tissue density or appearances of alien bodies (e.g. stones in kidneys or gallbladders). Propose to use wavelet-analysis and neural nets for indirect measurement of relative density of tissue by images of internal organs. It shall allow to reveal a disease on early stage.

  20. Application of artificial neural networks in nonlinear analysis of trusses

    NASA Technical Reports Server (NTRS)

    Alam, J.; Berke, L.

    1991-01-01

    A method is developed to incorporate neural network model based upon the Backpropagation algorithm for material response into nonlinear elastic truss analysis using the initial stiffness method. Different network configurations are developed to assess the accuracy of neural network modeling of nonlinear material response. In addition to this, a scheme based upon linear interpolation for material data, is also implemented for comparison purposes. It is found that neural network approach can yield very accurate results if used with care. For the type of problems under consideration, it offers a viable alternative to other material modeling methods.

  1. Beneficial role of noise in artificial neural networks

    SciTech Connect

    Monterola, Christopher; Saloma, Caesar; Zapotocky, Martin

    2008-06-18

    We demonstrate enhancement of neural networks efficacy to recognize frequency encoded signals and/or to categorize spatial patterns of neural activity as a result of noise addition. For temporal information recovery, noise directly added to the receiving neurons allow instantaneous improvement of signal-to-noise ratio [Monterola and Saloma, Phys. Rev. Lett. 2002]. For spatial patterns however, recurrence is necessary to extend and homogenize the operating range of a feed-forward neural network [Monterola and Zapotocky, Phys. Rev. E 2005]. Finally, using the size of the basin of attraction of the networks learned patterns (dynamical fixed points), a procedure for estimating the optimal noise is demonstrated.

  2. Face recognition using artificial neural network group-based adaptive tolerance (GAT) trees.

    PubMed

    Zhang, M; Fulcher, J

    1996-01-01

    Recent artificial neural network research has focused on simple models, but such models have not been very successful in describing complex systems (such as face recognition). This paper introduces the artificial neural network group-based adaptive tolerance (GAT) tree model for translation-invariant face recognition, suitable for use in an airport security system. GAT trees use a two-stage divide-and-conquer tree-type approach. The first stage determines general properties of the input, such as whether the facial image contains glasses or a beard. The second stage identifies the individual. Face perception classification, detection of front faces with glasses and/or beards, and face recognition results using GAT trees under laboratory conditions are presented. We conclude that the neural network group-based model offers significant improvement over conventional neural network trees for this task. PMID:18263454

  3. Artificial neural networks for processing fluorescence spectroscopy data in skin cancer diagnostics

    NASA Astrophysics Data System (ADS)

    Lenhardt, L.; Zeković, I.; Dramićanin, T.; Dramićanin, M. D.

    2013-11-01

    Over the years various optical spectroscopic techniques have been widely used as diagnostic tools in the discrimination of many types of malignant diseases. Recently, synchronous fluorescent spectroscopy (SFS) coupled with chemometrics has been applied in cancer diagnostics. The SFS method involves simultaneous scanning of both emission and excitation wavelengths while keeping the interval of wavelengths (constant-wavelength mode) or frequencies (constant-energy mode) between them constant. This method is fast, relatively inexpensive, sensitive and non-invasive. Total synchronous fluorescence spectra of normal skin, nevus and melanoma samples were used as input for training of artificial neural networks. Two different types of artificial neural networks were trained, the self-organizing map and the feed-forward neural network. Histopathology results of investigated skin samples were used as the gold standard for network output. Based on the obtained classification success rate of neural networks, we concluded that both networks provided high sensitivity with classification errors between 2 and 4%.

  4. The Application of Artificial Neural Networks to Astronomical Classification

    NASA Astrophysics Data System (ADS)

    Naim, A.

    1995-12-01

    Galaxies are fundamental to the understanding of the structure and evolution of the universe. They contain stars, gas and dust, and serve as an astrophysical laboratory in which physical processes can be examined. In the context of the large scale structure of the universe galaxies can be viewed as test particles. They are bright and therefore visible at very large distances, and also numerous and so can be used to provide reliable statistics. In previous decades the major obstacle to studying the large scale structure of the universe was the relatively sparse data samples, because obtaining large quantities of galaxian images and spectra requires a lot of observing time, and the accumulation of significant data bases was therefore a slow process. This obstacle is in the process of being removed today, with the advent of large-scale surveys (e.g., the APM galaxy survey, the Sloan Digital Sky Survey and the 2 degree Field survey). The new challenge with which the astronomical community is faced is the management and analysis of the forthcoming extragalactic data bases. On top of the obvious need for better hardware to give large storage volumes and quick access, one needs to devise automated tools for data analysis. The sheer volume of the data renders manual analysis impractical. It would be best if one could somehow transfer the knowledge and expertise accumulated over years of painstaking manual analysis to a machine. This thesis is part of an effort to achieve this goal. I borrowed techniques that have proved useful in other fields (e.g., engineering) and applied them to astronomical datasets. The major tool I used was Artificial Neural Networks (ANNs), which was originally conceived as a simplified computational model for the brain. The scope of methods and algorithms referred to as ANNs is quite wide. In particular, a distinction is made between Supervised Learning algorithms and Unsupervised methods. The former put the emphasis on ``teaching'' a machine to do

  5. Adaptive artificial neural network for autonomous robot control

    NASA Technical Reports Server (NTRS)

    Arras, Michael K.; Protzel, Peter W.; Palumbo, Daniel L.

    1992-01-01

    The topics are presented in viewgraph form and include: neural network controller for robot arm positioning with visual feedback; initial training of the arm; automatic recovery from cumulative fault scenarios; and error reduction by iterative fine movements.

  6. Application of Artificial Neural Networks in Differential Thermal Analysis of Inorganic Compounds

    NASA Astrophysics Data System (ADS)

    Ilgun, Ozlem; Beken, Murat; Alekberov, Vilayet; Ozcanli, Yesim

    2010-01-01

    Thermal decomposition of inorganic compounds have been analyzed by simultaneous differential thermal analysis (DTA) method. Also phase transitions and critical points have been investigated. Additionally a computer model based on backpropagation multilayer feed-forward artificial neural networks (ANNs) have been used for the stimulation and prediction of critical points and phase transitions of inorganic compounds. Experimental data and output values of artificial neural networks have been compared and ANN predictions showed a considerably good result due to some unjustified data values and ANN predictions concurred with each other.

  7. Deep neural nets as a method for quantitative structure-activity relationships.

    PubMed

    Ma, Junshui; Sheridan, Robert P; Liaw, Andy; Dahl, George E; Svetnik, Vladimir

    2015-02-23

    Neural networks were widely used for quantitative structure-activity relationships (QSAR) in the 1990s. Because of various practical issues (e.g., slow on large problems, difficult to train, prone to overfitting, etc.), they were superseded by more robust methods like support vector machine (SVM) and random forest (RF), which arose in the early 2000s. The last 10 years has witnessed a revival of neural networks in the machine learning community thanks to new methods for preventing overfitting, more efficient training algorithms, and advancements in computer hardware. In particular, deep neural nets (DNNs), i.e. neural nets with more than one hidden layer, have found great successes in many applications, such as computer vision and natural language processing. Here we show that DNNs can routinely make better prospective predictions than RF on a set of large diverse QSAR data sets that are taken from Merck's drug discovery effort. The number of adjustable parameters needed for DNNs is fairly large, but our results show that it is not necessary to optimize them for individual data sets, and a single set of recommended parameters can achieve better performance than RF for most of the data sets we studied. The usefulness of the parameters is demonstrated on additional data sets not used in the calibration. Although training DNNs is still computationally intensive, using graphical processing units (GPUs) can make this issue manageable. PMID:25635324

  8. Customizable VLSI artificial neural network chips based on a novel technology

    SciTech Connect

    Fu, C. Y.; Law, B.; Chapline, G.; Swenson, D.

    1993-09-14

    The human cerebral cortex contains approximately 10{sup 11} neurons and 10{sup 14} synapses. It thus seems logical that any technology intended to mimic human capabilities should have the ability to fabricate a very large number of neurons and even larger numbers of synapses. This paper describes an implementation of hardware neural networks using highly linear thin-film resistor technology and an 8-bit binary weight circuit to produce customizable artificial neural network chips and systems.

  9. On Design and Implementation of Neural-Machine Interface for Artificial Legs

    PubMed Central

    Zhang, Xiaorong; Liu, Yuhong; Zhang, Fan; Ren, Jin; Sun, Yan (Lindsay); Yang, Qing

    2011-01-01

    The quality of life of leg amputees can be improved dramatically by using a cyber physical system (CPS) that controls artificial legs based on neural signals representing amputees’ intended movements. The key to the CPS is the neural-machine interface (NMI) that senses electromyographic (EMG) signals to make control decisions. This paper presents a design and implementation of a novel NMI using an embedded computer system to collect neural signals from a physical system - a leg amputee, provide adequate computational capability to interpret such signals, and make decisions to identify user’s intent for prostheses control in real time. A new deciphering algorithm, composed of an EMG pattern classifier and a post-processing scheme, was developed to identify the user’s intended lower limb movements. To deal with environmental uncertainty, a trust management mechanism was designed to handle unexpected sensor failures and signal disturbances. Integrating the neural deciphering algorithm with the trust management mechanism resulted in a highly accurate and reliable software system for neural control of artificial legs. The software was then embedded in a newly designed hardware platform based on an embedded microcontroller and a graphic processing unit (GPU) to form a complete NMI for real time testing. Real time experiments on a leg amputee subject and an able-bodied subject have been carried out to test the control accuracy of the new NMI. Our extensive experiments have shown promising results on both subjects, paving the way for clinical feasibility of neural controlled artificial legs. PMID:22389637

  10. On the Relationships between Generative Encodings, Regularity, and Learning Abilities when Evolving Plastic Artificial Neural Networks

    PubMed Central

    Tonelli, Paul; Mouret, Jean-Baptiste

    2013-01-01

    A major goal of bio-inspired artificial intelligence is to design artificial neural networks with abilities that resemble those of animal nervous systems. It is commonly believed that two keys for evolving nature-like artificial neural networks are (1) the developmental process that links genes to nervous systems, which enables the evolution of large, regular neural networks, and (2) synaptic plasticity, which allows neural networks to change during their lifetime. So far, these two topics have been mainly studied separately. The present paper shows that they are actually deeply connected. Using a simple operant conditioning task and a classic evolutionary algorithm, we compare three ways to encode plastic neural networks: a direct encoding, a developmental encoding inspired by computational neuroscience models, and a developmental encoding inspired by morphogen gradients (similar to HyperNEAT). Our results suggest that using a developmental encoding could improve the learning abilities of evolved, plastic neural networks. Complementary experiments reveal that this result is likely the consequence of the bias of developmental encodings towards regular structures: (1) in our experimental setup, encodings that tend to produce more regular networks yield networks with better general learning abilities; (2) whatever the encoding is, networks that are the more regular are statistically those that have the best learning abilities. PMID:24236099

  11. Modeling of mass transfer of Phospholipids in separation process with supercritical CO2 fluid by RBF artificial neural networks

    Technology Transfer Automated Retrieval System (TEKTRAN)

    An artificial Radial Basis Function (RBF) neural network model was developed for the prediction of mass transfer of the phospholipids from canola meal in supercritical CO2 fluid. The RBF kind of artificial neural networks (ANN) with orthogonal least squares (OLS) learning algorithm were used for mod...

  12. Quantitative analysis of cefalexin based on artificial neural networks combined with modified genetic algorithm using short near-infrared spectroscopy

    NASA Astrophysics Data System (ADS)

    Huan, Yanfu; Feng, Guodong; Wang, Bin; Ren, Yulin; Fei, Qiang

    2013-05-01

    In this paper, a novel chemometric method was developed for rapid, accurate, and quantitative analysis of cefalexin in samples. The experiments were carried out by using the short near-infrared spectroscopy coupled with artificial neural networks. In order to enhancing the predictive ability of artificial neural networks model, a modified genetic algorithm was used to select fixed number of wavelength.

  13. Total solar irradiance reconstruction using artificial neural networks

    NASA Astrophysics Data System (ADS)

    Tebabal Yirdaw, Ambelu; Damtie, Baylie; Nigussie, Melessew; Bires, Abiyot; Yizengaw, Endawoke

    2015-08-01

    A feed-forward neural network which can account for nonlinear relationships was used to reconstruct total solar irradiance (TSI). A single layer feed forward neural network with back-propagation algorithm have been implemented for reconstructing daily total solar irradiance from daily photometric sunspot index, and core to wing ratio of Mg II index data. The data year from 1978 to 2013 was used for the training, validation and testing purpose. In order to obtain the optimum neural network for TSI reconstruction, the root mean square error (RMSE), mean absolute error (MAE) and regression coefficient have been taken into account. We have carried out the analysis is made by comparing the reconstructed TSI from neural networks (NNs ) and TSI measurement from satellite. We have found out that the reconstructed TSI and the PMOD composite have the correlation coefficient of about R=0.9307 over the span of the recorded, 1978 to 2013. The NNs model output indicates that reconstructed TSI from solar proxies (photometric index and MgII ) can explain 86.6% of the variance of TSI. Neural network is able to recreate TSI observations on a time scale of a day. This reconstructed TSI using NNs further strengthens the view that surface magnetism indeed plays a dominant role in modulating solar irradiance.

  14. An Examination of Application of Artificial Neural Network in Cognitive Radios

    NASA Astrophysics Data System (ADS)

    Bello Salau, H.; Onwuka, E. N.; Aibinu, A. M.

    2013-12-01

    Recent advancement in software radio technology has led to the development of smart device known as cognitive radio. This type of radio fuses powerful techniques taken from artificial intelligence, game theory, wideband/multiple antenna techniques, information theory and statistical signal processing to create an outstanding dynamic behavior. This cognitive radio is utilized in achieving diverse set of applications such as spectrum sensing, radio parameter adaptation and signal classification. This paper contributes by reviewing different cognitive radio implementation that uses artificial intelligence such as the hidden markov models, metaheuristic algorithm and artificial neural networks (ANNs). Furthermore, different areas of application of ANNs and their performance metrics based approach are also examined.

  15. Detection and classification of CCD defects with an artificial neural network

    NASA Astrophysics Data System (ADS)

    Rogers, R. D.; Riess, A. G.

    1994-05-01

    We have developed an artificial neural-network (ANN) system which locates and classifies defects in CCDs. This system, based on a feedforward neural network, was trained with a conjugate gradient training algorithm using observational data from an astronomical telesope. The network was tested with data from four large CCDs (2048 x 2048 pixels each) and found defects with a higher efficiency and in a much shorter time than human inspectors. This method of detecting and classifying objects in images is quite general and we discuss other applications in astronomy. In an appendix we provide a recipe for neural computing to make this technique more acessible to the astronomical community.

  16. Application of Artificial Neural Networks to the Design of Turbomachinery Airfoils

    NASA Technical Reports Server (NTRS)

    Rai, Man Mohan; Madavan, Nateri

    1997-01-01

    Artificial neural networks are widely used in engineering applications, such as control, pattern recognition, plant modeling and condition monitoring to name just a few. In this seminar we will explore the possibility of applying neural networks to aerodynamic design, in particular, the design of turbomachinery airfoils. The principle idea behind this effort is to represent the design space using a neural network (within some parameter limits), and then to employ an optimization procedure to search this space for a solution that exhibits optimal performance characteristics. Results obtained for design problems in two spatial dimensions will be presented.

  17. A Model for Improving the Learning Curves of Artificial Neural Networks

    PubMed Central

    2016-01-01

    In this article, the performance of a hybrid artificial neural network (i.e. scale-free and small-world) was analyzed and its learning curve compared to three other topologies: random, scale-free and small-world, as well as to the chemotaxis neural network of the nematode Caenorhabditis Elegans. One hundred equivalent networks (same number of vertices and average degree) for each topology were generated and each was trained for one thousand epochs. After comparing the mean learning curves of each network topology with the C. elegans neural network, we found that the networks that exhibited preferential attachment exhibited the best learning curves. PMID:26901646

  18. Vein matching using artificial neural network in vein authentication systems

    NASA Astrophysics Data System (ADS)

    Noori Hoshyar, Azadeh; Sulaiman, Riza

    2011-10-01

    Personal identification technology as security systems is developing rapidly. Traditional authentication modes like key; password; card are not safe enough because they could be stolen or easily forgotten. Biometric as developed technology has been applied to a wide range of systems. According to different researchers, vein biometric is a good candidate among other biometric traits such as fingerprint, hand geometry, voice, DNA and etc for authentication systems. Vein authentication systems can be designed by different methodologies. All the methodologies consist of matching stage which is too important for final verification of the system. Neural Network is an effective methodology for matching and recognizing individuals in authentication systems. Therefore, this paper explains and implements the Neural Network methodology for finger vein authentication system. Neural Network is trained in Matlab to match the vein features of authentication system. The Network simulation shows the quality of matching as 95% which is a good performance for authentication system matching.

  19. From artificial neural networks to spiking neuron populations and back again.

    PubMed

    de Kamps, M; van der Velde, F

    2001-01-01

    In this paper, we investigate the relation between Artificial Neural Networks (ANNs) and networks of populations of spiking neurons. The activity of an artificial neuron is usually interpreted as the firing rate of a neuron or neuron population. Using a model of the visual cortex, we will show that this interpretation runs into serious difficulties. We propose to interpret the activity of an artificial neuron as the steady state of a cross-inhibitory circuit, in which one population codes for 'positive' artificial neuron activity and another for 'negative' activity. We will show that with this interpretation it is possible, under certain circumstances, to transform conventional ANNs (e.g. trained with 'back-propagation') into biologically plausible networks of spiking populations. However, in general, the use of biologically motivated spike response functions introduces artificial neurons that behave differently from the ones used in the classical ANN paradigm. PMID:11665784

  20. Detection, location, and quantification of structural damage by neural-net-processed moiré profilometry

    NASA Astrophysics Data System (ADS)

    Grossman, Barry G.; Gonzalez, Frank S.; Blatt, Joel H.; Hooker, Jeffery A.

    1992-03-01

    The development of efficient high speed techniques to recognize, locate, and quantify damage is vitally important for successful automated inspection systems such as ones used for the inspection of undersea pipelines. Two critical problems must be solved to achieve these goals: the reduction of nonuseful information present in the video image and automatic recognition and quantification of extent and location of damage. Artificial neural network processed moire profilometry appears to be a promising technique to accomplish this. Real time video moire techniques have been developed which clearly distinguish damaged and undamaged areas on structures, thus reducing the amount of extraneous information input into an inspection system. Artificial neural networks have demonstrated advantages for image processing, since they can learn the desired response to a given input and are inherently fast when implemented in hardware due to their parallel computing architecture. Video moire images of pipes with dents of different depths were used to train a neural network, with the desired output being the location and severity of the damage. The system was then successfully tested with a second series of moire images. The techniques employed and the results obtained are discussed.

  1. Path optimisation of a mobile robot using an artificial neural network controller

    NASA Astrophysics Data System (ADS)

    Singh, M. K.; Parhi, D. R.

    2011-01-01

    This article proposed a novel approach for design of an intelligent controller for an autonomous mobile robot using a multilayer feed forward neural network, which enables the robot to navigate in a real world dynamic environment. The inputs to the proposed neural controller consist of left, right and front obstacle distance with respect to its position and target angle. The output of the neural network is steering angle. A four layer neural network has been designed to solve the path and time optimisation problem of mobile robots, which deals with the cognitive tasks such as learning, adaptation, generalisation and optimisation. A back propagation algorithm is used to train the network. This article also analyses the kinematic design of mobile robots for dynamic movements. The simulation results are compared with experimental results, which are satisfactory and show very good agreement. The training of the neural nets and the control performance analysis has been done in a real experimental setup.

  2. Predicting Item Difficulty in a Reading Comprehension Test with an Artificial Neural Network.

    ERIC Educational Resources Information Center

    Perkins, Kyle; And Others

    1995-01-01

    This article reports the results of using a three-layer back propagation artificial neural network to predict item difficulty in a reading comprehension test. Three classes of variables were examined: text structure, propositional analysis, and cognitive demand. Results demonstrate that the networks can consistently predict item difficulty. (JL)

  3. Predicting Item Difficulty in a Reading Comprehension Test with an Artificial Neural Network.

    ERIC Educational Resources Information Center

    Perkins, Kyle; And Others

    This paper reports the results of using a three-layer backpropagation artificial neural network to predict item difficulty in a reading comprehension test. Two network structures were developed, one with and one without a sigmoid function in the output processing unit. The data set, which consisted of a table of coded test items and corresponding…

  4. Predicting post-treatment survivability of patients with breast cancer using Artificial Neural Network methods.

    PubMed

    Wang, Tan-Nai; Cheng, Chung-Hao; Chiu, Hung-Wen

    2013-01-01

    In the last decade, the use of data mining techniques has become widely accepted in medical applications, especially in predicting cancer patients' survival. In this study, we attempted to train an Artificial Neural Network (ANN) to predict the patients' five-year survivability. Breast cancer patients who were diagnosed and received standard treatment in one hospital during 2000 to 2003 in Taiwan were collected for train and test the ANN. There were 604 patients in this dataset excluding died not in breast cancer. Among them 140 patients died within five years after their first radiotherapy treatment. The artificial neural networks were created by STATISTICA(®) software. Five variables (age, surgery and radiotherapy type, tumor size, regional lymph nodes, distant metastasis) were selected as the input features for ANN to predict the five-year survivability of breast cancer patients. We trained 100 artificial neural networks and chose the best one to analyze. The accuracy rate is 85% and area under the receiver operating characteristic (ROC) curve is 0.79. It shows that artificial neural network is a good tool to predict the five-year survivability of breast cancer patients. PMID:24109931

  5. Artificial Neural Networks: A New Approach for Predicting Application Behavior. AIR 2001 Annual Forum Paper.

    ERIC Educational Resources Information Center

    Gonzalez, Julie M. Byers; DesJardins, Stephen L.

    This paper examines how predictive modeling can be used to study application behavior. A relatively new technique, artificial neural networks (ANNs), was applied to help predict which students were likely to get into a large Research I university. Data were obtained from a university in Iowa. Two cohorts were used, each containing approximately…

  6. Automatic Keyword Identification by Artificial Neural Networks Compared to Manual Identification by Users of Filtering Systems.

    ERIC Educational Resources Information Center

    Boger, Zvi; Kuflik, Tsvi; Shoval, Peretz; Shapira, Bracha

    2001-01-01

    Discussion of information filtering (IF) and information retrieval focuses on the use of an artificial neural network (ANN) as an alternative method for both IF and term selection and compares its effectiveness to that of traditional methods. Results show that the ANN relevance prediction out-performs the prediction of an IF system. (Author/LRW)

  7. Statistical Classification for Cognitive Diagnostic Assessment: An Artificial Neural Network Approach

    ERIC Educational Resources Information Center

    Cui, Ying; Gierl, Mark; Guo, Qi

    2016-01-01

    The purpose of the current investigation was to describe how the artificial neural networks (ANNs) can be used to interpret student performance on cognitive diagnostic assessments (CDAs) and evaluate the performances of ANNs using simulation results. CDAs are designed to measure student performance on problem-solving tasks and provide useful…

  8. The Use of Artificial Neural Networks to Estimate Speech Intelligibility from Acoustic Variables: A Preliminary Analysis.

    ERIC Educational Resources Information Center

    Metz, Dale Evan; And Others

    1992-01-01

    A preliminary scheme for estimating the speech intelligibility of hearing-impaired speakers from acoustic parameters, using a computerized artificial neural network to process mathematically the acoustic input variables, is outlined. Tests with 60 hearing-impaired speakers found the scheme to be highly accurate in identifying speakers separated by…

  9. [Optimization of pellet formulation with the help of artificial neural networks].

    PubMed

    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

  10. Artificial neural network estimation of soil erosion and nutrient concentrations in runoff from land application areas

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The transport of sediment and nutrients from land application areas is an environmental concern. New methods are needed for estimating soil and nutrient concentrations of runoff from cropland areas on which manure is applied. Artificial Neural Networks (ANN) trained with a Backpropagation (BP) algor...

  11. Reconstructing missing daily precipitation data using regression trees and artificial neural networks

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Incomplete meteorological data has been a problem in environmental modeling studies. The objective of this work was to develop a technique to reconstruct missing daily precipitation data in the central part of Chesapeake Bay Watershed using regression trees (RT) and artificial neural networks (ANN)....

  12. Predicting Final GPA of Graduate School Students: Comparing Artificial Neural Networking and Simultaneous Multiple Regression

    ERIC Educational Resources Information Center

    Anderson, Joan L.

    2006-01-01

    Data from graduate student applications at a large Western university were used to determine which factors were the best predictors of success in graduate school, as defined by cumulative graduate grade point average. Two statistical models were employed and compared: artificial neural networking and simultaneous multiple regression. Both models…

  13. Optimum cutting parameters selection strategy based on neural network and artificial intelligence

    NASA Astrophysics Data System (ADS)

    Liang, Jian C.; Wen, Xisen; Li, Shengyi; Yang, Shuzi

    1995-08-01

    In this paper an optimum cutting parameters selection strategy based on neural network and artificial intelligence is proposed. It combines NN with AI and solves the problems of intelligent decision-making for cutting parameters during machining process. BP algorithm and inference engine design are discussed. Application examples of the strategy are simulated. The results show that the proposed strategy is very effective.

  14. Estimating tree bole volume using artificial neural network models for four species in Turkey.

    PubMed

    Ozçelik, Ramazan; Diamantopoulou, Maria J; Brooks, John R; Wiant, Harry V

    2010-01-01

    Tree bole volumes of 89 Scots pine (Pinus sylvestris L.), 96 Brutian pine (Pinus brutia Ten.), 107 Cilicica fir (Abies cilicica Carr.) and 67 Cedar of Lebanon (Cedrus libani A. Rich.) trees were estimated using Artificial Neural Network (ANN) models. Neural networks offer a number of advantages including the ability to implicitly detect complex nonlinear relationships between input and output variables, which is very helpful in tree volume modeling. Two different neural network architectures were used and produced the Back propagation (BPANN) and the Cascade Correlation (CCANN) Artificial Neural Network models. In addition, tree bole volume estimates were compared to other established tree bole volume estimation techniques including the centroid method, taper equations, and existing standard volume tables. An overview of the features of ANNs and traditional methods is presented and the advantages and limitations of each one of them are discussed. For validation purposes, actual volumes were determined by aggregating the volumes of measured short sections (average 1 meter) of the tree bole using Smalian's formula. The results reported in this research suggest that the selected cascade correlation artificial neural network (CCANN) models are reliable for estimating the tree bole volume of the four examined tree species since they gave unbiased results and were superior to almost all methods in terms of error (%) expressed as the mean of the percentage errors. PMID:19880241

  15. Artificial frame filling using adaptive neural fuzzy inference system for particle image velocimetry dataset

    NASA Astrophysics Data System (ADS)

    Akdemir, Bayram; Doǧan, Sercan; Aksoy, Muharrem H.; Canli, Eyüp; Özgören, Muammer

    2015-03-01

    Liquid behaviors are very important for many areas especially for Mechanical Engineering. Fast camera is a way to observe and search the liquid behaviors. Camera traces the dust or colored markers travelling in the liquid and takes many pictures in a second as possible as. Every image has large data structure due to resolution. For fast liquid velocity, there is not easy to evaluate or make a fluent frame after the taken images. Artificial intelligence has much popularity in science to solve the nonlinear problems. Adaptive neural fuzzy inference system is a common artificial intelligence in literature. Any particle velocity in a liquid has two dimension speed and its derivatives. Adaptive Neural Fuzzy Inference System has been used to create an artificial frame between previous and post frames as offline. Adaptive neural fuzzy inference system uses velocities and vorticities to create a crossing point vector between previous and post points. In this study, Adaptive Neural Fuzzy Inference System has been used to fill virtual frames among the real frames in order to improve image continuity. So this evaluation makes the images much understandable at chaotic or vorticity points. After executed adaptive neural fuzzy inference system, the image dataset increase two times and has a sequence as virtual and real, respectively. The obtained success is evaluated using R2 testing and mean squared error. R2 testing has a statistical importance about similarity and 0.82, 0.81, 0.85 and 0.8 were obtained for velocities and derivatives, respectively.

  16. Artificial neural network classification using a minimal training set - Comparison to conventional supervised classification

    NASA Technical Reports Server (NTRS)

    Hepner, George F.; Logan, Thomas; Ritter, Niles; Bryant, Nevin

    1990-01-01

    Recent research has shown an artificial neural network (ANN) to be capable of pattern recognition and the classification of image data. This paper examines the potential for the application of neural network computing to satellite image processing. A second objective is to provide a preliminary comparison and ANN classification. An artificial neural network can be trained to do land-cover classification of satellite imagery using selected sites representative of each class in a manner similar to conventional supervised classification. One of the major problems associated with recognition and classifications of pattern from remotely sensed data is the time and cost of developing a set of training sites. This reseach compares the use of an ANN back propagation classification procedure with a conventional supervised maximum likelihood classification procedure using a minimal training set. When using a minimal training set, the neural network is able to provide a land-cover classification superior to the classification derived from the conventional classification procedure. This research is the foundation for developing application parameters for further prototyping of software and hardware implementations for artificial neural networks in satellite image and geographic information processing.

  17. 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.

  18. Artificial neural network model for identifying taxi gross emitter from remote sensing data of vehicle emission.

    PubMed

    Zeng, Jun; Guo, Hua-fang; Hu, Yue-ming

    2007-01-01

    Vehicle emission has been the major source of air pollution in urban areas in the past two decades. This article proposes an artificial neural network model for identifying the taxi gross emitters based on the remote sensing data. After carrying out the field test in Guangzhou and analyzing various factors from the emission data, the artificial neural network modeling was proved to be an advisable method of identifying the gross emitters. On the basis of the principal component analysis and the selection of algorithm and architecture, the Back-Propagation neural network model with 8-17-1 architecture was established as the optimal approach for this purpose. It gave a percentage of hits of 93%. Our previous research result and the result from aggression analysis were compared, and they provided respectively the percentage of hits of 81.63% and 75%. This comparison demonstrates the potentiality and validity of the proposed method in the identification of taxi gross emitters. PMID:17915705

  19. Evaluation of the efficiency of artificial neural networks for genetic value prediction.

    PubMed

    Silva, G N; Tomaz, R S; Sant'Anna, I C; Carneiro, V Q; Cruz, C D; Nascimento, M

    2016-01-01

    Artificial neural networks have shown great potential when applied to breeding programs. In this study, we propose the use of artificial neural networks as a viable alternative to conventional prediction methods. We conduct a thorough evaluation of the efficiency of these networks with respect to the prediction of breeding values. Therefore, we considered eight simulated scenarios, and for the purpose of genetic value prediction, seven statistical parameters in addition to the phenotypic mean in a network designed as a multilayer perceptron. After an evaluation of different network configurations, the results demonstrated the superiority of neural networks compared to estimation procedures based on linear models, and indicated high predictive accuracy and network efficiency. PMID:27051007

  20. RRAM-based hardware implementations of artificial neural networks: progress update and challenges ahead

    NASA Astrophysics Data System (ADS)

    Prezioso, M.; Merrikh-Bayat, F.; Chakrabarti, B.; Strukov, D.

    2016-02-01

    Artificial neural networks have been receiving increasing attention due to their superior performance in many information processing tasks. Typically, scaling up the size of the network results in better performance and richer functionality. However, large neural networks are challenging to implement in software and customized hardware are generally required for their practical implementations. In this work, we will discuss our group's recent efforts on the development of such custom hardware circuits, based on hybrid CMOS/memristor circuits, in particular of CMOL variety. We will start by reviewing the basics of memristive devices and of CMOL circuits. We will then discuss our recent progress towards demonstration of hybrid circuits, focusing on the experimental and theoretical results for artificial neural networks based on crossbarintegrated metal oxide memristors. We will conclude presentation with the discussion of the remaining challenges and the most pressing research needs.

  1. Optimization with artificial neural network systems - A mapping principle and a comparison to gradient based methods

    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.

  2. Modelling the spectral energy distribution of galaxies: introducing the artificial neural network

    NASA Astrophysics Data System (ADS)

    Silva, L.; Schurer, A.; Granato, G. L.; Almeida, C.; Baugh, C. M.; Frenk, C. S.; Lacey, C. G.; Paoletti, L.; Petrella, A.; Selvestrel, D.

    2011-01-01

    The spectral energy distribution (SED) of galaxies is a complex function of the star formation history and geometrical arrangement of stars and gas in galaxies. The computation of the radiative transfer of stellar radiation through the dust distribution is time-consuming. This aspect becomes unacceptable in particular when dealing with the predictions by semi-analytical galaxy formation models populating cosmological volumes, to be then compared with multi-wavelength surveys. Mainly for this aim, we have implemented an artificial neural network (ANN) algorithm into the spectro-photometric and radiative transfer code GRASIL in order to compute the SED of galaxies in a short computing time. This allows to avoid the adoption of empirical templates that may have nothing to do with the mock galaxies output by models. The ANN has been implemented to compute the dust emission spectrum (the bottleneck of the computation), and separately for the star-forming molecular clouds (MC) and the diffuse dust (due to their different properties and dependencies). We have defined the input neurons effectively determining their emission, which means this implementation has a general applicability and is not linked to a particular galaxy formation model. We have trained the net for the disc and spherical geometries, and tested its performance to reproduce the SED of disc and starburst galaxies, as well as for a semi-analytical model for spheroidal galaxies. We have checked that for this model both the SEDs and the galaxy counts in the Herschel bands obtained with the ANN approximation are almost superimposed to the same quantities obtained with the full GRASIL. We conclude that this method appears robust and advantageous, and will present the application to a more complex SAM in another paper.

  3. Reduced-Order Modeling for Flutter/LCO Using Recurrent Artificial Neural Network

    NASA Technical Reports Server (NTRS)

    Yao, Weigang; Liou, Meng-Sing

    2012-01-01

    The present study demonstrates the efficacy of a recurrent artificial neural network to provide a high fidelity time-dependent nonlinear reduced-order model (ROM) for flutter/limit-cycle oscillation (LCO) modeling. An artificial neural network is a relatively straightforward nonlinear method for modeling an input-output relationship from a set of known data, for which we use the radial basis function (RBF) with its parameters determined through a training process. The resulting RBF neural network, however, is only static and is not yet adequate for an application to problems of dynamic nature. The recurrent neural network method [1] is applied to construct a reduced order model resulting from a series of high-fidelity time-dependent data of aero-elastic simulations. Once the RBF neural network ROM is constructed properly, an accurate approximate solution can be obtained at a fraction of the cost of a full-order computation. The method derived during the study has been validated for predicting nonlinear aerodynamic forces in transonic flow and is capable of accurate flutter/LCO simulations. The obtained results indicate that the present recurrent RBF neural network is accurate and efficient for nonlinear aero-elastic system analysis

  4. Forecasting solar proton event with artificial neural network

    NASA Astrophysics Data System (ADS)

    Gong, J.; Wang, J.; Xue, B.; Liu, S.; Zou, Z.

    Solar proton event (SPE), relatively rare but popular in solar maximum, can bring hazard situation to spacecraft. As a special event, SPE always accompanies flare, which is also called proton flare. To produce such an eruptive event, large amount energy must be accumulated within the active region. So we can investigate the character of the active region and its evolving trend, together with other such as cm radio emission and soft X-ray background to evaluate the potential of SEP in chosen area. In order to summarize the omen of SPEs in the active regions behind the observed parameters, we employed AI technology. Full connecting neural network was chosen to fulfil this job. After constructing the network, we train it with 13 parameters that was able to exhibit the character of active regions and their evolution trend. More than 80 sets of event parameter were defined to teach the neural network to identify whether an active region was potential of SPE. Then we test this model with a data base consisting SPE and non-SPE cases that was not used to train the neural network. The result showed that 75% of the choice by the model was right.

  5. Search for predictive generic model of aqueous solubility using Bayesian neural nets.

    PubMed

    Bruneau, P

    2001-01-01

    Several predictive models of aqueous solubility have been published. They have good performances on the data sets which have been used for training the models, but usually these data sets do not contain many structures similar to the structures of interest to the drug research and their applicability in drug hunting is questionable. A very diverse data set has been gathered with compounds issued from literature reports and proprietary compounds. These compounds have been grouped in a so-called literature data set I, a proprietary data set II, and a mixed data set III formed by I and II. About 100 descriptors emphasizing surface properties were calculated for every compound. Bayesian learning of neural nets which cumulates the advantages of neural nets without having their weaknesses was used to select the most parsimonious models and train them, from I, II, and III. The models were established by either selecting the most efficient descriptors one by one using a modified Gram-Schmidt procedure (GS) or by simplifying a most complete model using automatic relevance procedure (ARD). The predictive ability of the models was accessed using validation data sets as much unrelated to the training sets as possible, using two new parameters: NDD(x,ref) the normalized smallest descriptor distance of a compound x to a reference data set and CD(x,mod) the combination of NDD(x,ref) with the dispersion of the Bayesian neural nets calculations. The results show that it is possible to obtain a generic predictive model from database I but that the diversity of database II is too restricted to give a model with good generalization ability and that the ARD method applied to the mixed database III gives the best predictive model. PMID:11749587

  6. Architecture and biological applications of artificial neural networks: a tuberculosis perspective.

    PubMed

    Darsey, Jerry A; Griffin, William O; Joginipelli, Sravanthi; Melapu, Venkata Kiran

    2015-01-01

    Advancement of science and technology has prompted researchers to develop new intelligent systems that can solve a variety of problems such as pattern recognition, prediction, and optimization. The ability of the human brain to learn in a fashion that tolerates noise and error has attracted many researchers and provided the starting point for the development of artificial neural networks: the intelligent systems. Intelligent systems can acclimatize to the environment or data and can maximize the chances of success or improve the efficiency of a search. Due to massive parallelism with large numbers of interconnected processers and their ability to learn from the data, neural networks can solve a variety of challenging computational problems. Neural networks have the ability to derive meaning from complicated and imprecise data; they are used in detecting patterns, and trends that are too complex for humans, or other computer systems. Solutions to the toughest problems will not be found through one narrow specialization; therefore we need to combine interdisciplinary approaches to discover the solutions to a variety of problems. Many researchers in different disciplines such as medicine, bioinformatics, molecular biology, and pharmacology have successfully applied artificial neural networks. This chapter helps the reader in understanding the basics of artificial neural networks, their applications, and methodology; it also outlines the network learning process and architecture. We present a brief outline of the application of neural networks to medical diagnosis, drug discovery, gene identification, and protein structure prediction. We conclude with a summary of the results from our study on tuberculosis data using neural networks, in diagnosing active tuberculosis, and predicting chronic vs. infiltrative forms of tuberculosis. PMID:25502388

  7. Temporal and Spatial prediction of groundwater levels using Artificial Neural Networks, Fuzzy logic and Kriging interpolation.

    NASA Astrophysics Data System (ADS)

    Tapoglou, Evdokia; Karatzas, George P.; Trichakis, Ioannis C.; Varouchakis, Emmanouil A.

    2014-05-01

    The purpose of this study is to examine the use of Artificial Neural Networks (ANN) combined with kriging interpolation method, in order to simulate the hydraulic head both spatially and temporally. Initially, ANNs are used for the temporal simulation of the hydraulic head change. The results of the most appropriate ANNs, determined through a fuzzy logic system, are used as an input for the kriging algorithm where the spatial simulation is conducted. The proposed algorithm is tested in an area located across Isar River in Bayern, Germany and covers an area of approximately 7800 km2. The available data extend to a time period from 1/11/2008 to 31/10/2012 (1460 days) and include the hydraulic head at 64 wells, temperature and rainfall at 7 weather stations and surface water elevation at 5 monitoring stations. One feedforward ANN was trained for each of the 64 wells, where hydraulic head data are available, using a backpropagation algorithm. The most appropriate input parameters for each wells' ANN are determined considering their proximity to the measuring station, as well as their statistical characteristics. For the rainfall, the data for two consecutive time lags for best correlated weather station, as well as a third and fourth input from the second best correlated weather station, are used as an input. The surface water monitoring stations with the three best correlations for each well are also used in every case. Finally, the temperature for the best correlated weather station is used. Two different architectures are considered and the one with the best results is used henceforward. The output of the ANNs corresponds to the hydraulic head change per time step. These predictions are used in the kriging interpolation algorithm. However, not all 64 simulated values should be used. The appropriate neighborhood for each prediction point is constructed based not only on the distance between known and prediction points, but also on the training and testing error of

  8. Expert system for heart function based on artificial neural networks and fuzzy theory

    NASA Astrophysics Data System (ADS)

    Yu, Wei; Li, Xiaoying; Yu, Daoyin; Mao, Yi; Hua, Qi

    1998-09-01

    In this paper, a computer-aided diagnosis system for heart function based on artificial neural networks and fuzzy logic is introduced. Typical parameters reflecting heart function, provided by echocardiography, were used as input of neural networks and their corresponding heart functions as output. To obtain an analytic and discrimination model closer to brain, we combined fuzzy theory with neural network technology, and input parameters are fuzzily treated. During distinguishing morbid style, we used fuzzy interval, fuzzy number and its related possibility distribution concepts, and selected appropriate operator, and so get its corresponding membership, meanwhile membership was put out of interval of linguistic to consist with language expression. The network selected was BP, and back- propagation algorithm was used to train the network. After studying the result evaluated by expert, the neural network was used to appreciate 150 testees' heart function, of which 90.7% was consistent with experts' diagnosis.

  9. Forecasting SPEI and SPI Drought Indices Using the Integrated Artificial Neural Networks

    PubMed Central

    Maca, Petr; Pech, Pavel

    2016-01-01

    The presented paper compares forecast of drought indices based on two different models of artificial neural networks. The first model is based on feedforward multilayer perceptron, sANN, and the second one is the integrated neural network model, hANN. The analyzed drought indices are the standardized precipitation index (SPI) and the standardized precipitation evaporation index (SPEI) and were derived for the period of 1948–2002 on two US catchments. The meteorological and hydrological data were obtained from MOPEX experiment. The training of both neural network models was made by the adaptive version of differential evolution, JADE. The comparison of models was based on six model performance measures. The results of drought indices forecast, explained by the values of four model performance indices, show that the integrated neural network model was superior to the feedforward multilayer perceptron with one hidden layer of neurons. PMID:26880875

  10. Tuning the stator resistance of induction motors using artificial neural network

    SciTech Connect

    Cabrera, L.A.; Elbuluk, M.E.; Husain, I.

    1997-09-01

    Tuning the stator resistance of induction motors is very important, especially when it is used to implement direct torque control (DTC) in which the stator resistance is a main parameter. In this paper, an artificial network (ANN) is used to accomplish tuning of the stator resistance of an induction motor. The parallel recursive prediction error and backpropagation training algorithms were used in training the neural network for the simulation and experimental results, respectively. The neural network used to tune the stator resistance was trained on-line, making the DTC strategy more robust and accurate. Simulation results are presented for three different neural-network configurations showing the efficiency of the tuning process. Experimental results were obtained for the one of the three neural-network configuration. Both simulation and experimental results showed that the ANN have tuned the stator resistance in the controller to track actual resistance of the machine.

  11. Forecasting SPEI and SPI Drought Indices Using the Integrated Artificial Neural Networks.

    PubMed

    Maca, Petr; Pech, Pavel

    2016-01-01

    The presented paper compares forecast of drought indices based on two different models of artificial neural networks. The first model is based on feedforward multilayer perceptron, sANN, and the second one is the integrated neural network model, hANN. The analyzed drought indices are the standardized precipitation index (SPI) and the standardized precipitation evaporation index (SPEI) and were derived for the period of 1948-2002 on two US catchments. The meteorological and hydrological data were obtained from MOPEX experiment. The training of both neural network models was made by the adaptive version of differential evolution, JADE. The comparison of models was based on six model performance measures. The results of drought indices forecast, explained by the values of four model performance indices, show that the integrated neural network model was superior to the feedforward multilayer perceptron with one hidden layer of neurons. PMID:26880875

  12. Optical imaging of fluorescent carbon biomarkers using artificial neural networks

    NASA Astrophysics Data System (ADS)

    Dolenko, Tatiana A.; Burikov, Sergey A.; Vervald, Alexey M.; Vlasov, Igor I.; Dolenko, Sergey A.; Laptinskiy, Kirill A.; Rosenholm, Jessica M.; Shenderova, Olga A.

    2014-11-01

    The principle possibility of extraction of fluorescence of nanoparticles in the presence of background autofluorescence of a biological environment using neural network algorithms is demonstrated. It is shown that the methods used allow detection of carbon nanoparticles fluorescence against the background of the autofluorescence of egg white with a sufficiently low concentration detection threshold (not more than 2 μg/ml for carbon dots and 3 μg/ml for nanodiamonds). It was also shown that the use of the input data compression can further improve the accuracy of solving the inverse problem by 1.5 times.

  13. Development of Global Precipitation Estimation System Using Artificial Neural Network Models

    NASA Astrophysics Data System (ADS)

    Hsu, K. L.

    2015-12-01

    The PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network) system, developed at UC Irvine, is one unique source to estimate global precipitation in near real-time using infrared and passive microwave information from Geosynchronous Earth Orbital (GEO) and Low Earth Orbital (LEO) satellites. The algorithm uses an Artificial Neural Network to extract cold cloud pixels and neighboring features from GEO-satellites' infrared images to generate rain rate. The precipitation estimates from the neural network are further adjusted by the PMW precipitation estimates produced using the data from LEO satellites. The operational PERSIANN system estimates global precipitation in near real-time. Data sources are also extended to the reconstruction of historical data for the past 30 years for hydroclimate studies. Continuing development of precipitation retrieval using artificial neural network models and advanced machine learning methods are ongoing. Studies including effective feature extraction from satellite multiple spectral imagery, integration of multiple satellite information, and merge of ground and satellite precipitation retrievals. Evaluation of PERSIANN precipitation and its application for catchment scale hydrologic simulation will be discussed.

  14. Pyrolysis patterns of 5 close Corynebacterium species analyzed by artificial neural networks.

    PubMed

    Voisin, Sébastien; Terreux, Raphaël; Renaud, François N R; Freney, Jean; Domard, Monique; Deruaz, Daniel

    2004-05-01

    In the present study, an artificial neural network was trained with the Stuttgart Neural Networks Simulator, in order to identify Corynebacterium species by analyzing their pyrolysis patterns. An earlier study described the combination of pyrolysis, gas chromatography and atomic emission detection we used on whole cell bacteria. Carbon, sulfur and nitrogen were detected in the pyrolysis compounds. Pyrolysis patterns were obtained from 52 Corynebacterium strains belonging to 5 close species. These data were previously analyzed by Euclidean distances calculation followed by Unweighted Pair Group Method of Averages, a clustering method. With this early method, strains from 3 of the 5 species (C. xerosis, C. freneyi and C. amycolatum) were correctly characterized even if the 29 strains of C. amycolatum were grouped into 2 subgroups. Strains from the 2 remaining species (C. minutissimum and C. striatum) cannot be separated. To build an artificial neural network, able to discriminate the 5 previous species, the pyrolysis data of 42 selected strains were used as learning set and the 10 remaining strains as testing set. The chosen learning algorithm was Back-Propagation with Momentum. Parameters used to train a correct network are described here, and the results analyzed. The obtained artificial neural network has the following cone-shaped structure: 144 nodes in input, 25 and 9 nodes in 2 successive hidden layers, and then 5 outputs. It could classify all the strains in their species group. This network completes a chemotaxonomic method for Corynebacterium identification. PMID:15028867

  15. Biological and bionic hands: natural neural coding and artificial perception

    PubMed Central

    Bensmaia, Sliman J.

    2015-01-01

    The first decade and a half of the twenty-first century brought about two major innovations in neuroprosthetics: the development of anthropomorphic robotic limbs that replicate much of the function of a native human arm and the refinement of algorithms that decode intended movements from brain activity. However, skilled manipulation of objects requires somatosensory feedback, for which vision is a poor substitute. For upper-limb neuroprostheses to be clinically viable, they must therefore provide for the restoration of touch and proprioception. In this review, I discuss efforts to elicit meaningful tactile sensations through stimulation of neurons in somatosensory cortex. I focus on biomimetic approaches to sensory restoration, which leverage our current understanding about how information about grasped objects is encoded in the brain of intact individuals. I argue that not only can sensory neuroscience inform the development of sensory neuroprostheses, but also that the converse is true: stimulating the brain offers an exceptional opportunity to causally interrogate neural circuits and test hypotheses about natural neural coding. PMID:26240424

  16. Biological and bionic hands: natural neural coding and artificial perception.

    PubMed

    Bensmaia, Sliman J

    2015-09-19

    The first decade and a half of the twenty-first century brought about two major innovations in neuroprosthetics: the development of anthropomorphic robotic limbs that replicate much of the function of a native human arm and the refinement of algorithms that decode intended movements from brain activity. However, skilled manipulation of objects requires somatosensory feedback, for which vision is a poor substitute. For upper-limb neuroprostheses to be clinically viable, they must therefore provide for the restoration of touch and proprioception. In this review, I discuss efforts to elicit meaningful tactile sensations through stimulation of neurons in somatosensory cortex. I focus on biomimetic approaches to sensory restoration, which leverage our current understanding about how information about grasped objects is encoded in the brain of intact individuals. I argue that not only can sensory neuroscience inform the development of sensory neuroprostheses, but also that the converse is true: stimulating the brain offers an exceptional opportunity to causally interrogate neural circuits and test hypotheses about natural neural coding. PMID:26240424

  17. BIAS-FREE SHEAR ESTIMATION USING ARTIFICIAL NEURAL NETWORKS

    SciTech Connect

    Gruen, D.; Seitz, S.; Koppenhoefer, J.; Riffeser, A.

    2010-09-01

    Bias due to imperfect shear calibration is the biggest obstacle when constraints on cosmological parameters are to be extracted from large area weak lensing surveys such as Pan-STARRS-3{pi}, DES, or future satellite missions like EUCLID. We demonstrate that bias present in existing shear measurement pipelines (e.g., KSB) can be almost entirely removed by means of neural networks. In this way, bias correction can depend on the properties of the individual galaxy instead of being a single global value. We present a procedure to train neural networks for shear estimation and apply this to subsets of simulated GREAT08 RealNoise data. We also show that circularization of the point-spread function (PSF) before measuring the shear reduces the scatter related to the PSF anisotropy correction and thus leads to improved measurements, particularly on low and medium signal-to-noise data. Our results are competitive with the best performers in the GREAT08 competition, especially for the medium and higher signal-to-noise sets. Expressed in terms of the quality parameter defined by GREAT08, we achieve a Q{approx} 40, 140, and 1300 without and 50, 200, and 1300 with circularization for low, medium, and high signal-to-noise data sets, respectively.

  18. Application of artificial neural networks to predict the deflections of reinforced concrete beams

    NASA Astrophysics Data System (ADS)

    Kaczmarek, Mateusz; Szymańska, Agnieszka

    2016-06-01

    Nonlinear structural mechanics should be taken into account in the practical design of reinforced concrete structures. Cracking is one of the major sources of nonlinearity. Description of deflection of reinforced concrete elements is a computational problem, mainly because of the difficulties in modelling the nonlinear stress-strain relationship of concrete and steel. In design practise, in accordance with technical rules (e.g., Eurocode 2), a simplified approach for reinforced concrete is used, but the results of simplified calculations differ from the results of experimental studies. Artificial neural network is a versatile modelling tool capable of making predictions of values that are difficult to obtain in numerical analysis. This paper describes the creation and operation of a neural network for making predictions of deflections of reinforced concrete beams at different load levels. In order to obtain a database of results, that is necessary for training and testing the neural network, a research on measurement of deflections in reinforced concrete beams was conducted by the authors in the Certified Research Laboratory of the Building Engineering Institute at Wrocław University of Science and Technology. The use of artificial neural networks is an innovation and an alternative to traditional methods of solving the problem of calculating the deflections of reinforced concrete elements. The results show the effectiveness of using artificial neural network for predicting the deflection of reinforced concrete beams, compared with the results of calculations conducted in accordance with Eurocode 2. The neural network model presented in this paper can acquire new data and be used for further analysis, with availability of more research results.

  19. Modeling discharge-sediment relationship using neural networks with artificial bee colony algorithm

    NASA Astrophysics Data System (ADS)

    Kisi, Ozgur; Ozkan, Coskun; Akay, Bahriye

    2012-03-01

    SummaryEstimation of suspended sediment concentration carried by a river is very important for many water resources projects. The accuracy of artificial neural networks (ANN) with artificial bee colony (ABC) algorithm is investigated in this paper for modeling discharge-suspended sediment relationship. The ANN-ABC was compared with those of the neural differential evolution, adaptive neuro-fuzzy, neural networks and rating curve models. The daily stream flow and suspended sediment concentration data from two stations, Rio Valenciano Station and Quebrada Blanca Station, were used as case studies. For evaluating the ability of the models, mean square error and determination coefficient criteria were used. Comparison results showed that the ANN-ABC was able to produce better results than the neural differential evolution, neuro-fuzzy, neural networks and rating curve models. The logarithm transformed data were also used as input to the proposed ANN-ABC models. It was found that the logarithm transform significantly increased accuracy of the models in suspended sediment estimation.

  20. Segmentation of anatomical structures in x-ray computed tomography images using artificial neural networks

    NASA Astrophysics Data System (ADS)

    Zhang, Di; Valentino, Daniel J.

    2002-05-01

    Hierarchies of artificial neural networks(ANN's) were trained to segment regularly-shaped and constantly-located anatomical structures in x-ray computed tomography (CT) images. These neural networks learned to associate a point in an image with the anatomical structure containing the point using the image pixel intensity values located in a pattern around the point. The single layer ANN and the bilayer and multi-layer hierarchies of neural networks were developed and evaluated. The hierarchical Artificial Neural Networks(HANN's) consisted of a high-level ANN that identified large-scale anatomical structures (e.g., the head or chest), whose result was passed to a group of neural networks that identified smaller structures (e.g., the brain, sinus, soft tissue, skull, bone, or lung) within the large-scale structures. The ANN's were trained to segment and classify images based on different numbers of training images, numbers of sampling points per image, pixel intensity sampling patterns, hidden layer configuration. The experimental results indicate that multi-layer hierarchy of ANN's trained with data collected from multiple image series accurately classified anatomical structures in unknown chest and head CT images.

  1. THE CHOICE OF OPTIMAL STRUCTURE OF ARTIFICIAL NEURAL NETWORK CLASSIFIER INTENDED FOR CLASSIFICATION OF WELDING FLAWS

    SciTech Connect

    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.

  2. Predictive non-linear modeling of complex data by artificial neural networks.

    PubMed

    Almeida, Jonas S

    2002-02-01

    An artificial neural network (ANN) is an artificial intelligence tool that identifies arbitrary nonlinear multiparametric discriminant functions directly from experimental data. The use of ANNs has gained increasing popularity for applications where a mechanistic description of the dependency between dependent and independent variables is either unknown or very complex. This machine learning technique can be roughly described as a universal algebraic function that will distinguish signal from noise directly from experimental data. The application of ANNs to complex relationships makes them highly attractive for the study of biological systems. Recent applications include the analysis of expression profiles and genomic and proteomic sequences. PMID:11849962

  3. A Rapid Aerodynamic Design Procedure Based on Artificial Neural Networks

    NASA Technical Reports Server (NTRS)

    Rai, Man Mohan

    2001-01-01

    An aerodynamic design procedure that uses neural networks to model the functional behavior of the objective function in design space has been developed. This method incorporates several improvements to an earlier method that employed a strategy called parameter-based partitioning of the design space in order to reduce the computational costs associated with design optimization. As with the earlier method, the current method uses a sequence of response surfaces to traverse the design space in search of the optimal solution. The new method yields significant reductions in computational costs by using composite response surfaces with better generalization capabilities and by exploiting synergies between the optimization method and the simulation codes used to generate the training data. These reductions in design optimization costs are demonstrated for a turbine airfoil design study where a generic shape is evolved into an optimal airfoil.

  4. Application of artificial neural networks to thermal detection of disbonds

    NASA Technical Reports Server (NTRS)

    Prabhu, D. R.; Howell, P. A.; Syed, H. I.; Winfree, W. P.

    1992-01-01

    A novel technique for processing thermal data is presented and applied to simulation as well as experimental data. Using a neural network of thermal data classification, good classification accuracies are obtained, and the resulting images exhibit very good contrast between bonded and disbonded locations. In order to minimize the preprocessing required before using the network of classification, the temperature values were directly employed to train a network using data from an on-site testing run of a commercial aircraft. Training was extremely fast, and the resulting classification also agreed reasonably well with an ultrasonic characterization of the panel. The results obtained using one sample show the partially disbonded vertical doubler. The vertical lines along the doubler correspond to the original extent of the doubler obtained using blueprints of the aircraft.

  5. Signal recognition efficiencies of artificial neural-network pulse-shape discrimination in HPGe -decay searches

    NASA Astrophysics Data System (ADS)

    Caldwell, A.; Cossavella, F.; Majorovits, B.; Palioselitis, D.; Volynets, O.

    2015-07-01

    A pulse-shape discrimination method based on artificial neural networks was applied to pulses simulated for different background, signal and signal-like interactions inside a germanium detector. The simulated pulses were used to investigate variations of efficiencies as a function of used training set. It is verified that neural networks are well-suited to identify background pulses in true-coaxial high-purity germanium detectors. The systematic uncertainty on the signal recognition efficiency derived using signal-like evaluation samples from calibration measurements is estimated to be 5 %. This uncertainty is due to differences between signal and calibration samples.

  6. Application of Artificial Neural Networks to Investigate the Energy Performance of Household Refrigerator-Freezers

    NASA Astrophysics Data System (ADS)

    Saidur, R.; Masjuki, H. H.

    In this study, the energy consumption of 149 domestic refrigerators has been monitored in Malaysian households. A questionnaire was used to get relevant information regarding the usage of this appliance in the actual kitchen environment to feed into neural networks. Prediction performance of Artificial Neural Networks (ANN) approach was investigated using actual monitored and survey data. Statistical analyses in terms of fraction of variance R2, Coefficient of Variation (COV), RMS are calculated to judge the performance of NN model. It has been found that the regression coefficient R2 is very close to unity for the best prediction performance results.

  7. Direct photon identification with artificial neural network in the photon spectrometer PHOS

    NASA Astrophysics Data System (ADS)

    Bogolyubsky, M. Yu.; Kharlov, Yu. V.; Sadovsky, S. A.

    2003-04-01

    A neural network method is developed to separate direct photons from neutral pions in the PHOS spectrometer of the ALICE experiment at the LHC collider. The neural net has been taught to distinguish different classes of events by analyzing the energy profile tensor of a cluster in its eigenvector coordinate system. The Monte-Carlo simulation shows that this method diminishes the probability of π 0-meson misidentification as a photon by an order compared with the direct photon detection efficiency in the energy range up to 120 GeV.

  8. Application of neural nets to system identification and bifurcation analysis of real world experimental data

    SciTech Connect

    Adomaitis, R.A.; Kevrekidis, I.G. . Dept. of Chemical Engineering); Farber, R.M.; Lapedes, A.S. ); Hudson, J.L.; Kube, M. . Dept. of Chemical Engineering)

    1990-02-01

    We report results on the use of neural nets, and the closely related radial basis nets'', to analyze experimental time series from electro-chemical systems. We show how the nets may be used to derive a map that describes the nonlinear system, and how reserving an extra input line'' of the network allows one to learn the system behavior dependent on a control variable. Pruning'' of the network after training appears to result in elimination of spurious connection weights and enhanced predictive accuracy. Subsequent analysis of the learned map using techniques of bifurcation theory allows both nonlinear system identification and accurate and efficient predictions of long-term system behavior. The electrochemical system that was used involved the electrodissolution of copper in phosphoric acid. This system exhibits interesting low dimensional dynamics such transitions from steady state to oscillatory behavior and from period-one to period-two oscillations. This analysis provides an example of methodology that can be fruitful in understanding systems for which no adequate phenomenological model exists, or for which predictions of system behavior given a large scale, complicated model is inherently impractical. 17 refs., 2 figs.

  9. Modeling the thermotaxis behavior of C.elegans based on the artificial neural network.

    PubMed

    Li, Mingxu; Deng, Xin; Wang, Jin; Chen, Qiaosong; Tang, Yun

    2016-07-01

    ASBTRACT This research aims at modeling the thermotaxis behavior of C.elegans which is a kind of nematode with full clarified neuronal connections. Firstly, this work establishes the motion model which can perform the undulatory locomotion with turning behavior. Secondly, the thermotaxis behavior is modeled by nonlinear functions and the nonlinear functions are learned by artificial neural network. Once the artificial neural networks have been well trained, they can perform the desired thermotaxis behavior. Last, several testing simulations are carried out to verify the effectiveness of the model for thermotaxis behavior. This work also analyzes the different performances of the model under different environments. The testing results reveal the essence of the thermotaxis of C.elegans to some extent, and theoretically support the research on the navigation of the crawling robots. PMID:27286293

  10. Damage identification for large span structure based on multiscale inputs to artificial neural networks.

    PubMed

    Lu, Wei; Teng, Jun; Cui, Yan

    2014-01-01

    In structural health monitoring system, little research on the damage identification from different types of sensors applied to large span structure has been done in the field. In fact, it is significant to estimate the whole structural safety if the multitype sensors or multiscale measurements are used in application of structural health monitoring and the damage identification for large span structure. A methodology to combine the local and global measurements in noisy environments based on artificial neural network is proposed in this paper. For a real large span structure, the capacity of the methodology is validated, including the decision on damage placement, the discussions on the number of the sensors, and the optimal parameters for artificial neural networks. Furthermore, the noisy environments in different levels are simulated to demonstrate the robustness and effectiveness of the proposed approach. PMID:24977207

  11. A study on precursors leading to geomagnetic storms using artificial neural network

    NASA Astrophysics Data System (ADS)

    Singh, Gaurav; Singh, A. K.

    2016-06-01

    Space weather prediction involves advance forecasting of the magnitude and onset time of major geomagnetic storms on Earth. In this paper, we discuss the development of an artificial neural network-based model to study the precursor leading to intense and moderate geomagnetic storms, following halo coronal mass ejection (CME) and related interplanetary (IP) events. IP inputs were considered within a 5-day time window after the commencement of storm. The artificial neural network (ANN) model training, testing and validation datasets were constructed based on 110 halo CMEs (both full and partial halo and their properties) observed during the ascending phase of the 24th solar cycle between 2009 and 2014. The geomagnetic storm occurrence rate from halo CMEs is estimated at a probability of 79%, by this model.

  12. Echo state networks as an alternative to traditional artificial neural networks in rainfall-runoff modelling

    NASA Astrophysics Data System (ADS)

    de Vos, N. J.

    2013-01-01

    Despite theoretical benefits of recurrent artificial neural networks over their feedforward counterparts, it is still unclear whether the former offer practical advantages as rainfall-runoff models. The main drawback of recurrent networks is the increased complexity of the training procedure due to their architecture. This work uses the recently introduced and conceptually simple echo state networks for streamflow forecasts on twelve river basins in the Eastern United States, and compares them to a variety of traditional feedforward and recurrent approaches. Two modifications on the echo state network models are made that increase the hydrologically relevant information content of their internal state. The results show that the echo state networks outperform feedforward networks and are competitive with state-of-the-art recurrent networks, across a range of performance measures. This, along with their simplicity and ease of training, suggests that they can be considered promising alternatives to traditional artificial neural networks in rainfall-runoff modelling.

  13. Biologically inspired intelligent decision making: a commentary on the use of artificial neural networks in bioinformatics.

    PubMed

    Manning, Timmy; Sleator, Roy D; Walsh, Paul

    2014-01-01

    Artificial neural networks (ANNs) are a class of powerful machine learning models for classification and function approximation which have analogs in nature. An ANN learns to map stimuli to responses through repeated evaluation of exemplars of the mapping. This learning approach results in networks which are recognized for their noise tolerance and ability to generalize meaningful responses for novel stimuli. It is these properties of ANNs which make them appealing for applications to bioinformatics problems where interpretation of data may not always be obvious, and where the domain knowledge required for deductive techniques is incomplete or can cause a combinatorial explosion of rules. In this paper, we provide an introduction to artificial neural network theory and review some interesting recent applications to bioinformatics problems. PMID:24335433

  14. Hardware-based artificial neural networks for size, weight, and power constrained platforms

    NASA Astrophysics Data System (ADS)

    Wysocki, B. T.; McDonald, N. R.; Thiem, C. D.

    2014-05-01

    A fully parallel, silicon-based artificial neural network (CM1K) built on zero instruction set computer (ZISC) technology was used for change detection and object identification in video data. Fundamental pattern recognition capabilities were demonstrated with reduced neuron numbers utilizing only a few, or in some cases one, neuron per category. This simplified approach was used to validate the utility of few neuron networks for use in applications that necessitate severe size, weight, and power (SWaP) restrictions. The limited resource requirements and massively parallel nature of hardware-based artificial neural networks (ANNs) make them superior to many software approaches in resource limited systems, such as micro-UAVs, mobile sensor platforms, and pocket-sized robots.

  15. Determination of type and concentration of DNA nitrogenous bases by Raman spectroscopy using artificial neural networks

    NASA Astrophysics Data System (ADS)

    Laptinskiy, Kirill A.; Burikov, Sergey A.; Sarmanova, Olga E.; Dolenko, Sergey A.; Dolenko, Tatiana A.

    2016-04-01

    In this article the results of solution of two-parametrical inverse problems of laser Raman spectroscopy of identification and determination of concentration of DNA nitrogenous bases in two-component solutions are presented. Elaboration of methods of control of reactions with DNA strands in remote real-time mode is necessary for solution of one of the basic problems of creation of biocomputers - increase of reliability of molecular DNA-computations. The comparative analysis of two used methods of solution of stated problems has demonstrated convincing advantages of technique of artificial neural networks. Use of artificial neural networks allowed to reach the accuracy of determination of concentration of each base in two-component solutions 0.2-0.3 g/l.

  16. Evolution of an artificial neural network based autonomous land vehicle controller.

    PubMed

    Baluja, S

    1996-01-01

    This paper presents an evolutionary method for creating an artificial neural network based autonomous land vehicle controller. The evolved controllers perform better in unseen situations than those trained with an error backpropagation learning algorithm designed for this task. In this paper, an overview of the previous connectionist based approaches to this task is given, and the evolutionary algorithms used in this study are described in detail. Methods for reducing the high computational costs of training artificial neural networks with evolutionary algorithms are explored. Error metrics specific to the task of autonomous vehicle control are introduced; the evolutionary algorithms guided by these error metrics reveal improved performance over those guided by the standard sum-squared error metric. Finally, techniques for integrating evolutionary search and error backpropagation are presented. The evolved networks are designed to control Carnegie Mellon University's NAVLAB vehicles in road following tasks. PMID:18263046

  17. A study on precursors leading to geomagnetic storms using artificial neural network

    NASA Astrophysics Data System (ADS)

    Singh, Gaurav; Singh, A. K.

    2016-07-01

    Space weather prediction involves advance forecasting of the magnitude and onset time of major geomagnetic storms on Earth. In this paper, we discuss the development of an artificial neural network-based model to study the precursor leading to intense and moderate geomagnetic storms, following halo coronal mass ejection (CME) and related interplanetary (IP) events. IP inputs were considered within a 5-day time window after the commencement of storm. The artificial neural network (ANN) model training, testing and validation datasets were constructed based on 110 halo CMEs (both full and partial halo and their properties) observed during the ascending phase of the 24th solar cycle between 2009 and 2014. The geomagnetic storm occurrence rate from halo CMEs is estimated at a probability of 79%, by this model.

  18. A Red-Light Running Prevention System Based on Artificial Neural Network and Vehicle Trajectory Data

    PubMed Central

    Li, Pengfei; Li, Yan; Guo, Xiucheng

    2014-01-01

    The high frequency of red-light running and complex driving behaviors at the yellow onset at intersections cannot be explained solely by the dilemma zone and vehicle kinematics. In this paper, the author presented a red-light running prevention system which was based on artificial neural networks (ANNs) to approximate the complex driver behaviors during yellow and all-red clearance and serve as the basis of an innovative red-light running prevention system. The artificial neural network and vehicle trajectory are applied to identify the potential red-light runners. The ANN training time was also acceptable and its predicting accurate rate was over 80%. Lastly, a prototype red-light running prevention system with the trained ANN model was described. This new system can be directly retrofitted into the existing traffic signal systems. PMID:25435870

  19. Damage Identification for Large Span Structure Based on Multiscale Inputs to Artificial Neural Networks

    PubMed Central

    Teng, Jun; Cui, Yan

    2014-01-01

    In structural health monitoring system, little research on the damage identification from different types of sensors applied to large span structure has been done in the field. In fact, it is significant to estimate the whole structural safety if the multitype sensors or multiscale measurements are used in application of structural health monitoring and the damage identification for large span structure. A methodology to combine the local and global measurements in noisy environments based on artificial neural network is proposed in this paper. For a real large span structure, the capacity of the methodology is validated, including the decision on damage placement, the discussions on the number of the sensors, and the optimal parameters for artificial neural networks. Furthermore, the noisy environments in different levels are simulated to demonstrate the robustness and effectiveness of the proposed approach. PMID:24977207

  20. Modeling of severe geomagnetic storms of solar cycle 23 by means of artificial neural networks

    NASA Astrophysics Data System (ADS)

    Revallo, Milos; Valach, Fridrich; Hejda, Pavel; Bochnicek, Josef

    2015-04-01

    We set up a model for strong geomagnetic storms of solar cycle 23 using the method of artificial neural networks combined with an empirical model of the solar wind magnetosphere interaction. The set of solar wind data obtained from the ACE satellite is considered and the corresponding geomagnetic response is modeled and compared with real data. The discontinuity in magnetic field at the magnetopause is shown to play a key role in this study. The geomagnetic response is evaluated in terms of the Dst index. To assess the model performance, we compute the skill scores, namely the correlation coefficient and the prediction efficiency. We compare the model with previously known similar models based on artificial neural networks.

  1. Suspended Sediment Forecasting in Gao-Pen River Using Artificial Neural Network

    NASA Astrophysics Data System (ADS)

    Chiang, Jie-Lun; Yeh, Chia-Hsun

    2010-05-01

    River suspended sediment yield has a strong relationship to the river discharge. Linear regression and exponential regression models were most commonly used approaches to estimate sediment yield from river discharge. However, the accuracy is not precise enough, and the same discharge may have different sediment concentrations due to discharge rise and recession. In these years, Artificial Neural Networks(ANN) are applied for forecasting and classification in many fields and lead to good performance. Multilayer perceptrons of back propagation(BP) develop completely and are used for forecasting successfully in the past researches. In this study, Artificial Neural Network (BP) was used to build discharge-sediment model for forecasting suspended sediment in Gao-Pen river. The results show that ANN outperformed linear regression model and power regression model.

  2. Prediction of IV curves for a superconducting thin film using artificial neural networks

    NASA Astrophysics Data System (ADS)

    Kamran, M.; Haider, S. A.; Akram, T.; Naqvi, S. R.; He, S. K.

    2016-07-01

    We propose a framework using artificial neural networks that predicts the IV characteristics of a superconducting thin film with square array of nano-engineered periodic antidots, called holes. We adopt the conventionally used commercial physical properties measurement system to obtain a dataset comprising transport measurements, and use this dataset to train our artificial neural network. Once trained, the model is capable of predicting the curve for varying temperature and magnetic flux values, which are cross validated by the physical properties measurement system. Consistent with the works in literature, our framework suggests Josephson Junctions like behavior near transition temperature and at stronger magnetic fields. Our study is important since repeated measurements using the conventional method are time consuming and costly; we demonstrate that the proposed method may be effectively used to classify the IV characteristics over a wide range of temperature and magnetic field values.

  3. On the properties of artificial neural network filters for bone-suppressed digital radiography

    NASA Astrophysics Data System (ADS)

    Park, Eunpyeong; Park, Junbeom; Kim, Daecheon; Youn, Hanbean; Jeon, Hosang; Kim, Jin Sung; Kang, Dong-Joong; Kim, Ho Kyung

    2016-04-01

    Dual-energy imaging can enhance lesion conspicuity. However, the conventional (fast kilovoltage switching) dual-shot dual-energy imaging is vulnerable to patient motion. The single-shot method requires a special design of detector system. Alternatively, single-shot bone-suppressed imaging is possible using post-image processing combined with a filter obtained from training an artificial neural network. In this study, the authors investigate the general properties of artificial neural network filters for bone-suppressed digital radiography. The filter properties are characterized in terms of various parameters such as the size of input vector, the number of hidden units, the learning rate, and so on. The preliminary result shows that the bone-suppressed image obtained from the filter, which is designed with 5,000 teaching images from a single radiograph, results in about 95% similarity with a commercial bone-enhanced image.

  4. Automatic event detection based on artificial neural networks

    NASA Astrophysics Data System (ADS)

    Doubravová, Jana; Wiszniowski, Jan; Horálek, Josef

    2015-04-01

    The proposed algorithm was developed to be used for Webnet, a local seismic network in West Bohemia. The Webnet network was built to monitor West Bohemia/Vogtland swarm area. During the earthquake swarms there is a large number of events which must be evaluated automatically to get a quick estimate of the current earthquake activity. Our focus is to get good automatic results prior to precise manual processing. With automatic data processing we may also reach a lower completeness magnitude. The first step of automatic seismic data processing is the detection of events. To get a good detection performance we require low number of false detections as well as high number of correctly detected events. We used a single layer recurrent neural network (SLRNN) trained by manual detections from swarms in West Bohemia in the past years. As inputs of the SLRNN we use STA/LTA of half-octave filter bank fed by vertical and horizontal components of seismograms. All stations were trained together to obtain the same network with the same neuron weights. We tried several architectures - different number of neurons - and different starting points for training. Networks giving the best results for training set must not be the optimal ones for unknown waveforms. Therefore we test each network on test set from different swarm (but still with similar characteristics, i.e. location, focal mechanisms, magnitude range). We also apply a coincidence verification for each event. It means that we can lower the number of false detections by rejecting events on one station only and force to declare an event on all stations in the network by coincidence on two or more stations. In further work we would like to retrain the network for each station individually so each station will have its own coefficients (neural weights) set. We would also like to apply this method to data from Reykjanet network located in Reykjanes peninsula, Iceland. As soon as we have a reliable detection, we can proceed to

  5. Design of a Thermoacoustic Sensor for Low Intensity Ultrasound Measurements Based on an Artificial Neural Network.

    PubMed

    Xing, Jida; Chen, Jie

    2015-01-01

    In therapeutic ultrasound applications, accurate ultrasound output intensities are crucial because the physiological effects of therapeutic ultrasound are very sensitive to the intensity and duration of these applications. Although radiation force balance is a benchmark technique for measuring ultrasound intensity and power, it is costly, difficult to operate, and compromised by noise vibration. To overcome these limitations, the development of a low-cost, easy to operate, and vibration-resistant alternative device is necessary for rapid ultrasound intensity measurement. Therefore, we proposed and validated a novel two-layer thermoacoustic sensor using an artificial neural network technique to accurately measure low ultrasound intensities between 30 and 120 mW/cm2. The first layer of the sensor design is a cylindrical absorber made of plexiglass, followed by a second layer composed of polyurethane rubber with a high attenuation coefficient to absorb extra ultrasound energy. The sensor determined ultrasound intensities according to a temperature elevation induced by heat converted from incident acoustic energy. Compared with our previous one-layer sensor design, the new two-layer sensor enhanced the ultrasound absorption efficiency to provide more rapid and reliable measurements. Using a three-dimensional model in the K-wave toolbox, our simulation of the ultrasound propagation process demonstrated that the two-layer design is more efficient than the single layer design. We also integrated an artificial neural network algorithm to compensate for the large measurement offset. After obtaining multiple parameters of the sensor characteristics through calibration, the artificial neural network is built to correct temperature drifts and increase the reliability of our thermoacoustic measurements through iterative training about ten seconds. The performance of the artificial neural network method was validated through a series of experiments. Compared to our previous

  6. Design of a Thermoacoustic Sensor for Low Intensity Ultrasound Measurements Based on an Artificial Neural Network

    PubMed Central

    Xing, Jida; Chen, Jie

    2015-01-01

    In therapeutic ultrasound applications, accurate ultrasound output intensities are crucial because the physiological effects of therapeutic ultrasound are very sensitive to the intensity and duration of these applications. Although radiation force balance is a benchmark technique for measuring ultrasound intensity and power, it is costly, difficult to operate, and compromised by noise vibration. To overcome these limitations, the development of a low-cost, easy to operate, and vibration-resistant alternative device is necessary for rapid ultrasound intensity measurement. Therefore, we proposed and validated a novel two-layer thermoacoustic sensor using an artificial neural network technique to accurately measure low ultrasound intensities between 30 and 120 mW/cm2. The first layer of the sensor design is a cylindrical absorber made of plexiglass, followed by a second layer composed of polyurethane rubber with a high attenuation coefficient to absorb extra ultrasound energy. The sensor determined ultrasound intensities according to a temperature elevation induced by heat converted from incident acoustic energy. Compared with our previous one-layer sensor design, the new two-layer sensor enhanced the ultrasound absorption efficiency to provide more rapid and reliable measurements. Using a three-dimensional model in the K-wave toolbox, our simulation of the ultrasound propagation process demonstrated that the two-layer design is more efficient than the single layer design. We also integrated an artificial neural network algorithm to compensate for the large measurement offset. After obtaining multiple parameters of the sensor characteristics through calibration, the artificial neural network is built to correct temperature drifts and increase the reliability of our thermoacoustic measurements through iterative training about ten seconds. The performance of the artificial neural network method was validated through a series of experiments. Compared to our previous

  7. Artificial neural network based on SQUIDs: demonstration of network training and operation

    NASA Astrophysics Data System (ADS)

    Chiarello, F.; Carelli, P.; Castellano, M. G.; Torrioli, G.

    2013-12-01

    We propose a scheme for the realization of artificial neural networks based on superconducting quantum interference devices (SQUIDs). In order to demonstrate the operation of this scheme we designed and successfully tested a small network that implements an XOR gate and is trained by means of examples. The proposed scheme can be particularly convenient as support for superconducting applications such as detectors for astrophysics, high energy experiments, medicine imaging and so on.

  8. A study of artificial neural networks for estimating riverine biodiversity

    NASA Astrophysics Data System (ADS)

    Tsai, W.; Chang, F.; Chiang, Y.

    2010-12-01

    As the population and demand for land use rapidly increased, the use of environmental resources has exceeded the rate of naturalization that might result in the degeneracy of ecological structure and the decrease of the diversities of species which could reduce the resources provided by environment. Due to the raise of eco-environmental restoration concept in the past years, people gradually pay attention to the coexistence relationship between human beings and eco-environment and the impacts of human activities on eco-environment. Stream flow management is the idea that combines the concept of ecology and provides the demand for both human and river ecosystem. This study built the Self-Organizing Radial Basis Neural Networks to categorize the stream flow data and estimate the diversities of fish families in river ecosystem by using Taiwan Ecohydrology Index System (TEIS). In this study there are 60 flow stations, and the stream flow data were only collected with records more than 20 years. A moving average method was applied to TEIS statistics to reflect the effects of antecedent flow condition. The input data are the TEIS statistics of different moving average periods. The output of the model showed a high authenticity of the prediction for the diversities. The result shows that this model can not only categorize the stream flow data but also estimate the bio-diversity efficiently and precisely.

  9. Target discrimination in synthetic aperture radar using artificial neural networks.

    PubMed

    Principe, J C; Kim, M; Fisher, M

    1998-01-01

    This paper addresses target discrimination in synthetic aperture radar (SAR) imagery using linear and nonlinear adaptive networks. Neural networks are extensively used for pattern classification but here the goal is discrimination. We show that the two applications require different cost functions. We start by analyzing with a pattern recognition perspective the two-parameter constant false alarm rate (CFAR) detector which is widely utilized as a target detector in SAR. Then we generalize its principle to construct the quadratic gamma discriminator (QGD), a nonparametrically trained classifier based on local image intensity. The linear processing element of the QCD is further extended with nonlinearities yielding a multilayer perceptron (MLP) which we call the NL-QGD (nonlinear QGD). MLPs are normally trained based on the L(2) norm. We experimentally show that the L(2) norm is not recommended to train MLPs for discriminating targets in SAR. Inspired by the Neyman-Pearson criterion, we create a cost function based on a mixed norm to weight the false alarms and the missed detections differently. Mixed norms can easily be incorporated into the backpropagation algorithm, and lead to better performance. Several other norms (L(8), cross-entropy) are applied to train the NL-QGD and all outperformed the L(2) norm when validated by receiver operating characteristics (ROC) curves. The data sets are constructed from TABILS 24 ISAR targets embedded in 7 km(2) of SAR imagery (MIT/LL mission 90). PMID:18276330

  10. Artificial Neural Network with Hardware Training and Hardware Refresh

    NASA Technical Reports Server (NTRS)

    Duong, Tuan A. (Inventor)

    2003-01-01

    A neural network circuit is provided having a plurality of circuits capable of charge storage. Also provided is a plurality of circuits each coupled to at least one of the plurality of charge storage circuits and constructed to generate an output in accordance with a neuron transfer function. Each of a plurality of circuits is coupled to one of the plurality of neuron transfer function circuits and constructed to generate a derivative of the output. A weight update circuit updates the charge storage circuits based upon output from the plurality of transfer function circuits and output from the plurality of derivative circuits. In preferred embodiments, separate training and validation networks share the same set of charge storage circuits and may operate concurrently. The validation network has a separate transfer function circuits each being coupled to the charge storage circuits so as to replicate the training network s coupling of the plurality of charge storage to the plurality of transfer function circuits. The plurality of transfer function circuits may be constructed each having a transconductance amplifier providing differential currents combined to provide an output in accordance with a transfer function. The derivative circuits may have a circuit constructed to generate a biased differential currents combined so as to provide the derivative of the transfer function.

  11. Supervised feature ranking using a genetic algorithm optimized artificial neural network.

    PubMed

    Lin, Thy-Hou; Chiu, Shih-Hau; Tsai, Keng-Chang

    2006-01-01

    A genetic algorithm optimized artificial neural network GNW has been designed to rank features for two diversified multivariate data sets. The dimensions of these data sets are 85x24 and 62x25 for 24 or 25 molecular descriptors being computed for 85 matrix metalloproteinase-1 inhibitors or 62 hepatitis C virus NS3 protease inhibitors, respectively. Each molecular descriptor computed is treated as a feature and input into an input layer node of the artificial neural network. To optimize the artificial neural network by the genetic algorithm, each interconnected weight between input and hidden or between hidden and output layer nodes is binary encoded as a 16 bits string in a chromosome, and the chromosome is evolved by crossover and mutation operations. Each input layer node and its associated weights of the trained GNW are systematically omitted once (the self-depleted weights), and the corresponding weight adjustments due to the omission are computed to keep the overall network behavior unchanged. The primary feature ranking index defined as the sum of self-depleted weights and the corresponding weight adjustments computed is found capable of separating good from bad features for some artificial data sets of known feature rankings tested. The final feature indexes used to rank the data sets are computed as a sum of the weighted frequency of each feature being ranked in a particular rank for each data set being partitioned into numerous clusters. The two data sets are also clustered by a standard K-means method and trained by a support vector machine (SVM) for feature ranking using the computed F-scores as feature ranking index. It is found that GNW outperforms the SVM method on three artificial as well as the matrix metalloproteinase-1 inhibitor data sets studied. A clear-cut separation of good from bad features is offered by the GNW but not by the SVM method for a feature pool of known feature ranking. PMID:16859292

  12. Annual precipitation forecast for west, southwest, and south provinces of Iran using artificial neural networks

    NASA Astrophysics Data System (ADS)

    Azadi, Samira; Sepaskhah, Ali Reza

    2012-07-01

    Rainfed agriculture plays an important role in the agricultural production of the southern and western provinces of Iran. In rainfed agriculture, the adequacy of annual precipitation is considered as an important factor for dryland field and supplemental irrigation management. Different methods can be used for predicting the annual precipitation based on climatic and non-climatic inputs. Among which artificial neural networks (ANN) is one of these methods. The purpose of this research was to predict the annual precipitation amount (millimeters) in the west, southwest, and south of Islamic Republic of Iran with the total area of 394,259 km2, by applying non-climatic inputs according to the long-time average precipitation in each station (millimeters), 47.5 mm precipitation since the first of autumn (day), t 47.5, and other effective parameters like coordinate and altitude of the stations, by using the artificial neural networks. In order to intelligently estimate the annual amount of precipitation in the study regions (ten provinces), feedforward backpropagation artificial neural network model has been used (method I). To predict the annual precipitation amount more accurately, the region under study was divided into three sub-regions, according to the precipitation mapping, and for each sub-region, the neural networks were developed using t 47.5 and long-time average annual precipitation in each station (method II). It is concluded that neural networks did not significantly increase the prediction accuracy in the study area compared with multiple regression model proposed by other investigators. However, in case of ANN, it is better to use a structure of 2-6-6-10-1 and Levenberg-Marquardt learning algorithm and sigmoid logistic activation function for prediction of annual precipitation.

  13. An investigation on the estimation of sediment concentration by artificial neural networks

    NASA Astrophysics Data System (ADS)

    Chen, A.; Chiang, Y.; Chang, S.; Chang, F.

    2010-12-01

    Estimation of sediment concentration in reservoirs, one of the important issues for watershed planning in Taiwan, is closely related to the water resources management in upstream river. It is difficult to continuously and effectively measure the sediment in the watershed. Furthermore, traditional methods such as regression analysis and sediment rating curve are not able to provide effective simulation of sediment concentration. As a result, there is a necessity of establishing an accurate model for estimating sediment concentration. The daily streamflow and sediment concentration data measured at Hsia-Yun gauging stations and precipitation data measured at 16 rainfall gauging stations in Shihmen Reservoir were collected in the years of 1982-2006. In this study, a total of 484 datasets were the inputs to an artificial neural network (ANN), in which 316, 77 and 91 datasets were arranged for model training, validation and testing accordingly. The artificial neural network was applied to estimating sediment concentration at Hsia-Yun gauging station. Results obtained from ANN were compared with those of conventional methods. The investigation revealed that the ANN provided a coefficient of efficiency of 0.77 in testing phase and performed much better than sediment rating curve. Keywords: watershed management, river sediment concentration, artificial neural network(ANN).

  14. Prediction of Yarn Strength Utilization in Cotton Woven Fabrics using Artificial Neural Network

    NASA Astrophysics Data System (ADS)

    Mishra, Swapna

    2015-10-01

    The paper presents an endeavor to predict the percentage yarn strength utilization (% SU) in cotton woven fabrics using artificial neural network approach. Fabrics in plain, 2/2 twill, 3/1 twill and 4-end broken twill weaves having three pick densities and three weft counts in each weave have been considered. Different artificial neural network models, with different set of input parameters, have been explored. It has been found that % SU can be predicted fairly accurately by only five fabric parameters, namely the number of load bearing and transverse yarns per unit length, the yarn crimp % in the load bearing and transverse directions and the float length of the weave. Trend analysis of the artificial neural network model has also been carried out to see how the various parameters affect the % SU. The results indicate that while an increase in the number of load bearing or transverse yarns increases the % SU, an increase in the float length and the crimp % in the yarns have a detrimental effect.

  15. Stream classification using hierarchical artificial neural networks: A fluvial hazard management tool

    NASA Astrophysics Data System (ADS)

    Besaw, Lance E.; Rizzo, Donna M.; Kline, Michael; Underwood, Kristen L.; Doris, Jeffrey J.; Morrissey, Leslie A.; Pelletier, Keith

    2009-06-01

    SummaryWatershed managers and planners have long sought decision-making tools for forecasting changes in stream-channels over large spatial and temporal scales. In this research, we apply non-parametric, clustering and classification artificial neural networks to assimilate large amounts of disparate data types for use in fluvial hazard management decision-making. Two types of artificial neural networks (a counterpropagation algorithm and a Kohonen self-organizing map) are used in hierarchy to predict reach-scale stream geomorphic condition, inherent vulnerability and sensitivity to adjustments using expert knowledge in combination with a variety of geomorphic assessment field data. Seven hundred and eighty-nine Vermont stream reaches (+7500 km) have been assessed by the Vermont Agency of Natural Resources' geomorphic assessment protocols, and are used in the development of this work. More than 85% of the reach-scale stream geomorphic condition and inherent vulnerability predictions match expert evaluations. The method's usefulness as a QA/QC tool is discussed. The Kohonen self-organizing map clusters the 789 reaches into groupings of stream sensitivity (or instability). By adjusting the weight of input variables, experts can fine-tune the classification system to better understand and document similarities/differences among expert opinions. The use of artificial neural networks allows for an adaptive watershed management approach, does not require the development of site-specific, physics-based, stream models (i.e., is data-driven), and provides a standardized approach for classifying river network sensitivity in various contexts.

  16. Neural net classification of liver ultrasonogram for quantitative evaluation of diffuse liver disease

    NASA Astrophysics Data System (ADS)

    Lee, Dong Hyuk; Kim, JongHyo; Kim, Hee C.; Lee, Yong W.; Min, Byong Goo

    1997-04-01

    There have been a number of studies on the quantitative evaluation of diffuse liver disease by using texture analysis technique. However, the previous studies have been focused on the classification between only normal and abnormal pattern based on textural properties, resulting in lack of clinically useful information about the progressive status of liver disease. Considering our collaborative research experience with clinical experts, we judged that not only texture information but also several shape properties are necessary in order to successfully classify between various states of disease with liver ultrasonogram. Nine image parameters were selected experimentally. One of these was texture parameter and others were shape parameters measured as length, area and curvature. We have developed a neural-net algorithm that classifies liver ultrasonogram into 9 categories of liver disease: 3 main category and 3 sub-steps for each. Nine parameters were collected semi- automatically from the user by using graphical user interface tool, and then processed to give a grade for each parameter. Classifying algorithm consists of two steps. At the first step, each parameter was graded into pre-defined levels using neural network. in the next step, neural network classifier determined disease status using graded nine parameters. We implemented a PC based computer-assist diagnosis workstation and installed it in radiology department of Seoul National University Hospital. Using this workstation we collected 662 cases during 6 months. Some of these were used for training and others were used for evaluating accuracy of the developed algorithm. As a conclusion, a liver ultrasonogram classifying algorithm was developed using both texture and shape parameters and neural network classifier. Preliminary results indicate that the proposed algorithm is useful for evaluation of diffuse liver disease.

  17. Linear and nonlinear ARMA model parameter estimation using an artificial neural network

    NASA Technical Reports Server (NTRS)

    Chon, K. H.; Cohen, R. J.

    1997-01-01

    This paper addresses parametric system identification of linear and nonlinear dynamic systems by analysis of the input and output signals. Specifically, we investigate the relationship between estimation of the system using a feedforward neural network model and estimation of the system by use of linear and nonlinear autoregressive moving-average (ARMA) models. By utilizing a neural network model incorporating a polynomial activation function, we show the equivalence of the artificial neural network to the linear and nonlinear ARMA models. We compare the parameterization of the estimated system using the neural network and ARMA approaches by utilizing data generated by means of computer simulations. Specifically, we show that the parameters of a simulated ARMA system can be obtained from the neural network analysis of the simulated data or by conventional least squares ARMA analysis. The feasibility of applying neural networks with polynomial activation functions to the analysis of experimental data is explored by application to measurements of heart rate (HR) and instantaneous lung volume (ILV) fluctuations.

  18. Flood Identification from Satellite Images Using Artificial Neural Networks

    NASA Astrophysics Data System (ADS)

    Chang, L.; Kao, I.; Shih, K.

    2011-12-01

    Typhoons and storms hit Taiwan several times every year and they cause serious flood disasters. Because the rivers are short and steep, and their flows are relatively fast with floods lasting only few hours and usually less than one day. Flood identification can provide the flood disaster and extent information to disaster assistance and recovery centers. Due to the factors of the weather, it is not suitable for aircraft or traditional multispectral satellite; hence, the most appropriate way for investigating flooding extent is to use Synthetic Aperture Radar (SAR) satellite. In this study, back-propagation neural network (BPNN) model and multivariate linear regression (MLR) model are built to identify the flooding extent from SAR satellite images. The input variables of the BPNN model are Radar Cross Section (RCS) value and mean of the pixel, standard deviation, minimum and maximum of RCS values among its adjacent 3×3 pixels. The MLR model uses two images of the non-flooding and flooding periods, and The inputs are the difference between the RCS values of two images and the variances among its adjacent 3×3 pixels. The results show that the BPNN model can perform much better than the MLR model. The correct percentages are more than 80% and 73% in training and testing data, respectively. Many misidentified areas are very fragmented and unrelated. In order to reinforce the correct percentage, morphological image analysis is used to modify the outputs of these identification models. Through morphological operations, most of the small, fragmented and misidentified areas can be correctly assigned to flooding or non-flooding areas. The final results show that the flood identification of satellite images has been improved a lot and the correct percentages increases up to more than 90%.

  19. A New Artificial Neural Network Approach in Solving Inverse Kinematics of Robotic Arm (Denso VP6242)

    PubMed Central

    Dülger, L. Canan; Kapucu, Sadettin

    2016-01-01

    This paper presents a novel inverse kinematics solution for robotic arm based on artificial neural network (ANN) architecture. The motion of robotic arm is controlled by the kinematics of ANN. A new artificial neural network approach for inverse kinematics is proposed. The novelty of the proposed ANN is the inclusion of the feedback of current joint angles configuration of robotic arm as well as the desired position and orientation in the input pattern of neural network, while the traditional ANN has only the desired position and orientation of the end effector in the input pattern of neural network. In this paper, a six DOF Denso robotic arm with a gripper is controlled by ANN. The comprehensive experimental results proved the applicability and the efficiency of the proposed approach in robotic motion control. The inclusion of current configuration of joint angles in ANN significantly increased the accuracy of ANN estimation of the joint angles output. The new controller design has advantages over the existing techniques for minimizing the position error in unconventional tasks and increasing the accuracy of ANN in estimation of robot's joint angles. PMID:27610129

  20. A New Artificial Neural Network Approach in Solving Inverse Kinematics of Robotic Arm (Denso VP6242).

    PubMed

    Almusawi, Ahmed R J; Dülger, L Canan; Kapucu, Sadettin

    2016-01-01

    This paper presents a novel inverse kinematics solution for robotic arm based on artificial neural network (ANN) architecture. The motion of robotic arm is controlled by the kinematics of ANN. A new artificial neural network approach for inverse kinematics is proposed. The novelty of the proposed ANN is the inclusion of the feedback of current joint angles configuration of robotic arm as well as the desired position and orientation in the input pattern of neural network, while the traditional ANN has only the desired position and orientation of the end effector in the input pattern of neural network. In this paper, a six DOF Denso robotic arm with a gripper is controlled by ANN. The comprehensive experimental results proved the applicability and the efficiency of the proposed approach in robotic motion control. The inclusion of current configuration of joint angles in ANN significantly increased the accuracy of ANN estimation of the joint angles output. The new controller design has advantages over the existing techniques for minimizing the position error in unconventional tasks and increasing the accuracy of ANN in estimation of robot's joint angles. PMID:27610129

  1. Fracture-frequency prediction from borehole wireline logs using artificial neural networks

    SciTech Connect

    FitzGerald, E.M.; Bean, C.J.; Reilly, R.

    1999-11-01

    Borehole-wall imaging is currently the most reliable means of mapping discontinuities within boreholes. As these imaging techniques are expensive and thus not always included in a logging run, a method of predicting fracture frequency directly from traditional logging tool responses would be very useful and cost effective. Artificial neural networks (ANNs) show great potential in this area. ANNs are computational systems that attempt to mimic natural biological neural networks. They have the ability to recognize patterns and develop their own generalizations about a given data set. Neural networks are trained on data sets for which the solution is known and tested on data not previously seen in order to validate the network result. The authors show that artificial neural networks, due to their pattern recognition capabilities, are able to assess the signal strength of fracture-related heterogeneity in a borehole log and thus fracture frequency within a borehole. A combination of wireline logs (neutron porosity, bulk density, P-sonic, S-sonic, deep resistivity and shallow resistivity) were used as input parameters to the ANN. Fracture frequency calculated from borehole televiewer data was used as the single output parameter. The ANN was trained using a back-propagation algorithm with a momentum learning function. In addition to fracture frequency within a single borehole, an ANN trained on a subset of boreholes in an area could be used for prediction over the entire set of boreholes, thus allowing the lateral correlation of fracture zones.

  2. Development of Artificial Neural Network Model for Diesel Fuel Properties Prediction using Vibrational Spectroscopy.

    PubMed

    Bolanča, Tomislav; Marinović, Slavica; Ukić, Sime; Jukić, Ante; Rukavina, Vinko

    2012-06-01

    This paper describes development of artificial neural network models which can be used to correlate and predict diesel fuel properties from several FTIR-ATR absorbances and Raman intensities as input variables. Multilayer feed forward and radial basis function neural networks have been used to rapid and simultaneous prediction of cetane number, cetane index, density, viscosity, distillation temperatures at 10% (T10), 50% (T50) and 90% (T90) recovery, contents of total aromatics and polycyclic aromatic hydrocarbons of commercial diesel fuels. In this study two-phase training procedures for multilayer feed forward networks were applied. While first phase training algorithm was constantly the back propagation one, two second phase training algorithms were varied and compared, namely: conjugate gradient and quasi Newton. In case of radial basis function network, radial layer was trained using K-means radial assignment algorithm and three different radial spread algorithms: explicit, isotropic and K-nearest neighbour. The number of hidden layer neurons and experimental data points used for the training set have been optimized for both neural networks in order to insure good predictive ability by reducing unnecessary experimental work. This work shows that developed artificial neural network models can determine main properties of diesel fuels simultaneously based on a single and fast IR or Raman measurement. PMID:24061237

  3. Prediction of jominy hardness profiles of steels using artificial neural networks

    NASA Astrophysics Data System (ADS)

    Vermeulen, W. G.; van der Wolk, P. J.; de Weijer, A. P.; van der Zwaag, S.

    1996-02-01

    Jominy hardness profiles of steels were predicted from chemical composition and austenitizing temperature using an artificial neural network. The neural network was trained using some 4000 examples, covering a wide range of steel compositions. The performance of the neural network is examined as a function of the network architecture, the number of alloying elements, and the number of data sets used for training. A well-trained network predicts the Jominy hardness profile with an average error of about 2 HRC. Special attention was devoted to the effect of boron on hardenability. A network trained using data only from boron steels produced results similar to those of a network trained using all data available. The accuracy of the predictions of the model is compared with that of an analytical model for hardenability and with that of a partial least- squares model using the same set of data.

  4. Cyclone track forecasting based on satellite images using artificial neural networks

    NASA Astrophysics Data System (ADS)

    Kovordányi, Rita; Roy, Chandan

    Many places around the world are exposed to tropical cyclones and associated storm surges. In spite of massive efforts, a great number of people die each year as a result of cyclone events. To mitigate this damage, improved forecasting techniques must be developed. The technique presented here uses artificial neural networks to interpret NOAA-AVHRR satellite images. A multi-layer neural network, resembling the human visual system, was trained to forecast the movement of cyclones based on satellite images. The trained network produced correct directional forecast for 98% of test images, thus showing a good generalization capability. The results indicate that multi-layer neural networks could be further developed into an effective tool for cyclone track forecasting using various types of remote sensing data. Future work includes extension of the present network to handle a wide range of cyclones and to take into account supplementary information, such as wind speeds, water temperature, humidity, and air pressure.

  5. Demonstration of the feasibility of a complete ellipsometric characterization method based on an artificial neural network.

    PubMed

    Battie, Yann; Robert, Stéphane; Gereige, Issam; Jamon, Damien; Stchakovsky, Michel

    2009-10-01

    Ellipsometry is an optical technique that is widely used for determining optical and geometrical properties of optical thin films. These properties are in general extracted from the ellipsometric measurement by solving an inverse problem. Classical methods like the Levenberg-Marquardt algorithm are generally too long, depending on direct calculation and are very sensitive to local minima. In this way, the neural network has proved to be an efficient tool for solving these kinds of problems in a very short time. Indeed, it is rapid and less sensitive to local minima than the classical inversion method. We suggest a complete neural ellipsometric characterization method for determining the index dispersion law and the thickness of a simple SiO(2) or photoresist thin layer on Si, SiO(2), and BK7 substrates. The influence of the training couples on the artificial neural network performance is also discussed. PMID:19798371

  6. Efficient Digital Implementation of The Sigmoidal Function For Artificial Neural Network

    NASA Astrophysics Data System (ADS)

    Pratap, Rana; Subadra, M.

    2011-10-01

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

  7. Neural-Net-Based Predictive Modeling of Spout Eye Size in Steelmaking

    NASA Astrophysics Data System (ADS)

    Palaneeswaran, Ekambaram; Brooks, Geoffrey; Xu, Xiaodong Bernard

    2012-06-01

    Gas stirring is commonly used in pyrometallurgical vessels to enhance mass and heat transfer and to promote impurity removal. In the case of secondary steelmaking, the spout eye area is caused by the escape of the gas from the top of the smelt where the liquid steel is directly exposed to the air, and oxygen can be picked up through the spout eye area that can reduce the quality of steel. Thus, controlling the size of the spout eye area is very important to improving the quality of the steel and to keeping the consistency of the product. The set of prevailing models to predict spout eye size are based on specific practically difficult variables, e.g., height of slag in hot upper layer of vessels and gas flow rate at nozzle exit. Recently, the cold model results showed that the stirring process can be conveniently monitored by the signals such as (1) the image signal from the top of the vessel, (2) the sound of the stirring process, and (3) the vibration on the wall of the vessel. This article outlines the key details of a novel research investigation using neural-network-based predictive modeling such as general regression neural networks (GRNN) with genetic adaptive calibrations. Predictive capacities and generalization potentials of five model constructs ( i.e., with different sets of input parameters) were explored, and the neural net modeling yielded encouraging outcomes, e.g., (1) excellent goodness-of-fit generalization measures including high values of correlation and R 2 validation parameters ( e.g., r = 0.921 and R 2 = 0.845 in a model validation), and (2) low values of root mean square of errors ( e.g., 3.034). Overall, the research outlined in this article demonstrates that the spout eye size can be effectively predicted by predictive neural net modeling with convenient and practically measurable variables such as sound and vibration observations on the steelmaking vessels. These results have only been demonstrated for a cold model of the process, and

  8. Real-time classification of signals from three-component seismic sensors using neural nets

    SciTech Connect

    Bowman, B.C.; Dowla, F.

    1992-05-05

    Adaptive seismic data acquisition systems with capabilities of signal discrimination and event classification are important in treaty monitoring, proliferation, and earthquake early detection systems. Potential applications include monitoring underground chemical explosions, as well as other military, cultural, and natural activities where characteristics of signals change rapidly and without warning. In these applications, the ability to detect and interpret events rapidly without falling behind the influx of the data is critical. We developed a system for real-time data acquisition, analysis, learning, and classification of recorded events employing some of the latest technology in computer hardware, software, and artificial neural networks methods. The system is able to train dynamically, and updates its knowledge based on new data. The software is modular and hardware-independent; i.e., the front-end instrumentation is transparent to the analysis system. The software is designed to take advantage of the multiprocessing environment of the Unix operating system. The Unix System V shared memory and static RAM protocols for data access and the semaphore mechanism for interprocess communications were used. As the three-component sensor detects a seismic signal, it is displayed graphically on a color monitor using X11/Xlib graphics with interactive screening capabilities. For interesting events, the triaxial signal polarization is computed, a fast Fourier Transform (FFT) algorithm is applied, and the normalized power spectrum is transmitted to a backpropagation neural network for event classification. The system is currently capable of handling three data channels with a sampling rate of 500 Hz, which covers the bandwidth of most seismic events. The system has been tested in laboratory setting with artificial events generated in the vicinity of a three-component sensor.

  9. Estimating satellite pose and motion parameters using a novelty filter and neural net tracker

    NASA Technical Reports Server (NTRS)

    Lee, Andrew J.; Casasent, David; Vermeulen, Pieter; Barnard, Etienne

    1989-01-01

    A system for determining the position, orientation and motion of a satellite with respect to a robotic spacecraft using video data is advanced. This system utilizes two levels of pose and motion estimation: an initial system which provides coarse estimates of pose and motion, and a second system which uses the coarse estimates and further processing to provide finer pose and motion estimates. The present paper emphasizes the initial coarse pose and motion estimation sybsystem. This subsystem utilizes novelty detection and filtering for locating novel parts and a neural net tracker to track these parts over time. Results of using this system on a sequence of images of a spin stabilized satellite are presented.

  10. Investigation of neural-net based control strategies for improved power system dynamic performance

    SciTech Connect

    Sobajic, D.J.

    1995-12-31

    The ability to accurately predict the behavior of a dynamic system is of essential importance in monitoring and control of complex processes. In this regard recent advances in neural-net base system identification represent a significant step toward development and design of a new generation of control tools for increased system performance and reliability. The enabling functionality is the one of accurate representation of a model of a nonlinear and nonstationary dynamic system. This functionality provides valuable new opportunities including: (1) The ability to predict future system behavior on the basis of actual system observations, (2) On-line evaluation and display of system performance and design of early warning systems, and (3) Controller optimization for improved system performance. In this presentation, we discuss the issues involved in definition and design of learning control systems and their impact on power system control. Several numerical examples are provided for illustrative purpose.

  11. A CFBPN Artificial Neural Network Model for Educational Qualitative Data Analyses: Example of Students' Attitudes Based on Kellerts' Typologies

    ERIC Educational Resources Information Center

    Yorek, Nurettin; Ugulu, Ilker

    2015-01-01

    In this study, artificial neural networks are suggested as a model that can be "trained" to yield qualitative results out of a huge amount of categorical data. It can be said that this is a new approach applied in educational qualitative data analysis. In this direction, a cascade-forward back-propagation neural network (CFBPN) model was…

  12. Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research.

    PubMed

    Agatonovic-Kustrin, S; Beresford, R

    2000-06-01

    Artificial neural networks (ANNs) are biologically inspired computer programs designed to simulate the way in which the human brain processes information. ANNs gather their knowledge by detecting the patterns and relationships in data and learn (or are trained) through experience, not from programming. An ANN is formed from hundreds of single units, artificial neurons or processing elements (PE), connected with coefficients (weights), which constitute the neural structure and are organised in layers. The power of neural computations comes from connecting neurons in a network. Each PE has weighted inputs, transfer function and one output. The behavior of a neural network is determined by the transfer functions of its neurons, by the learning rule, and by the architecture itself. The weights are the adjustable parameters and, in that sense, a neural network is a parameterized system. The weighed sum of the inputs constitutes the activation of the neuron. The activation signal is passed through transfer function to produce a single output of the neuron. Transfer function introduces non-linearity to the network. During training, the inter-unit connections are optimized until the error in predictions is minimized and the network reaches the specified level of accuracy. Once the network is trained and tested it can be given new input information to predict the output. Many types of neural networks have been designed already and new ones are invented every week but all can be described by the transfer functions of their neurons, by the learning rule, and by the connection formula. ANN represents a promising modeling technique, especially for data sets having non-linear relationships which are frequently encountered in pharmaceutical processes. In terms of model specification, artificial neural networks require no knowledge of the data source but, since they often contain many weights that must be estimated, they require large training sets. In addition, ANNs can combine

  13. A microsensor array for quantification of lubricant contaminants using a back propagation artificial neural network

    NASA Astrophysics Data System (ADS)

    Zhu, Xiaoliang; Du, Li; Liu, Bendong; Zhe, Jiang

    2016-06-01

    We present a method based on an electrochemical sensor array and a back propagation artificial neural network for detection and quantification of four properties of lubrication oil, namely water (0, 500 ppm, 1000 ppm), total acid number (TAN) (13.1, 13.7, 14.4, 15.6 mg KOH g‑1), soot (0, 1%, 2%, 3%) and sulfur content (1.3%, 1.37%, 1.44%, 1.51%). The sensor array, consisting of four micromachined electrochemical sensors, detects the four properties with overlapping sensitivities. A total set of 36 oil samples containing mixtures of water, soot, and sulfuric acid with different concentrations were prepared for testing. The sensor array’s responses were then divided to three sets: training sets (80% data), validation sets (10%) and testing sets (10%). Several back propagation artificial neural network architectures were trained with the training and validation sets; one architecture with four input neurons, 50 and 5 neurons in the first and second hidden layer, and four neurons in the output layer was selected. The selected neural network was then tested using the four sets of testing data (10%). Test results demonstrated that the developed artificial neural network is able to quantitatively determine the four lubrication properties (water, TAN, soot, and sulfur content) with a maximum prediction error of 18.8%, 6.0%, 6.7%, and 5.4%, respectively, indicting a good match between the target and predicted values. With the developed network, the sensor array could be potentially used for online lubricant oil condition monitoring.

  14. Neural net-based controller for flutter suppression using ASTROS with smart structures

    NASA Astrophysics Data System (ADS)

    Nam, Changho; Chen, P. C.; Liu, Danny D.; Chattopadhyay, Aditi; Kim, Jongsun

    2000-06-01

    Recent development of a smart structures module and its successful integration with a multidisciplinary design optimization software ASTROS* and an Aeroservoelasticity (ASE) module is presented. A modeled F-16 wing using piezoelectric (PZT) actuators was used as an example to demonstrate the integrated software capability to design a flutter suppression system. For an active control design, neural network based robust controller will be used for this study. A smart structures module is developed by modifying the existing thermal loads module in ASTROS* in order to include the effects of the induced strain due to piezoelectric (PZT) actuation. The thermal-PZT equivalence model enables the modifications of the thermal stress module to accommodate the smart structures module in ASTROS*. ZONA developed the control surface (CS)/PZT equivalence model principle, which ensures the interchangeability between the CS force input and the PZT force input to the ASE modules in ASTROS*. The results show that the neural net based controller can increase the flutter speed.

  15. Artificial neural network approach for atomic coordinate prediction of carbon nanotubes

    NASA Astrophysics Data System (ADS)

    Acı, Mehmet; Avcı, Mutlu

    2016-07-01

    In this paper, four artificial neural network (ANN) models [i.e., feed-forward neural network (FFNN), function fitting neural network (FITNET), cascade-forward neural network (CFNN) and generalized regression neural network] have been developed for atomic coordinate prediction of carbon nanotubes (CNTs). The research reported in this study has two primary objectives: (1) to develop ANN prediction models that calculate atomic coordinates of CNTs instead of using any simulation software and (2) to use results of the ANN models as an initial value of atomic coordinates for reducing number of iterations in calculation process. The dataset consisting of 10,721 data samples was created by combining the atomic coordinates of elements and chiral vectors using BIOVIA Materials Studio CASTEP (CASTEP) software. All prediction models yield very low mean squared normalized error and mean absolute error rates. Multiple correlation coefficient (R) results of FITNET, FFNN and CFNN models are close to 1. Compared with CASTEP, calculation times decrease from days to minutes. It would seem possible to predict CNTs' atomic coordinates using ANN models can be successfully used instead of mathematical calculations.

  16. Unfolding the neutron spectrum of a NE213 scintillator using artificial neural networks.

    PubMed

    Sharghi Ido, A; Bonyadi, M R; Etaati, G R; Shahriari, M

    2009-10-01

    Artificial neural networks technology has been applied to unfold the neutron spectra from the pulse height distribution measured with NE213 liquid scintillator. Here, both the single and multi-layer perceptron neural network models have been implemented to unfold the neutron spectrum from an Am-Be neutron source. The activation function and the connectivity of the neurons have been investigated and the results have been analyzed in terms of the network's performance. The simulation results show that the neural network that utilizes the Satlins transfer function has the best performance. In addition, omitting the bias connection of the neurons improve the performance of the network. Also, the SCINFUL code is used for generating the response functions in the training phase of the process. Finally, the results of the neural network simulation have been compared with those of the FORIST unfolding code for both (241)Am-Be and (252)Cf neutron sources. The results of neural network are in good agreement with FORIST code. PMID:19586776

  17. The use of artificial neural networks to predict the muscle behavior

    NASA Astrophysics Data System (ADS)

    Kutilek, Patrik; Viteckova, Slavka; Svoboda, Zdenĕk; Smrcka, Pavel

    2013-09-01

    The aim of this article is to introduce methods of prediction of muscle behavior of the lower extremities based on artificial neural networks, which can be used for medical purposes. Our work focuses on predicting muscletendon forces and moments during human gait with the use of angle-time diagram. A group of healthy children and children with cerebral palsy were measured using a Vicon MoCap system. The kinematic data was recorded and the OpenSim software system was used to identify the joint angles, muscle-tendon forces and joint muscle moment, which are presented graphically with time diagrams. The musculus gastrocnemius medialis that is often studied in the context of cerebral palsy have been chosen to study the method of prediction. The diagrams of mean muscle-tendon force and mean moment are plotted and the data about the force-time and moment-time dependencies are used for training neural networks. The new way of prediction of muscle-tendon forces and moments based on neural networks was tested. Neural networks predicted the muscle forces and moments of healthy children and children with cerebral palsy. The designed method of prediction by neural networks could help to identify the difference between muscle behavior of healthy subjects and diseased subjects.

  18. Parameter estimation of brain tumors using intraoperative thermal imaging based on artificial tactile sensing in conjunction with artificial neural network

    NASA Astrophysics Data System (ADS)

    Sadeghi-Goughari, M.; Mojra, A.; Sadeghi, S.

    2016-02-01

    Intraoperative Thermal Imaging (ITI) is a new minimally invasive diagnosis technique that can potentially locate margins of brain tumor in order to achieve maximum tumor resection with least morbidity. This study introduces a new approach to ITI based on artificial tactile sensing (ATS) technology in conjunction with artificial neural networks (ANN) and feasibility and applicability of this method in diagnosis and localization of brain tumors is investigated. In order to analyze validity and reliability of the proposed method, two simulations were performed. (i) An in vitro experimental setup was designed and fabricated using a resistance heater embedded in agar tissue phantom in order to simulate heat generation by a tumor in the brain tissue; and (ii) A case report patient with parafalcine meningioma was presented to simulate ITI in the neurosurgical procedure. In the case report, both brain and tumor geometries were constructed from MRI data and tumor temperature and depth of location were estimated. For experimental tests, a novel assisted surgery robot was developed to palpate the tissue phantom surface to measure temperature variations and ANN was trained to estimate the simulated tumor’s power and depth. Results affirm that ITI based ATS is a non-invasive method which can be useful to detect, localize and characterize brain tumors.

  19. Cistanches identification based on fluorescent spectral imaging technology combined with principal component analysis and artificial neural network

    NASA Astrophysics Data System (ADS)

    Dong, Jia; Huang, Furong; Li, Yuanpeng; Xiao, Chi; Xian, Ruiyi; Ma, Zhiguo

    2015-03-01

    In this study, fluorescent spectral imaging technology combined with principal component analysis (PCA) and artificial neural networks (ANNs) was used to identify Cistanche deserticola, Cistanche tubulosa and Cistanche sinensis, which are traditional Chinese medicinal herbs. The fluorescence spectroscopy imaging system acquired the spectral images of 40 cistanche samples, and through image denoising, binarization processing to make sure the effective pixels. Furthermore, drew the spectral curves whose data in the wavelength range of 450-680 nm for the study. Then preprocessed the data by first-order derivative, analyzed the data through principal component analysis and artificial neural network. The results shows: Principal component analysis can generally distinguish cistanches, through further identification by neural networks makes the results more accurate, the correct rate of the testing and training sets is as high as 100%. Based on the fluorescence spectral imaging technique and combined with principal component analysis and artificial neural network to identify cistanches is feasible.

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

    PubMed

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

    2016-02-01

    Artificial neural networks are sophisticated modelling and prediction tools capable of extracting complex, non-linear relationships between predictor (input) and predicted (output) variables. This study explores this capacity by modelling non-linearities in the hardiness-modulated burnout process with a neural network. Specifically, two multi-layer feed-forward artificial neural networks are concatenated in an attempt to model the composite non-linear burnout process. Sensitivity analysis, a Monte Carlo-based global simulation technique, is then utilised to examine the first-order effects of the predictor variables on the burnout sub-dimensions and consequences. Results show that (1) this concatenated artificial neural network approach is feasible to model the burnout process, (2) sensitivity analysis is a prolific method to study the relative importance of predictor variables and (3) the relationships among variables involved in the development of burnout and its consequences are to different degrees non-linear. PMID:26230967

  1. Third Conference on Artificial Intelligence for Space Applications, part 1

    NASA Technical Reports Server (NTRS)

    Denton, Judith S. (Compiler); Freeman, Michael S. (Compiler); Vereen, Mary (Compiler)

    1987-01-01

    The application of artificial intelligence to spacecraft and aerospace systems is discussed. Expert systems, robotics, space station automation, fault diagnostics, parallel processing, knowledge representation, scheduling, man-machine interfaces and neural nets are among the topics discussed.

  2. Third Conference on Artificial Intelligence for Space Applications, part 2

    NASA Technical Reports Server (NTRS)

    Denton, Judith S. (Compiler); Freeman, Michael S. (Compiler); Vereen, Mary (Compiler)

    1988-01-01

    Topics relative to the application of artificial intelligence to space operations are discussed. New technologies for space station automation, design data capture, computer vision, neural nets, automatic programming, and real time applications are discussed.

  3. Sea water level forecasting using genetic programming and comparing the performance with Artificial Neural Networks

    NASA Astrophysics Data System (ADS)

    Ali Ghorbani, Mohammad; Khatibi, Rahman; Aytek, Ali; Makarynskyy, Oleg; Shiri, Jalal

    2010-05-01

    Water level forecasting at various time intervals using records of past time series is of importance in water resources engineering and management. In the last 20 years, emerging approaches over the conventional harmonic analysis techniques are based on using Genetic Programming (GP) and Artificial Neural Networks (ANNs). In the present study, the GP is used to forecast sea level variations, three time steps ahead, for a set of time intervals comprising 12 h, 24 h, 5 day and 10 day time intervals using observed sea levels. The measurements from a single tide gauge at Hillarys Boat Harbor, Western Australia, were used to train and validate the employed GP for the period from December 1991 to December 2002. Statistical parameters, namely, the root mean square error, correlation coefficient and scatter index, are used to measure their performances. These were compared with a corresponding set of published results using an Artificial Neural Network model. The results show that both these artificial intelligence methodologies perform satisfactorily and may be considered as alternatives to the harmonic analysis.

  4. Comparison of classical statistical methods and artificial neural network in traffic noise prediction

    SciTech Connect

    Nedic, Vladimir; Despotovic, Danijela; Cvetanovic, Slobodan; Despotovic, Milan; Babic, Sasa

    2014-11-15

    Traffic is the main source of noise in urban environments and significantly affects human mental and physical health and labor productivity. Therefore it is very important to model the noise produced by various vehicles. Techniques for traffic noise prediction are mainly based on regression analysis, which generally is not good enough to describe the trends of noise. In this paper the application of artificial neural networks (ANNs) for the prediction of traffic noise is presented. As input variables of the neural network, the proposed structure of the traffic flow and the average speed of the traffic flow are chosen. The output variable of the network is the equivalent noise level in the given time period L{sub eq}. Based on these parameters, the network is modeled, trained and tested through a comparative analysis of the calculated values and measured levels of traffic noise using the originally developed user friendly software package. It is shown that the artificial neural networks can be a useful tool for the prediction of noise with sufficient accuracy. In addition, the measured values were also used to calculate equivalent noise level by means of classical methods, and comparative analysis is given. The results clearly show that ANN approach is superior in traffic noise level prediction to any other statistical method. - Highlights: • We proposed an ANN model for prediction of traffic noise. • We developed originally designed user friendly software package. • The results are compared with classical statistical methods. • The results are much better predictive capabilities of ANN model.

  5. Comparative nonlinear modeling of renal autoregulation in rats: Volterra approach versus artificial neural networks.

    PubMed

    Chon, K H; Holstein-Rathlou, N H; Marsh, D J; Marmarelis, V Z

    1998-01-01

    Volterra models have been increasingly popular in modeling studies of nonlinear physiological systems. In this paper, feedforward artificial neural networks with two types of activation functions (sigmoidal and polynomial) are utilized for modeling the nonlinear dynamic relation between renal blood pressure and flow data, and their performance is compared to Volterra models obtained by use of the leading kernel estimation method based on Laguerre expansions. The results for the two types of artificial neural networks (sigmoidal and polynomial) and the Volterra models are comparable in terms of normalized mean-square error (NMSE) of the respective output prediction for independent testing data. However, the Volterra models obtained via the Laguerre expansion technique achieve this prediction NMSE with approximately half the number of free parameters relative to either neural-network model. Nonetheless, both approaches are deemed effective in modeling nonlinear dynamic systems and their cooperative use is recommended in general, since they may exhibit different strengths and weaknesses depending on the specific characteristics of each application. PMID:18252466

  6. Supervised artificial neural network-based method for conversion of solar radiation data (case study: Algeria)

    NASA Astrophysics Data System (ADS)

    Laidi, Maamar; Hanini, Salah; Rezrazi, Ahmed; Yaiche, Mohamed Redha; El Hadj, Abdallah Abdallah; Chellali, Farouk

    2016-01-01

    In this study, a backpropagation artificial neural network (BP-ANN) model is used as an alternative approach to predict solar radiation on tilted surfaces (SRT) using a number of variables involved in physical process. These variables are namely the latitude of the site, mean temperature and relative humidity, Linke turbidity factor and Angstrom coefficient, extraterrestrial solar radiation, solar radiation data measured on horizontal surfaces (SRH), and solar zenith angle. Experimental solar radiation data from 13 stations spread all over Algeria around the year (2004) were used for training/validation and testing the artificial neural networks (ANNs), and one station was used to make the interpolation of the designed ANN. The ANN model was trained, validated, and tested using 60, 20, and 20 % of all data, respectively. The configuration 8-35-1 (8 inputs, 35 hidden, and 1 output neurons) presented an excellent agreement between the prediction and the experimental data during the test stage with determination coefficient of 0.99 and root meat squared error of 5.75 Wh/m2, considering a three-layer feedforward backpropagation neural network with Levenberg-Marquardt training algorithm, a hyperbolic tangent sigmoid and linear transfer function at the hidden and the output layer, respectively. This novel model could be used by researchers or scientists to design high-efficiency solar devices that are usually tilted at an optimum angle to increase the solar incident on the surface.

  7. Fuzzy logic and neural networks in artificial intelligence and pattern recognition

    NASA Astrophysics Data System (ADS)

    Sanchez, Elie

    1991-10-01

    With the use of fuzzy logic techniques, neural computing can be integrated in symbolic reasoning to solve complex real world problems. In fact, artificial neural networks, expert systems, and fuzzy logic systems, in the context of approximate reasoning, share common features and techniques. A model of Fuzzy Connectionist Expert System is introduced, in which an artificial neural network is designed to construct the knowledge base of an expert system from, training examples (this model can also be used for specifications of rules in fuzzy logic control). Two types of weights are associated with the synaptic connections in an AND-OR structure: primary linguistic weights, interpreted as labels of fuzzy sets, and secondary numerical weights. Cell activation is computed through min-max fuzzy equations of the weights. Learning consists in finding the (numerical) weights and the network topology. This feedforward network is described and first illustrated in a biomedical application (medical diagnosis assistance from inflammatory-syndromes/proteins profiles). Then, it is shown how this methodology can be utilized for handwritten pattern recognition (characters play the role of diagnoses): in a fuzzy neuron describing a number for example, the linguistic weights represent fuzzy sets on cross-detecting lines and the numerical weights reflect the importance (or weakness) of connections between cross-detecting lines and characters.

  8. A comparative study for the estimation of geodetic point velocity by artificial neural networks

    NASA Astrophysics Data System (ADS)

    Yilmaz, M.; Gullu, M.

    2014-06-01

    Space geodesy era provides velocity information which results in the positioning of geodetic points by considering the time evolution. The geodetic point positions on the Earth's surface change over time due to plate tectonics, and these changes have to be accounted for geodetic purposes. The velocity field of geodetic network is determined from GPS sessions. Velocities of the new structured geodetic points within the geodetic network are estimated from this velocity field by the interpolation methods. In this study, the utility of Artificial Neural Networks (ANN) widely applied in diverse fields of science is investigated in order to estimate the geodetic point velocities. Back Propagation Artificial Neural Network (BPANN) and Radial Basis Function Neural Network (RBFNN) are used to estimate the geodetic point velocities. In order to evaluate the performance of ANNs, the velocities are also interpolated by Kriging (KRIG) method. The results are compared in terms of the root mean square error (RMSE) over five different geodetic networks. It was concluded that the estimation of geodetic point velocity by BPANN is more effective and accurate than by KRIG when the points to be estimated are more than the points known.

  9. Inverse simulation system for manual-controlled rendezvous and docking based on artificial neural network

    NASA Astrophysics Data System (ADS)

    Zhou, Wanmeng; Wang, Hua; Tang, Guojin; Guo, Shuai

    2016-09-01

    The time-consuming experimental method for handling qualities assessment cannot meet the increasing fast design requirements for the manned space flight. As a tool for the aircraft handling qualities research, the model-predictive-control structured inverse simulation (MPC-IS) has potential applications in the aerospace field to guide the astronauts' operations and evaluate the handling qualities more effectively. Therefore, this paper establishes MPC-IS for the manual-controlled rendezvous and docking (RVD) and proposes a novel artificial neural network inverse simulation system (ANN-IS) to further decrease the computational cost. The novel system was obtained by replacing the inverse model of MPC-IS with the artificial neural network. The optimal neural network was trained by the genetic Levenberg-Marquardt algorithm, and finally determined by the Levenberg-Marquardt algorithm. In order to validate MPC-IS and ANN-IS, the manual-controlled RVD experiments on the simulator were carried out. The comparisons between simulation results and experimental data demonstrated the validity of two systems and the high computational efficiency of ANN-IS.

  10. Bias correction of temperature produced by the Community Climate System Model using Artificial Neural Networks

    NASA Astrophysics Data System (ADS)

    Moghim, S.; Hsu, K.; Bras, R. L.

    2013-12-01

    General Circulation Models (GCMs) are used to predict circulation and energy transfers between the atmosphere and the land. It is known that these models produce biased results that will have impact on their uses. This work proposes a new method for bias correction: the equidistant cumulative distribution function-artificial neural network (EDCDFANN) procedure. The method uses artificial neural networks (ANNs) as a surrogate model to estimate bias-corrected temperature, given an identification of the system derived from GCM models output variables. A two-layer feed forward neural network is trained with observations during a historical period and then the adjusted network can be used to predict bias-corrected temperature for future periods. To capture the extreme values this method is combined with the equidistant CDF matching method (EDCDF, Li et al. 2010). The proposed method is tested with the Community Climate System Model (CCSM3) outputs using air and skin temperature, specific humidity, shortwave and longwave radiation as inputs to the ANN. This method decreases the mean square error and increases the spatial correlation between the modeled temperature and the observed one. The results indicate the EDCDFANN has potential to remove the biases of the model outputs.

  11. A compact optical instrument with artificial neural network for pH determination.

    PubMed

    Capel-Cuevas, Sonia; López-Ruiz, Nuria; Martinez-Olmos, Antonio; Cuéllar, Manuel P; Pegalajar, Maria del Carmen; Palma, Alberto José; de Orbe-Payá, Ignacio; Capitán-Vallvey, Luis Fermin

    2012-01-01

    The aim of this work was the determination of pH with a sensor array-based optical portable instrument. This sensor array consists of eleven membranes with selective colour changes at different pH intervals. The method for the pH calculation is based on the implementation of artificial neural networks that use the responses of the membranes to generate a final pH value. A multi-objective algorithm was used to select the minimum number of sensing elements required to achieve an accurate pH determination from the neural network, and also to minimise the network size. This helps to minimise instrument and array development costs and save on microprocessor energy consumption. A set of artificial neural networks that fulfils these requirements is proposed using different combinations of the membranes in the sensor array, and is evaluated in terms of accuracy and reliability. In the end, the network including the response of the eleven membranes in the sensor was selected for validation in the instrument prototype because of its high accuracy. The performance of the instrument was evaluated by measuring the pH of a large set of real samples, showing that high precision can be obtained in the full range. PMID:22778668

  12. Detection of pinhole defects in optical film using thermography and artificial neural network

    NASA Astrophysics Data System (ADS)

    Wang, Hongjin; Hsieh, Sheng-Jen; Singh, Bhavana

    2015-05-01

    Optical film provides anti-glare, anti-reflective, and protective features for cell phones and electronic displays such as LCD screens. Due to increased use of optical film, it is challenging for manufacturers to increase their efficiency in producing better quality films. Even a micro scratch in a high-end application of film can lead to a total failure of the display. Optical and visual methods are typically employed to detect defects, but these methods have limitations such as viewing angle artifacts and design of proper illumination source. This paper describes research to utilize artificial neural networks as a non-destructive defect detection model for predicting the presence of pinhole defects in film. An infrared camera captured the thermal response of optical film subjected to heating and cooling. Pinhole defects of various sizes (0.03mm, 0.08mm, 0.2mm, 0.4mm, 0.7mm, 1mm, 2mm, 3mm, 4mm) were investigated. Pinhole defects are one of the most common types of optical film defects. For the process of identification, thermal differences of defective and defect-free regions were investigated. An Artificial Neural Network was trained to use average absolute temperature difference and cooling rate to predict the presence of a defect. The ANN model was trained and verified using separate data sets. The ANN model was able to classify defective and non-defective samples with a 77.8% accuracy rate. The regression coefficient was 0.5874. These results suggest that artificial neural networks can be used for detecting pinhole defects.

  13. Monitoring rice nitrogen status using hyperspectral reflectance and artificial neural network.

    PubMed

    Yi, Qiu-Xiang; Huang, Jing-Feng; Wang, Fu-Min; Wang, Xiu-Zhen; Liu, Zhan-Yu

    2007-10-01

    Over use of nitrogen fertilization can result in groundwater pollution. Tools that can rapidly quantify the nitrogen status are needed for efficient fertilizer management and would be very helpful in reducing the environmental pollution caused by excessive nitrogen application. Remote sensing has a proven ability to provide spatial and temporal measurements of surface properties. In this study, the MLR (multiple linear regression) and ANN (artificial neural network) modeling methods were applied to the monitoring of rice N (nitrogen concentration, mg nitrogen g(-1) leaf dry weight) status using leaf level hyperspectral reflectance with two different input variables, and as a result four estimation models were proposed. RMSE (root-mean-square error), REP (relative error of prediction), R2 (coefficient of determination), as well as the intercept and slope between the observed and predicted N were used to test the performance of models. Very good agreements between the observed and the predicted N were obtained with all proposed models, which was especially true for the R-ANN (artificial neural network based on reflectance selected using MLR) model. Compared to the other three models, the R-ANN model improved the results by lowering the RMSE by 14.2%, 32.1%, and 31.5% for the R-LR (linear regression based on reflectance) model, PC-LR (linear regression based on principal components scores) model, and PC-ANN (artificial neural network based on principal components scores) model, respectively. It was concluded that the ANN algorithm may provide a useful exploratory and predictive tool when applied on hyperspectral reflectance data for nitrogen status monitoring. Besides, although the performance of MLR was superior to PCA used for ANN inputs selection, the encouraging results of PC-based models indicated the promising potential of ANN combined with PCA application on hyperspectral reflectance analysis. PMID:17969693

  14. Artificial Neural Network Modeling to Evaluate the Dynamic Flow Stress of 7050 Aluminum Alloy

    NASA Astrophysics Data System (ADS)

    Quan, Guo-zheng; Wang, Tong; Li, Yong-le; Zhan, Zong-yang; Xia, Yu-feng

    2016-02-01

    The flow stress data have been obtained by a set of isothermal hot compression tests, which were carried out in the temperature range of 573-723 K and strain rates of 0.01, 0.1, 1, and 10 s-1 with a reduction of 60% on a Gleeble-1500 thermo-mechanical simulator. On the basis of the experimental data, constitutive equation and an artificial neural network model were developed for the analysis and simulation of the flow behavior of the 7050 aluminum alloy. After training with standard back-propagation learning algorithm, the artificial neural network model has the ability to present the intrinsic relationship between the flow stress and the processing variables. In the present model, the temperature, strain, and strain rate were chosen as inputs, and the flow stress was chosen as output. By comparing the values of correlation coefficient and average absolute relative error, the prediction accuracy of the model and the improved Arrhenius-type model can be evaluated. The results indicated that the well-trained artificial neural network model is more accurate than the improved Arrhenius-type model in predicting the hot compressive behavior of the as-extruded 7050 aluminum alloy. Based on the predicted stress data and experimental stress data, the 3D continuous stress-strain maps at different strains, temperatures, and strain rates were plotted subsequently. Besides, the flow stress values at arbitrary temperature, strain rate, and strain are explicit on the 3D continuous stress-strain maps, which would be beneficial to articulate working processes more validly.

  15. Generalized in vitro-in vivo relationship (IVIVR) model based on artificial neural networks

    PubMed Central

    Mendyk, Aleksander; Tuszyński, Paweł K; Polak, Sebastian; Jachowicz, Renata

    2013-01-01

    Background The aim of this study was to develop a generalized in vitro-in vivo relationship (IVIVR) model based on in vitro dissolution profiles together with quantitative and qualitative composition of dosage formulations as covariates. Such a model would be of substantial aid in the early stages of development of a pharmaceutical formulation, when no in vivo results are yet available and it is impossible to create a classical in vitro-in vivo correlation (IVIVC)/IVIVR. Methods Chemoinformatics software was used to compute the molecular descriptors of drug substances (ie, active pharmaceutical ingredients) and excipients. The data were collected from the literature. Artificial neural networks were used as the modeling tool. The training process was carried out using the 10-fold cross-validation technique. Results The database contained 93 formulations with 307 inputs initially, and was later limited to 28 in a course of sensitivity analysis. The four best models were introduced into the artificial neural network ensemble. Complete in vivo profiles were predicted accurately for 37.6% of the formulations. Conclusion It has been shown that artificial neural networks can be an effective predictive tool for constructing IVIVR in an integrated generalized model for various formulations. Because IVIVC/IVIVR is classically conducted for 2–4 formulations and with a single active pharmaceutical ingredient, the approach described here is unique in that it incorporates various active pharmaceutical ingredients and dosage forms into a single model. Thus, preliminary IVIVC/IVIVR can be available without in vivo data, which is impossible using current IVIVC/IVIVR procedures. PMID:23569360

  16. AN ARTIFICIAL NEURAL NETWORK EVALUATION OF TUBERCULOSIS USING GENETIC AND PHYSIOLOGICAL PATIENT DATA

    SciTech Connect

    Griffin, William O.; Darsey, Jerry A.; Hanna, Josh; Razorilova, Svetlana; Kitaev, Mikhael; Alisherov, Avtandiil; Tarasenko, Olga

    2010-04-12

    When doctors see more cases of patients with tell-tale symptoms of a disease, it is hoped that they will be able to recognize an infection administer treatment appropriately, thereby speeding up recovery for sick patients. We hope that our studies can aid in the detection of tuberculosis by using a computer model called an artificial neural network. Our model looks at patients with and without tuberculosis (TB). The data that the neural network examined came from the following: patient' age, gender, place, of birth, blood type, Rhesus (Rh) factor, and genes of the human Leukocyte Antigens (HLA) system (9q34.1) present in the Major Histocompatibility Complex. With availability in genetic data and good research, we hope to give them an advantage in the detection of tuberculosis. We try to mimic the doctor's experience with a computer test, which will learn from patient data the factors that contribute to TB.

  17. On-Line Tracking Controller for Brushless DC Motor Drives Using Artificial Neural Networks

    NASA Technical Reports Server (NTRS)

    Rubaai, Ahmed

    1996-01-01

    A real-time control architecture is developed for time-varying nonlinear brushless dc motors operating in a high performance drives environment. The developed control architecture possesses the capabilities of simultaneous on-line identification and control. The dynamics of the motor are modeled on-line and controlled using an artificial neural network, as the system runs. The control architecture combines the experience and dependability of adaptive tracking systems with potential and promise of the neural computing technology. The sensitivity of real-time controller to parametric changes that occur during training is investigated. Such changes are usually manifested by rapid changes in the load of the brushless motor drives. This sudden change in the external load is simulated for the sigmoidal and sinusoidal reference tracks. The ability of the neuro-controller to maintain reasonable tracking accuracy in the presence of external noise is also verified for a number of desired reference trajectories.

  18. Modeling of relative intensity noise and terminal electrical noise of semiconductor lasers using artificial neural network

    NASA Astrophysics Data System (ADS)

    Rezaei, A.; Noori, L.

    2016-06-01

    In this paper, artificial neural network (ANN) is used to predict the source laser's relative intensity noise (RIN) and the terminal electrical noise (TEN) of semiconductor lasers. For this purpose, the multi-layer perceptron (MLP) neural network trained with the back propagation algorithm is used. To develop this model, the normalized bias current and frequency are selected as the input parameters and the RIN and TEN of semiconductor lasers are selected as the output parameters. The obtained results show that the proposed ANN model is in a good agreement with the numerical method, and a small error between the predicted values and the numerical solution is obtained. Therefore, the proposed ANN model is a useful, reliable, fast and cheap tool to predict the RIN and TEN of semiconductor lasers.

  19. Noise reduction and image enhancement using a hardware implementation of artificial neural networks

    NASA Astrophysics Data System (ADS)

    David, Robert; Williams, Erin; de Tremiolles, Ghislain; Tannhof, Pascal

    1999-03-01

    In this paper, we present a neural based solution developed for noise reduction and image enhancement using the ZISC, an IBM hardware processor which implements the Restricted Coulomb Energy algorithm and the K-Nearest Neighbor algorithm. Artificial neural networks present the advantages of processing time reduction in comparison with classical models, adaptability, and the weighted property of pattern learning. The goal of the developed application is image enhancement in order to restore old movies (noise reduction, focus correction, etc.), to improve digital television images, or to treat images which require adaptive processing (medical images, spatial images, special effects, etc.). Image results show a quantitative improvement over the noisy image as well as the efficiency of this system. Further enhancements are being examined to improve the output of the system.

  20. Prediction of wastewater treatment plants performance based on artificial fish school neural network

    NASA Astrophysics Data System (ADS)

    Zhang, Ruicheng; Li, Chong

    2011-10-01

    A reliable model for wastewater treatment plant is essential in providing a tool for predicting its performance and to form a basis for controlling the operation of the process. This would minimize the operation costs and assess the stability of environmental balance. For the multi-variable, uncertainty, non-linear characteristics of the wastewater treatment system, an artificial fish school neural network prediction model is established standing on actual operation data in the wastewater treatment system. The model overcomes several disadvantages of the conventional BP neural network. The results of model calculation show that the predicted value can better match measured value, played an effect on simulating and predicting and be able to optimize the operation status. The establishment of the predicting model provides a simple and practical way for the operation and management in wastewater treatment plant, and has good research and engineering practical value.

  1. Modeling of subdivision of scored tablets with the application of artificial neural networks.

    PubMed

    Sovány, T; Kása, P; Pintye-Hódi, K

    2010-02-01

    The subdivision of scored tablets is an important problem for the exact individual therapy of patients. The recent guidelines of the EU require verification of the equal mass of the tablet halves, but this problem has previously never been investigated in papers published on the production technological aspects. Our aim was therefore to study the effects of the physicochemical properties of the raw materials and the effects of the compression process on the breaking parameters of the tablets. Artificial neural networks (ANNs) were applied for data analysis and modeling, which are very useful optimizing systems. The abilities of four different types of ANNs to predict the parameters of the compression process and the tablets were compared. The radial basis function and multilayer perceptron ANNs furnished statistically significant better results than linear or generalized regression neural networks. These ANN models revealed that the subdivision of scored tablets is strongly influenced by the production parameters and the compositions of the powder mixtures. PMID:19569202

  2. Identifying three-phase induction motor faults using artificial neural networks

    PubMed

    Kolla; Varatharasa

    2000-01-01

    This paper presents an artificial neural network (ANN) based technique to identify faults in a three-phase induction motor. The main types of faults considered are overload, single phasing, unbalanced supply voltage, locked rotor, ground fault, over-voltage and under-voltage. Three-phase currents and voltages from the induction motor are used in the proposed approach. A feedforward layered neural network structure is used. The network is trained using the backpropagation algorithm. The trained network is tested with simulated fault current and voltage data. Fault detection is attempted in the no fault to fault transition period. Off-line testing results on a 3 HP induction motor model show that the proposed ANN based method is effective in identifying various types of faults. PMID:11106295

  3. Prediction of Force Measurements of a Microbend Sensor Based on an Artificial Neural Network

    PubMed Central

    Efendioglu, Hasan S.; Yildirim, Tulay; Fidanboylu, Kemal

    2009-01-01

    Artificial neural network (ANN) based prediction of the response of a microbend fiber optic sensor is presented. To the best of our knowledge no similar work has been previously reported in the literature. Parallel corrugated plates with three deformation cycles, 6 mm thickness of the spacer material and 16 mm mechanical periodicity between deformations were used in the microbend sensor. Multilayer Perceptron (MLP) with different training algorithms, Radial Basis Function (RBF) network and General Regression Neural Network (GRNN) are used as ANN models in this work. All of these models can predict the sensor responses with considerable errors. RBF has the best performance with the smallest mean square error (MSE) values of training and test results. Among the MLP algorithms and GRNN the Levenberg-Marquardt algorithm has good results. These models successfully predict the sensor responses, hence ANNs can be used as useful tool in the design of more robust fiber optic sensors. PMID:22399991

  4. A Pareto evolutionary artificial neural network approach for remote sensing image classification

    NASA Astrophysics Data System (ADS)

    Liu, Fujiang; Wu, Xincai; Guo, Yan; Sun, Huashan; Zhou, Feng; Mei, Linlu

    2006-10-01

    This paper presents a Pareto evolutionary artificial neural network (Pareto-EANN) approach based on the evolutionary algorithms for multiobjective optimization augmented with local search for the classification of remote sensing image. Its novelty lies in the use of a multiobjective genetic algorithm where single hidden layers Multilayer Perceptrons (MLP) are employed to indicate the accuracy/complexity trade-off. Some advantages of this approach include the ability to accommodate multiple criteria such as accuracy of the classifier and number of hidden units. We compared Pareto-EANN classifiers results of the classification of remote sensing image against standard backpropagation neural network classifiers and EANN classifiers; we show experimentally the efficiency of the proposed methodology.

  5. Nonlinear dynamic system identification using Chebyshev functional link artificial neural networks.

    PubMed

    Patra, J C; Kot, A C

    2002-01-01

    A computationally efficient artificial neural network (ANN) for the purpose of dynamic nonlinear system identification is proposed. The major drawback of feedforward neural networks, such as multilayer perceptrons (MLPs) trained with the backpropagation (BP) algorithm, is that they require a large amount of computation for learning. We propose a single-layer functional-link ANN (FLANN) in which the need for a hidden layer is eliminated by expanding the input pattern by Chebyshev polynomials. The novelty of this network is that it requires much less computation than that of a MLP. We have shown its effectiveness in the problem of nonlinear dynamic system identification. In the presence of additive Gaussian noise, the performance of the proposed network is found to be similar or superior to that of a MLP. A performance comparison in terms of computational complexity has also been carried out. PMID:18238146

  6. Classification of Weed Species Using Artificial Neural Networks Based on Color Leaf Texture Feature

    NASA Astrophysics Data System (ADS)

    Li, Zhichen; An, Qiu; Ji, Changying

    The potential impact of herbicide utilization compel people to use new method of weed control. Selective herbicide application is optimal method to reduce herbicide usage while maintain weed control. The key of selective herbicide is how to discriminate weed exactly. The HIS color co-occurrence method (CCM) texture analysis techniques was used to extract four texture parameters: Angular second moment (ASM), Entropy(E), Inertia quadrature (IQ), and Inverse difference moment or local homogeneity (IDM).The weed species selected for studying were Arthraxon hispidus, Digitaria sanguinalis, Petunia, Cyperus, Alternanthera Philoxeroides and Corchoropsis psilocarpa. The software of neuroshell2 was used for designing the structure of the neural network, training and test the data. It was found that the 8-40-1 artificial neural network provided the best classification performance and was capable of classification accuracies of 78%.

  7. Direct, inverse, and combined problems in complex engineered system modeling by artificial neural networks

    NASA Astrophysics Data System (ADS)

    Terekhoff, Serge A.

    1997-04-01

    This paper summarizes theoretical findings and applications of artificial neural networks to modeling of complex engineered system response in the abnormal environments. The thermal fire impact on the industrial container for waste and fissile materials was investigated using model and experimental data. Solutions for the direct problem show that the generalization properties of neural network based model are significantly better than those for standard interpolation methods. Minimal amount of data required for good prediction of system response is estimated in computer experiments with MLP network. It was shown that Kohonen's self-organizing map with counterpropagation may also estimate local accuracy of regularized solution for inverse and combined problems. Feature space regions of partial correctness of the inverse model can be automatically extracted using adaptive clustering. Practical findings include time strategy recommendations for fire-safe services when industrial or transport accidents occur.

  8. An Artificial Neural Network Evaluation of Tuberculosis Using Genetic and Physiological Patient Data

    NASA Astrophysics Data System (ADS)

    Griffin, William O.; Hanna, Josh; Razorilova, Svetlana; Kitaev, Mikhael; Alisherov, Avtandiil; Darsey, Jerry A.; Tarasenko, Olga

    2010-04-01

    When doctors see more cases of patients with tell-tale symptoms of a disease, it is hoped that they will be able to recognize an infection administer treatment appropriately, thereby speeding up recovery for sick patients. We hope that our studies can aid in the detection of tuberculosis by using a computer model called an artificial neural network. Our model looks at patients with and without tuberculosis (TB). The data that the neural network examined came from the following: patient' age, gender, place, of birth, blood type, Rhesus (Rh) factor, and genes of the human Leukocyte Antigens (HLA) system (9q34.1) present in the Major Histocompatibility Complex. With availability in genetic data and good research, we hope to give them an advantage in the detection of tuberculosis. We try to mimic the doctor's experience with a computer test, which will learn from patient data the factors that contribute to TB.

  9. Ship wake-detection procedure using conjugate gradient trained artificial neural networks

    SciTech Connect

    Fitch, J.P.; Lehman, S.K.; Dowla, F.U.; Lu, S.Y.; Johansson, E.M.; Goodman, D.M. )

    1991-09-01

    This paper reports that a method has been developed to reduce large two-dimensional images to significantly smaller feature lists. These feature lists overcome the problem of storing and manipulating large amounts of data. A new artificial neural network using conjugate gradient training methods, operating on sets of feature lists, was successfully trained to determine the presence or absence of wakes in synthetic aperture radar images. A comparison has been made between the different conjugate gradient and steepest-descent training methods and has demonstrated the superiority of the former over the latter.

  10. Quantitative analysis of tin alloy combined with artificial neural network prediction

    SciTech Connect

    Oh, Seong Y.; Yueh, Fang-Yu; Singh, Jagdish P.

    2010-05-01

    Laser-induced breakdown spectroscopy was applied to quantitative analysis of three impurities in Sn alloy. The impurities analysis was based on the internal standard method using the Sn I 333.062-nm line as the reference line to achieve the best reproducible results. Minor-element concentrations (Ag, Cu, Pb) in the alloy were comparatively evaluated by artificial neural networks (ANNs) and calibration curves. ANN was found to effectively predict elemental concentrations with a trend of nonlinear growth due to self-absorption. The limits of detection for Ag, Cu, and Pb in Sn alloy were determined to be 29, 197, and 213 ppm, respectively.

  11. Structural Damage Identification Based on Rough Sets and Artificial Neural Network

    PubMed Central

    Liu, Chengyin; Wu, Xiang; Wu, Ning; Liu, Chunyu

    2014-01-01

    This paper investigates potential applications of the rough sets (RS) theory and artificial neural network (ANN) method on structural damage detection. An information entropy based discretization algorithm in RS is applied for dimension reduction of the original damage database obtained from finite element analysis (FEA). The proposed approach is tested with a 14-bay steel truss model for structural damage detection. The experimental results show that the damage features can be extracted efficiently from the combined utilization of RS and ANN methods even the volume of measurement data is enormous and with uncertainties. PMID:25013847

  12. The identification of pitting and crevice corrosion spectra in electrochemical noise using an artificial neural network

    SciTech Connect

    Barton, T.F.; Tuck, D.L.; Wells, D.B.

    1996-12-31

    An artificial neural network has been developed to identify the onset and classify the type of localized corrosion from electrochemical noise spectra. The multilayer feedforward (MLF) network was trained by classical back-propagation to identify corrosion from the characteristics of the initial current ramp. Using 50 training files and 39 test files taken from measurements on Type 304 stainless steel in a dilute chloride electrolyte, the network accurately detected and classified 96% of the data and reported no misclassifications. Experiments with high levels of adventitious noise superimposed on the original data have been carried out to examine the noise tolerance of the network.

  13. Control the springback of metal sheets by using an artificial neural network

    NASA Astrophysics Data System (ADS)

    Crina, Axinte

    2007-04-01

    One of the greatest challenges of manufacturing sheet metal parts is to obtain consistent parts dimensions. Springback is the major cause of variations and inconsistencies in the final part geometry. Obtaining a consistent and desirable amount of springback is extremely difficult due to the non-linear effects and interactions of process and material parameters. In this work, the ability of an artificial neural network model to predict optimum process parameters and tools geometry which allow to obtain minimum amount of springback is tested, in the case of a cylindrical deep-drawing process.

  14. E(BV) Determinations of O and B Stars Using Artificial Neural Networks

    NASA Astrophysics Data System (ADS)

    Gulati, R.; Gupta, R.; Singh, H.

    1997-07-01

    We propose a new method to determine E(B-V) from low dispersion ultraviolet spectra by using artificial neural networks. This method has been applied to O and B stars in the IUE low dispersion catalog. A comparison of the ANN based determinations with the classical method of comparing observed (B-V) with intrinsic color (B-V)o is performed to assess the reliability and limitation of the new method. We have been able to determine E(B-V) to an accuracy of 0.08 magnitudes and also been able to single out a star which follows an anomalous interstellar extinction law. (SECTION: Computing and Data Analysis)

  15. Structural damage identification based on rough sets and artificial neural network.

    PubMed

    Liu, Chengyin; Wu, Xiang; Wu, Ning; Liu, Chunyu

    2014-01-01

    This paper investigates potential applications of the rough sets (RS) theory and artificial neural network (ANN) method on structural damage detection. An information entropy based discretization algorithm in RS is applied for dimension reduction of the original damage database obtained from finite element analysis (FEA). The proposed approach is tested with a 14-bay steel truss model for structural damage detection. The experimental results show that the damage features can be extracted efficiently from the combined utilization of RS and ANN methods even the volume of measurement data is enormous and with uncertainties. PMID:25013847

  16. Exponential stability preservation in discrete-time analogues of artificial neural networks with distributed delays

    NASA Astrophysics Data System (ADS)

    Mohamad, Sannay

    2008-05-01

    This paper demonstrates that there is a discrete-time analogue which does not require any restriction on the size of the time-step in order to preserve the exponential stability of an artificial neural network with distributed delays. The analysis exploits an appropriate Lyapunov sequence and a discrete-time system of Halanay inequalities, and also either a Young inequality or a geometric-arithmetic mean inequality, to derive several sufficient conditions on the network parameters for the exponential stability of the analogue. The sufficiency conditions are independent of the time-step, and they correspond to those that establish the exponential stability of the continuous-time network.

  17. A feedforward artificial neural network based on quantum effect vector-matrix multipliers.

    PubMed

    Levy, H J; McGill, T C

    1993-01-01

    The vector-matrix multiplier is the engine of many artificial neural network implementations because it can simulate the way in which neurons collect weighted input signals from a dendritic arbor. A new technology for building analog weighting elements that is theoretically capable of densities and speeds far beyond anything that conventional VLSI in silicon could ever offer is presented. To illustrate the feasibility of such a technology, a small three-layer feedforward prototype network with five binary neurons and six tri-state synapses was built and used to perform all of the fundamental logic functions: XOR, AND, OR, and NOT. PMID:18267745

  18. Toward IMRT 2D dose modeling using artificial neural networks: A feasibility study

    SciTech Connect

    Kalantzis, Georgios; Vasquez-Quino, Luis A.; Zalman, Travis; Pratx, Guillem; Lei, Yu

    2011-10-15

    Purpose: To investigate the feasibility of artificial neural networks (ANN) to reconstruct dose maps for intensity modulated radiation treatment (IMRT) fields compared with those of the treatment planning system (TPS). Methods: An artificial feed forward neural network and the back-propagation learning algorithm have been used to replicate dose calculations of IMRT fields obtained from PINNACLE{sup 3} v9.0. The ANN was trained with fluence and dose maps of IMRT fields for 6 MV x-rays, which were obtained from the amorphous silicon (a-Si) electronic portal imaging device of Novalis TX. Those fluence distributions were imported to the TPS and the dose maps were calculated on the horizontal midpoint plane of a water equivalent homogeneous cylindrical virtual phantom. Each exported 2D dose distribution from the TPS was classified into two clusters of high and low dose regions, respectively, based on the K-means algorithm and the Euclidian metric in the fluence-dose domain. The data of each cluster were divided into two sets for the training and validation phase of the ANN, respectively. After the completion of the ANN training phase, 2D dose maps were reconstructed by the ANN and isodose distributions were created. The dose maps reconstructed by ANN were evaluated and compared with the TPS, where the mean absolute deviation of the dose and the {gamma}-index were used. Results: A good agreement between the doses calculated from the TPS and the trained ANN was achieved. In particular, an average relative dosimetric difference of 4.6% and an average {gamma}-index passing rate of 93% were obtained for low dose regions, and a dosimetric difference of 2.3% and an average {gamma}-index passing rate of 97% for high dose region. Conclusions: An artificial neural network has been developed to convert fluence maps to corresponding dose maps. The feasibility and potential of an artificial neural network to replicate complex convolution kernels in the TPS for IMRT dose calculations

  19. Note: Artificial neural networks for the automated analysis of force map data in atomic force microscopy

    SciTech Connect

    Braunsmann, Christoph; Schäffer, Tilman E.

    2014-05-15

    Force curves recorded with the atomic force microscope on structured samples often show an irregular force versus indentation behavior. An analysis of such curves using standard contact models (e.g., the Sneddon model) would generate inaccurate Young's moduli. A critical inspection of the force curve shape is therefore necessary for estimating the reliability of the generated Young's modulus. We used a trained artificial neural network to automatically recognize curves of “good” and of “bad” quality. This is especially useful for improving the analysis of force maps that consist of a large number of force curves.

  20. Functional recognition imaging using artificial neural networks: applications to rapid cellular identification via broadband electromechanical response

    NASA Astrophysics Data System (ADS)

    Nikiforov, M. P.; Reukov, V. V.; Thompson, G. L.; Vertegel, A. A.; Guo, S.; Kalinin, S. V.; Jesse, S.

    2009-10-01

    Functional recognition imaging in scanning probe microscopy (SPM) using artificial neural network identification is demonstrated. This approach utilizes statistical analysis of complex SPM responses at a single spatial location to identify the target behavior, which is reminiscent of associative thinking in the human brain, obviating the need for analytical models. We demonstrate, as an example of recognition imaging, rapid identification of cellular organisms using the difference in electromechanical activity over a broad frequency range. Single-pixel identification of model Micrococcus lysodeikticus and Pseudomonas fluorescens bacteria is achieved, demonstrating the viability of the method.

  1. [Artificial neural network applied for spectral overlap interference correction in ICP-AES].

    PubMed

    Zhang, Z; Liu, S; Zeng, X

    1997-10-01

    A back-propagation artificial neural network (BP-ANN) has been applied to correcting spectral overlap interference in inductively coupled plasma atomic emission spectrometry (ICP-AES). Some network parameters including the range of input values and training sequence for training patterns presented to the network were discussed using simulated Ce 413.380nm and Pr 413.380nm line profiles. Results show that the noise in simulated mixture spectra will slow down the network convergence and has more influence on network prediction. PMID:15810366

  2. Application of artificial neural network for prediction of marine diesel engine performance

    NASA Astrophysics Data System (ADS)

    Mohd Noor, C. W.; Mamat, R.; Najafi, G.; Nik, W. B. Wan; Fadhil, M.

    2015-12-01

    This study deals with an artificial neural network (ANN) modelling of a marine diesel engine to predict the brake power, output torque, brake specific fuel consumption, brake thermal efficiency and volumetric efficiency. The input data for network training was gathered from engine laboratory testing running at various engine speed. The prediction model was developed based on standard back-propagation Levenberg-Marquardt training algorithm. The performance of the model was validated by comparing the prediction data sets with the measured experiment data. Results showed that the ANN model provided good agreement with the experimental data with high accuracy.

  3. Smart design of intratumoral thermosensitive β-lapachone hydrogels by Artificial Neural Networks.

    PubMed

    Díaz-Rodríguez, P; Landin, M

    2012-08-20

    This study presents Artificial Neural Networks (ANN) as a tool for designing injectable intratumoral formulations of the anticancer drug β-lapachone. This methodology permits insight into the interactions between variables and determines the design space of the formulation without the restrictions of an experimental design. An ANN model for two critical parameters of the formulations; the amount of solubilized drug and gel temperature was developed and validated. The model allowed an understanding of interactions between ingredients in the formulation and the fundamental phenomena as the formation of polypseudorotaxanes to be detected and quantified. PMID:22613207

  4. Modeling of Abrasion Resistance Performance of Persian Handmade Wool Carpets Using Artificial Neural Network

    NASA Astrophysics Data System (ADS)

    Gupta, Shravan Kumar; Goswami, Kamal Kanti

    2015-10-01

    This paper presents the application of Artificial Neural Network (ANN) modeling for the prediction of abrasion resistance of Persian handmade wool carpets. Four carpet constructional parameters, namely knot density, pile height, number of ply in pile yarn and pile yarn twist have been used as input parameters for ANN model. The prediction performance was judged in terms of statistical parameters like correlation coefficient ( R) and Mean Absolute Percentage Error ( MAPE). Though the training performance of ANN was very good, the generalization ability was not up to the mark. This implies that large number of training data should be used for the adequate training of ANN models.

  5. Utilization of artificial neural networks in the diagnosis of optic nerve diseases.

    PubMed

    Kara, Sadik; Güven, Ayşegül; Oner, Ayşe Oztürk

    2006-04-01

    This research is concentrated on the diagnosis of optic nerve disease through the analysis of pattern electroretinography (PERG) signals with the help of artificial neural network (ANN). Multilayer feed forward ANN trained with a Levenberg Marquart (LM) backpropagation algorithm was implemented. The designed classification structure has about 96.4% sensitivity, 90.4% specifity and positive prediction is calculated to be 94.2%. The end results are classified as healthy and diseased. Testing results were found to be compliant with the expected results that are derived from the physician's direct diagnosis. The end benefit would be to assist the physician to make the final decision without hesitation. PMID:16488775

  6. Mexican sign language recognition using normalized moments and artificial neural networks

    NASA Astrophysics Data System (ADS)

    Solís-V., J.-Francisco; Toxqui-Quitl, Carina; Martínez-Martínez, David; H.-G., Margarita

    2014-09-01

    This work presents a framework designed for the Mexican Sign Language (MSL) recognition. A data set was recorded with 24 static signs from the MSL using 5 different versions, this MSL dataset was captured using a digital camera in incoherent light conditions. Digital Image Processing was used to segment hand gestures, a uniform background was selected to avoid using gloved hands or some special markers. Feature extraction was performed by calculating normalized geometric moments of gray scaled signs, then an Artificial Neural Network performs the recognition using a 10-fold cross validation tested in weka, the best result achieved 95.83% of recognition rate.

  7. Forecasting Volatility in Indian Stock Market using Artificial Neural Network with Multiple Inputs and Outputs

    NASA Astrophysics Data System (ADS)

    DattaChaudhuri, Tamal; Ghosh, Indranil

    2015-06-01

    Volatility in stock markets has been extensively studied in the applied finance literature. In this paper, Artificial Neural Network models based on various back propagation algorithms have been constructed to predict volatility in the Indian stock market through volatility of NIFTY returns and volatility of gold returns. This model considers India VIX, CBOE VIX, volatility of crude oil returns (CRUDESDR), volatility of DJIA returns (DJIASDR), volatility of DAX returns (DAXSDR), volatility of Hang Seng returns (HANGSDR) and volatility of Nikkei returns (NIKKEISDR) as predictor variables. Three sets of experiments have been performed over three time periods to judge the effectiveness of the approach.

  8. Artificial neural network for the determination of Hubble Space Telescope aberration from stellar images

    NASA Technical Reports Server (NTRS)

    Barrett, Todd K.; Sandler, David G.

    1993-01-01

    An artificial-neural-network method, first developed for the measurement and control of atmospheric phase distortion, using stellar images, was used to estimate the optical aberration of the Hubble Space Telescope. A total of 26 estimates of distortion was obtained from 23 stellar images acquired at several secondary-mirror axial positions. The results were expressed as coefficients of eight orthogonal Zernike polynomials: focus through third-order spherical. For all modes other than spherical the measured aberration was small. The average spherical aberration of the estimates was -0.299 micron rms, which is in good agreement with predictions obtained when iterative phase-retrieval algorithms were used.

  9. Real-time water treatment process control with artificial neural networks

    SciTech Connect

    Zhang, Q.; Stanley, S.J.

    1999-02-01

    With more stringent requirements being placed on water treatment performance, operators need a reliable tool to optimize the process control in the treatment plant. In the present paper, one such tool is presented, which is a process control system built with the artificial neural network (ANN) modeling approach. The coagulation, flocculation, and sedimentation processes involve many complex physical and chemical phenomena and thus are difficult to model for process control with traditional methods. Proposed is the use of a neural network process control system for the coagulation, flocculation, and sedimentation processes. Presented is a review of influential control parameters and control requirements for these processes followed by the development of a feed forward neural network control scheme. A neural network process model was built based on nearly 2,000 sets of process control data. This model formed the major component of a software controller and was found to consistently predict the optimum alum and power activated carbon doses for different control actions. With minor modifications, the approach illustrated can be used for building control models for other water treatment processes.

  10. Prediction of Weld Penetration in FCAW of HSLA steel using Artificial Neural Networks

    NASA Astrophysics Data System (ADS)

    Asl, Y. Dadgar; Mostafa, N. B.; Panahizadeh R., V.; Seyedkashi, S. M. H.

    2011-01-01

    Flux-cored arc welding (FCAW) is a semiautomatic or automatic arc welding process that requires a continuously-fed consumable tubular electrode containing a flux. The main FCAW process parameters affecting the depth of penetration are welding current, arc voltage, nozzle-to-work distance, torch angle and welding speed. Shallow depth of penetration may contribute to failure of a welded structure since penetration determines the stress-carrying capacity of a welded joint. To avoid such occurrences; the welding process parameters influencing the weld penetration must be properly selected to obtain an acceptable weld penetration and hence a high quality joint. Artificial neural networks (ANN), also called neural networks (NN), are computational models used to express complex non-linear relationships between input and output data. In this paper, artificial neural network (ANN) method is used to predict the effects of welding current, arc voltage, nozzle-to-work distance, torch angle and welding speed on weld penetration depth in gas shielded FCAW of a grade of high strength low alloy steel. 32 experimental runs were carried out using the bead-on-plate welding technique. Weld penetrations were measured and on the basis of these 32 sets of experimental data, a feed-forward back-propagation neural network was created. 28 sets of the experiments were used as the training data and the remaining 4 sets were used for the testing phase of the network. The ANN has one hidden layer with eight neurons and is trained after 840 iterations. The comparison between the experimental results and ANN results showed that the trained network could predict the effects of the FCAW process parameters on weld penetration adequately.

  11. Prediction of Weld Penetration in FCAW of HSLA steel using Artificial Neural Networks

    SciTech Connect

    Asl, Y. Dadgar; Mostafa, N. B.; Panahizadeh, V. R.; Seyedkashi, S. M. H.

    2011-01-17

    Flux-cored arc welding (FCAW) is a semiautomatic or automatic arc welding process that requires a continuously-fed consumable tubular electrode containing a flux. The main FCAW process parameters affecting the depth of penetration are welding current, arc voltage, nozzle-to-work distance, torch angle and welding speed. Shallow depth of penetration may contribute to failure of a welded structure since penetration determines the stress-carrying capacity of a welded joint. To avoid such occurrences; the welding process parameters influencing the weld penetration must be properly selected to obtain an acceptable weld penetration and hence a high quality joint. Artificial neural networks (ANN), also called neural networks (NN), are computational models used to express complex non-linear relationships between input and output data. In this paper, artificial neural network (ANN) method is used to predict the effects of welding current, arc voltage, nozzle-to-work distance, torch angle and welding speed on weld penetration depth in gas shielded FCAW of a grade of high strength low alloy steel. 32 experimental runs were carried out using the bead-on-plate welding technique. Weld penetrations were measured and on the basis of these 32 sets of experimental data, a feed-forward back-propagation neural network was created. 28 sets of the experiments were used as the training data and the remaining 4 sets were used for the testing phase of the network. The ANN has one hidden layer with eight neurons and is trained after 840 iterations. The comparison between the experimental results and ANN results showed that the trained network could predict the effects of the FCAW process parameters on weld penetration adequately.

  12. Investigation on trophic state index by artificial neural networks (case study: Dez Dam of Iran)

    NASA Astrophysics Data System (ADS)

    Saghi, H.; Karimi, L.; Javid, A. H.

    2015-06-01

    Dam construction and surface runoff control is one of the most common approaches for water-needs supply of human societies. However, the increasing development of social activities and hence the subsequent increase in environmental pollutants leads to deterioration of water quality in dam reservoirs and eutrophication process could be intensified. So, the water quality of reservoirs is now one of the key factors in operation and water quality management of reservoirs. Hence, maintaining the quality of the stored water and identification and examination of changes along time has been a constant concern of humans that involves the water authorities. Traditionally, empirical trophic state indices of dam reservoirs often defined based on changes in concentration of effective factors (nutrients) and its consequences (increase in chlorophyll a), have been used as an efficient tool in the definition of dam reservoirs quality. In recent years, modeling techniques such as artificial neural networks have enhanced the prediction capability and the accuracy of these studies. In this study, artificial neural networks have been applied to analyze eutrophication process in the Dez Dam reservoir in Iran. In this paper, feed forward neural network with one input layer, one hidden layer and one output layer was applied using MATLAB neural network toolbox for trophic state index (TSI) analysis in the Dez Dam reservoir. The input data of this network are effective parameters in the eutrophication: nitrogen cycle parameters and phosphorous cycle parameters and parameters that will be changed by eutrophication: Chl a, SD, DO and the output data is TSI. Based on the results from estimation of modified Carlson trophic state index, Dez Dam reservoir is considered to be eutrophic in the early July to mid-November and would be mesotrophic with decrease in temperature. Therefore, a decrease in water quality of the dam reservoir during the warm seasons is expectable. The results indicated that

  13. The application of artificial neural networks to magnetotelluric time-series analysis

    NASA Astrophysics Data System (ADS)

    Manoj, C.; Nagarajan, Nandini

    2003-05-01

    Magnetotelluric (MT) signals are often contaminated with noise from natural or man-made processes that may not fit a normal distribution or are highly correlated. This may lead to serious errors in computed MT transfer functions and result in erroneous interpretation. A substantial improvement is possible when the time-series are presented as clean as possible for further processing. Cleaning of MT time-series is often done by manual editing. Editing of magnetotelluric time-series is subjective in nature and time consuming. Automation of such a process is difficult to achieve by statistical methods. Artificial neural networks (ANNs) are widely used to automate processes that require human intelligence. The objective here is to automate MT long-period time-series editing using ANN. A three-layer feed-forward artificial neural network (FANN) was adopted for the problem. As ANN-based techniques are computationally intensive, a novel approach was made, which involves editing of five simultaneously measured MT time-series that have been subdivided into stacks (a stack=5 × 256 data points). Neural network training was done at two levels. Signal and noise patterns of individual channels were taught first. Five channel parameters along with interchannel correlation and amplitude ratios formed the input for a final network, which predicts the quality of a stack. A large database (5000 traces for pattern training and 900 vectors for interchannel training) was prepared to train the network. There were two error parameters to minimize while training: training error and testing error. Training was stopped when both errors were below an acceptable level. The sensitivity of the neural network to the signal-to-noise ratio and the relative significance of its inputs were tested to ensure that the training was correct. MT time-series from four stations with varying degrees of noise contamination were used to demonstrate the application of the network. The application brought out

  14. Investigation on trophic state index by artificial neural networks (case study: Dez Dam of Iran)

    NASA Astrophysics Data System (ADS)

    Saghi, H.; Karimi, L.; Javid, A. H.

    2014-04-01

    Dam construction and surface runoff control is one of the most common approaches for water-needs supply of human societies. However, the increasing development of social activities and hence the subsequent increase in environmental pollutants leads to deterioration of water quality in dam reservoirs and eutrophication process could be intensified. So, the water quality of reservoirs is now one of the key factors in operation and water quality management of reservoirs. Hence, maintaining the quality of the stored water and identification and examination of changes along time has been a constant concern of humans that involves the water authorities. Traditionally, empirical trophic state indices of dam reservoirs often defined based on changes in concentration of effective factors (nutrients) and its consequences (increase in chlorophyll a), have been used as an efficient tool in the definition of dam reservoirs quality. In recent years, modeling techniques such as artificial neural networks have enhanced the prediction capability and the accuracy of these studies. In this study, artificial neural networks have been applied to analyze eutrophication process in the Dez Dam reservoir in Iran. In this paper, feed forward neural network with one input layer, one hidden layer and one output layer was applied using MATLAB neural network toolbox for trophic state index (TSI) analysis in the Dez Dam reservoir. The input data of this network are effective parameters in the eutrophication: nitrogen cycle parameters and phosphorous cycle parameters and parameters that will be changed by eutrophication: Chl a, SD, DO and the output data is TSI. Based on the results from estimation of modified Carlson trophic state index, Dez Dam reservoir is considered to be eutrophic in the early July to mid-November and would be mesotrophic with decrease in temperature. Therefore, a decrease in water quality of the dam reservoir during the warm seasons is expectable. The results indicated that

  15. Prediction of U-Mo dispersion nuclear fuels with Al-Si alloy using artificial neural network

    NASA Astrophysics Data System (ADS)

    Susmikanti, Mike; Sulistyo, Jos

    2014-09-01

    Dispersion nuclear fuels, consisting of U-Mo particles dispersed in an Al-Si matrix, are being developed as fuel for research reactors. The equilibrium relationship for a mixture component can be expressed in the phase diagram. It is important to analyze whether a mixture component is in equilibrium phase or another phase. The purpose of this research it is needed to built the model of the phase diagram, so the mixture component is in the stable or melting condition. Artificial neural network (ANN) is a modeling tool for processes involving multivariable non-linear relationships. The objective of the present work is to develop code based on artificial neural network models of system equilibrium relationship of U-Mo in Al-Si matrix. This model can be used for prediction of type of resulting mixture, and whether the point is on the equilibrium phase or in another phase region. The equilibrium model data for prediction and modeling generated from experimentally data. The artificial neural network with resilient backpropagation method was chosen to predict the dispersion of nuclear fuels U-Mo in Al-Si matrix. This developed code was built with some function in MATLAB. For simulations using ANN, the Levenberg-Marquardt method was also used for optimization. The artificial neural network is able to predict the equilibrium phase or in the phase region. The develop code based on artificial neural network models was built, for analyze equilibrium relationship of U-Mo in Al-Si matrix.

  16. Prediction of U-Mo dispersion nuclear fuels with Al-Si alloy using artificial neural network

    SciTech Connect

    Susmikanti, Mike; Sulistyo, Jos

    2014-09-30

    Dispersion nuclear fuels, consisting of U-Mo particles dispersed in an Al-Si matrix, are being developed as fuel for research reactors. The equilibrium relationship for a mixture component can be expressed in the phase diagram. It is important to analyze whether a mixture component is in equilibrium phase or another phase. The purpose of this research it is needed to built the model of the phase diagram, so the mixture component is in the stable or melting condition. Artificial neural network (ANN) is a modeling tool for processes involving multivariable non-linear relationships. The objective of the present work is to develop code based on artificial neural network models of system equilibrium relationship of U-Mo in Al-Si matrix. This model can be used for prediction of type of resulting mixture, and whether the point is on the equilibrium phase or in another phase region. The equilibrium model data for prediction and modeling generated from experimentally data. The artificial neural network with resilient backpropagation method was chosen to predict the dispersion of nuclear fuels U-Mo in Al-Si matrix. This developed code was built with some function in MATLAB. For simulations using ANN, the Levenberg-Marquardt method was also used for optimization. The artificial neural network is able to predict the equilibrium phase or in the phase region. The develop code based on artificial neural network models was built, for analyze equilibrium relationship of U-Mo in Al-Si matrix.

  17. Artificial neural networks allow the use of simultaneous measurements of Alzheimer Disease markers for early detection of the disease

    PubMed Central

    Di Luca, Monica; Grossi, Enzo; Borroni, Barbara; Zimmermann, Martina; Marcello, Elena; Colciaghi, Francesca; Gardoni, Fabrizio; Intraligi, Marco; Padovani, Alessandro; Buscema, Massimo

    2005-01-01

    Background Previous studies have shown that in platelets of mild Alzheimer Disease (AD) patients there are alterations of specific APP forms, paralleled by alteration in expression level of both ADAM 10 and BACE when compared to control subjects. Due to the poor linear relation among each key-element of beta-amyloid cascade and the target diagnosis, the use of systems able to afford non linear tasks, like artificial neural networks (ANNs), should allow a better discriminating capacity in comparison with classical statistics. Objective To evaluate the accuracy of ANNs in AD diagnosis. Methods 37 mild-AD patients and 25 control subjects were enrolled, and APP, ADM10 and BACE measures were performed. Fifteen different models of feed-forward and complex-recurrent ANNs (provided by Semeion Research Centre), based on different learning laws (back propagation, sine-net, bi-modal) were compared with the linear discriminant analysis (LDA). Results The best ANN model correctly identified mild AD patients in the 94% of cases and the control subjects in the 92%. The corresponding diagnostic performance obtained with LDA was 90% and 73%. Conclusion This preliminary study suggests that the processing of biochemical tests related to beta-amyloid cascade with ANNs allows a very good discrimination of AD in early stages, higher than that obtainable with classical statistics methods. PMID:16048651

  18. Artificial neural networks forecasting of PM2.5 pollution using air mass trajectory based geographic model and wavelet transformation

    NASA Astrophysics Data System (ADS)

    Feng, Xiao; Li, Qi; Zhu, Yajie; Hou, Junxiong; Jin, Lingyan; Wang, Jingjie

    2015-04-01

    In the paper a novel hybrid model combining air mass trajectory analysis and wavelet transformation to improve the artificial neural network (ANN) forecast accuracy of daily average concentrations of PM2.5 two days in advance is presented. The model was developed from 13 different air pollution monitoring stations in Beijing, Tianjin, and Hebei province (Jing-Jin-Ji area). The air mass trajectory was used to recognize distinct corridors for transport of "dirty" air and "clean" air to selected stations. With each corridor, a triangular station net was constructed based on air mass trajectories and the distances between neighboring sites. Wind speed and direction were also considered as parameters in calculating this trajectory based air pollution indicator value. Moreover, the original time series of PM2.5 concentration was decomposed by wavelet transformation into a few sub-series with lower variability. The prediction strategy applied to each of them and then summed up the individual prediction results. Daily meteorological forecast variables as well as the respective pollutant predictors were used as input to a multi-layer perceptron (MLP) type of back-propagation neural network. The experimental verification of the proposed model was conducted over a period of more than one year (between September 2013 and October 2014). It is found that the trajectory based geographic model and wavelet transformation can be effective tools to improve the PM2.5 forecasting accuracy. The root mean squared error (RMSE) of the hybrid model can be reduced, on the average, by up to 40 percent. Particularly, the high PM2.5 days are almost anticipated by using wavelet decomposition and the detection rate (DR) for a given alert threshold of hybrid model can reach 90% on average. This approach shows the potential to be applied in other countries' air quality forecasting systems.

  19. A Restricted Boltzman Neural Net to Infer Carbon Uptake from OCO-2 Satellite Data

    NASA Astrophysics Data System (ADS)

    Halem, M.; Dorband, J. E.; Radov, A.; Barr-Dallas, M.; Gentine, P.

    2015-12-01

    For several decades, scientists have been using satellite observations to infer climate budgets of terrestrial carbon uptake employing inverse methods in conjunction with ecosystem models and coupled global climate models. This is an extremely important Big Data calculation today since the net annual photosynthetic carbon uptake changes annually over land and removes on average ~20% of the emissions from human contributions to atmospheric loading of CO2 from fossil fuels. Unfortunately, such calculations have large uncertainties validated with in-situ networks of measuring stations across the globe. One difficulty in using satellite data for these budget calculations is that the models need to assimilate surface fluxes of CO2 as well as soil moisture, vegatation cover and the eddy covariance of latent and sensible heat to calculate the carbon fixed in the soil while satellite spectral observations only provide near surface concentrations of CO2. In July 2014, NASA successfully launched OCO-2 which provides 3km surface measurements of CO2 over land and oceans. We have collected nearly one year of Level 2 XCO2 data from the OCO-2 satellite for 3 sites of ~200 km2 at equatorial, temperate and high latitudes. Each selected site was part of the Fluxnet or ARM system with tower stations for measuring and collecting CO2 fluxes on an hourly basis, in addition to eddy transports of the other parameters. We are also planning to acquire the 4km NDVI products from MODIS and registering the data to the 3km XCO2 footprints for the three sites. We have implemented a restricted Boltzman machine on the quantum annealing D-Wave computer, a novel deep learning neural net, to be used for training with station data to infer CO2 fluxes from collocated XCO2, MODIS vegetative land cover and MERRA reanalysis surface exchange products. We will present performance assessments of the D-Wave Boltzman machine for generating XCO2 fluxes from the OCO-2 satellite observations for the 3 sites by

  20. An empirical test of the weighted effect approach to generalized prediction using recursive neural nets

    SciTech Connect

    Lang, R.

    1996-12-31

    The requirement of a strict and fixed distinction between dependent variables and independent variables, together with the presence of missing data, typically imposes considerable problems for most standard statistical prediction procedures. This paper describes a solution of these problems through the {open_quotes}weighted effect{close_quotes} approach in which recursive neural nets are used to learn how to compensate for any main and interaction effects attributable to missing data through the use of an {open_quotes}effect set{close_quotes} in addition to the data of actual cases. Extensive simulations of the approach based on an existing psychological data base showed high predictive validity, and a graceful degradation in performance with an increase in the number of unknown predictor variables. Moreover, the method proved amenable to the use of two-parameter logistic curves to arrive at a three way {open_quotes}low,{close_quotes} {open_quotes}high,{close_quotes} and {open_quotes}undecided{close_quotes} decision scheme with a-priori known error rates.