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检索条件"主题词=Backpropagation Algorithms"

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Inserting background knowledge in perceptrons through modification of the learning algorithm

Inserting background knowledge in perceptrons through modifi...

引用

International Conference on Neural Networks

作者： J. Bode Xun Liang Xiping Zhang Shouju Ren CIMS-ERC Department of Automation Tsinghua University Beijing China Institute of Computer Science and Technology Peking University Beijing China

Usually, knowledge to be learned by neural networks is represented implicitly in the training samples. The ability to insert knowledge apart from the implicit representations in training samples ("background knowledge") gives rise to the hope that the learning and operation behavior of neural networks can be improved. In this paper, we develop a method to accomplish the insertion of expert knowledge into the error function during training. We modify the backpropagation learning algorithm such that the network is trained not only to minimize output error but also to consider further information provided by the expert users who train a multilayer perceptron with one hidden layer. The results are tested with an artificial example from design cost estimation using very small training set sizes of 10 samples. They show significant improvement compared to approaches which do not consider background knowledge.

关键词： Neural networks backpropagation algorithms Multilayer perceptrons Network topology Knowledge based systems Management training Product design Automation Computer science Testing

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A new method for fuzzy models identification

A new method for fuzzy models identification

引用

International Conference on Industrial Electronics, Control and Instrumentation

作者： A. Cipriano M. Ramos F. Montoya Faculty of Engineering Catholic University of Chile Santiago Chile

In this paper a new method for fuzzy models identification is presented. The algorithm is based on an iterative procedure and determines the optimal membership functions through the minimization of the root mean square error. Using illustrative examples the new method is evaluated and compared with the identification algorithm of Sugeno and Yasukawa, and the Horikawa algorithm.

关键词： Fuzzy sets Iterative algorithms Input variables Parameter estimation Clustering algorithms Minimization methods Automatic control Least squares methods Neural networks backpropagation algorithms

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Neural approximations of optimal allocation policies for hybrid multiplexing

Neural approximations of optimal allocation policies for hyb...

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IEEE Conference and Exhibition on Global Telecommunications (GLOBECOM)

作者： R. Bolla F. Davoli P. Maryni T. Parisini Department of Communications Computer and System Sciences DIST-University of Genoa Genoa Italy

A multiplexing structure is considered, where TDM frames are used to carry both isochronous, circuit-switched, and asynchronous, packet-switched traffic. Control functions are sought, whose task is that of deciding the allocation of the frame capacity between the two traffic types. The problem is defined in the context of Markov decision processes, and multilayer feedforward neural networks are used to approximate the optimal control laws. A backpropagation algorithm is described, which exploits the finiteness of the system's state. The procedure is conceived in the framework of repetitive (receding horizon) control schemes. Numerical results are presented, as well as comparisons with the control laws obtained by applying a dynamic programming algorithm.

关键词： Communication system traffic control Multi-layer neural network backpropagation algorithms Time division multiplexing Circuits Neural networks Feedforward neural networks Optimal control Dynamic programming Heuristic algorithms

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Identification and control of induction motor stator currents using fast on-line random training of a neural network

Identification and control of induction motor stator current...

引用

Conference Record of the IEEE Industry Applications Society Annual Meeting (IAS)

作者： B. Burton F. Kamran R.G. Harley T.G. Habetler M. Brooke R. Poddar Department of Electrical Engineering University of Natal Durban South Africa School of Electrical and Computer Engr Georgia Institute of Technology Atlanta GA USA

Artificial neural networks (ANNs) which have no off-line pre-training, can be trained continually on-line to identify an inverter fed induction motor and control its stator currents. Due to the small time constants of the motor circuits, the time to complete one training cycle has to be extremely small. This paper proposes and evaluates a new, fast, on-line training algorithm which is based on the method of random search training, termed the random weight change (RWC) algorithm. Simulation results show that RWC training of an ANN yields performance very much the same as conventional backpropagation training. Unlike backpropagation, however, the RWC method can be implemented in mixed digital/analog hardware, and still have a sufficiently small training cycle time. The paper also proposes a VLSI implementation which one training cycle in as little as 8 /spl mu/sec. Such a fast ANN can identify and control the motor currents within a few milliseconds and thus provide self-tuning of the drive while the ANN has no prior information whatsoever of the connected inverter and motor.

关键词： Induction motors Stators Artificial neural networks Induction machines Equations Inverters backpropagation algorithms Machine vector control Computer networks Electronic mail

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Cascade error projection: an efficient hardware learning algorithm

Cascade error projection: an efficient hardware learning alg...

引用

International Conference on Neural Networks

作者： T.A. Duong Center For Space Microelectronics Technology Jet Propulsion Laboratory California Institute of Technology Pasadena CA USA Department of Electrical Engineering University of California Irvine CA USA

A new learning algorithm, termed cascade error projection (CEP), which provides efficient learning in hardware is presented. This algorithm is adapted a constructive architecture from cascade correlation and the dynamical stepsize of A/D conversion from the cascade backpropagation algorithm. The CEP technique is faster to execute, because part of the weights are deterministically obtained, and the learning of the remaining weights from the inputs to the hidden unit is performed as a single-layer perceptron learning with previously determined weights kept frozen. In addition, one can start out with zero weight values when the learning of each layer is commenced. Further, unlike cascade correlation algorithm, only a single hidden unit is added at a time. Therefore, the simplicity in hardware implementation is also achieved. In simulation, a fixed 100 epoch iterations is used for each single-layer perceptron learning. The highlight of this algorithm is that with round-off method, 5- to 8-bit parity problems can be solved with a limited synaptic resolution of only 3- to 4-bit, and the same problem with truncation technique would be solved with 5-bit or more synaptic resolution.

关键词： Hardware backpropagation algorithms

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Deconvolution neural networks for ultrasonic testing

Deconvolution neural networks for ultrasonic testing

引用

IEEE Symposium (IUS) Ultrasonics

作者： M.S. Unluturk J. Saniie Electrical and Computer Engineering Depanment Illinois Institute of Technology Chicago IL USA

In this study, three novel design procedures have been developed in implementing deconvolution using neural network algorithms. The first method is called the Deconvolution Neural Network (DNN), the second method is named the Autoassociative Deconvolution Neural Network (ADNN), and the third method is referred to as the Probabilistic Deconvolution Neural Network (PDNN). The DNN trains the network by employing brute force and by exposing the network to a set of target echoes with and without noise. The ADNN processes the data for signal-to-noise ratio enhancement using an autoassociative neural network, and then applies the deconvolution neural network. The PDNN consists of two processing stages. The first stage estimates parameters using Gram-Charlier approximation to describe the probability density functions corresponding to target echoes and scattering noise. Then, in the second processing block, these parameters are used to classify and detect multiple target echoes. Results obtained in the performance analysis of these algorithms indicate that multiple interfering target echoes can be deconvolved and resolved accurately in the presence of noise.

关键词： Deconvolution Neural networks Testing Algorithm design and analysis backpropagation algorithms Neurons Working environment noise Noise figure Noise level Computer networks

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On improved learning algorithms with adaptive parameter regulation in feedforward nets

On improved learning algorithms with adaptive parameter regu...

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International Conference on Neural Networks

作者： J. Wille T. Kolb Department of Mathematics Cottbus University of Technology Cottbus Germany

This paper should contribute to a structured and theoretical view of the backpropagation algorithm and some of its well-known extensions. Based on a mathematical investigation of the algorithms conditions for structured improvements and developments of these techniques are described. The construction of adaptive parameter regulations for learning and momentum rate follow. These parameter regulations allow the presentation of adaptive learning techniques. It is shown that off-line versions of these techniques represent minimization methods which are exact in mathematical sense. Under consideration of complexity conditions on-line algorithms are preferred and described in detail. Finally their numerical behaviour is investigated and simulation results are presented in comparison with standard algorithms.

关键词： backpropagation algorithms Minimization methods

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Pitch synchronous Fourier transform using neural networks

Pitch synchronous Fourier transform using neural networks

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International Conference on Neural Networks

作者： M. Namba Y. Ishida Department of Electronics and Communication Meiji University Kawasaki Japan

In this paper, we present an overall view of the adaptive discrete Fourier transform algorithm using neural networks, and its application example for adaptive filtering. The underlying concept of the proposed method is that the continuous Fourier transform can be approximated by the discrete Fourier transform. For structuring an inverse transform system, Fourier coefficients corresponding to weight values in the network are obtained by the backpropagation algorithm to minimize the error between the output of neural networks and the signals to be analyzed. Simulation results show that our method is effective and useful for the spectral analysis of the speech signals.

关键词： Fourier transforms Neural networks Discrete Fourier transforms Filtering algorithms Adaptive filters backpropagation algorithms Signal analysis Algorithm design and analysis Analytical models Spectral analysis

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A digital systolic neural network chip (DSNC)

A digital systolic neural network chip (DSNC)

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IEEE Convention of the Electrical and ELectronic Engineers in Israel

作者： N. Weinberg R. Ginosar Electrical Engineering Department Technion-Israel Institute of Technology Haifa Israel

The DSNC chip implements one neural network layer having 25 fully inter-connected neurons. It is a digital, systolic, pipelined, integer based architecture. The chip is primarily designed for image processing tasks devised upon the basic backpropagation neural-network algorithm. Forward processing as well as learning are supported and carried out in parallel with each other. A network built using this chip can have unlimited number of layers when working in forward mode, and up to three layers if learning is required. The topology of a network is user defined. A chip can handle up to 25 inputs in each input example. Input and output data are nine bits; weights, biases and their updated values are eight bits. A chip can process images of up to 550/spl times/550 pixels in real-time when processing neighborhoods of 25 pixels, and larger images when using smaller neighborhoods. The chip requires about two million transistors and 180 I/O pins.

关键词： Neural networks Neurons backpropagation algorithms Image processing Pixel Network topology Arithmetic Cities and towns Process design Algorithm design and analysis

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Application of artificial neural networks to inverse problems in biomagnetism

Application of artificial neural networks to inverse problem...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： K.K. Parker J.P. Wikswo Living State Physics Group Department of Physics and Astronomy Vanderbilt University Nashville TN USA

We have applied an artificial neural network (ANN) using the backpropagation learning algorithm to the biomagnetic inverse problem. A forward model was used to calculate the magnetic fields from propagating action potentials (APs) as would be seen in nerve or muscle bundles. This forward model depicted two design schemes of a high-resolution SQUID magnetometer, whose three pickup coils were in two configurations, a stacked array and a planar array with respect to the direction of propagation in the idealized bundle. The ANN was tasked with determining the location of the source in two dimensions, with the third being the direction of propagation which was assumed to be known. The ability of the network to determine the source location was dependent on the arrangement of the magnetometer pickup coils.

关键词： Artificial neural networks Inverse problems Coils backpropagation algorithms Biomagnetics Magnetic fields Muscles SQUID magnetometers Planar arrays Position measurement

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