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检索条件"主题词=Backpropagation Algorithms"

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Identification of switched reluctance motor states using application specific artificial neural networks

Identification of switched reluctance motor states using app...

引用

International Conference on Industrial Electronics, Control and Instrumentation

作者： J.J. Garside R.H. Brown A.A. Arkadan Marquette University Milwaukee WI USA

In this paper a novel artificial neural network architecture suitable to identify the states of a switched reluctance motor is developed. This architecture incorporates the a priori knowledge about the motor directly into the structure of a feedforward artificial neural network. A method for backpropagating the error is presented with emphasis given to the specifically developed application specific layers for the switched reluctance motor. The switched reluctance motor model is given. A summary of the integration of the motor model into the ANN is presented. Simulation results show increased convergence rates as well as superior overall identification of the motor states.

关键词： Reluctance motors Artificial neural networks Reluctance machines backpropagation algorithms Neurons Torque Switching circuits Equivalent circuits Circuit simulation Concurrent computing

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Dynamic load sharing within workstation clusters

Dynamic load sharing within workstation clusters

引用

IEEE International Conference on algorithms and Architectures for Parallel Processing (ICAP)

作者： P. Williams R. Togneri Department E&E Engineering Centre for Intelligent Information Processing System Nedlands Australia

Workstation clusters, in which user processes run on one specified machine, cause the potential for a load imbalance. An analysis is conducted to determine how various system resources interact with one another in terms of job throughput and user interaction time. Quantitative and qualitative analyses of theoretical load sharing methods are used in the development of a well engineering system which is configurable, reliable and has a standard interface. Performance evaluation and system testing show very positive results.< >

关键词： Workstations backpropagation algorithms Throughput Floods Condition monitoring Intelligent systems Machine intelligence Australia Reliability engineering Systems engineering and theory

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Acceleration of back-propagation for learning the forward and inverse kinematic equations

Acceleration of back-propagation for learning the forward an...

引用

International Symposium on Neuroinformatics and Neurocomputers

作者： A. Ramdane-Cherif V. Perdereau M. Drouin PARC Université Paris-VI Paris France

Learning in the context of neural networks means finding a set of synaptic weights that make the network perform the desired function. backpropagation it has two major drawbacks in its leaning efficiency such as slow learning speed and convergence to local minima. In this paper, the 1D minimization with respect to learning rate has been incorporated into backpropagation algorithm. Various techniques of a 1D optimization have been developed to adjust the order of learning rate during training, such as Goldstein method, Wolfe-Powell method, and dichotomy method. These methods under the backpropagation algorithm are used to learn forward and inverse kinematic equations of two degrees of freedom arm robot manipulator. The comparative study presented in this paper compares by simulation these different methods with respectively the standard backpropagation algorithm and the optimal gradient method. The simulation results show that the gradient method combined with the Goldstein or Wolfe-Powell method gives the best performance and faster minimization of the criterion.

关键词： Acceleration backpropagation algorithms Minimization methods Gradient methods Neural networks Convergence Optimization methods Kinematics Equations Robots

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Neural network techniques for fine-form discrimination

Neural network techniques for fine-form discrimination

引用

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

作者： F. Germagnoli S. Lazzari Giovanni Magenes Diparthento di Idormatica e Sistemistica University of Pavia Pavia Italy

The ability of neural network (NN) techniques in processing tensorial tactile images have been investigated. Tactile images were provided by a sensor array of 21/spl times/21 sensitive elements, realized with piezoelectric polymers, able to detect the six components of the stress tensor. Two different types of NN have been used: a multilayer perceptron trained with the backpropagation algorithm, to filter and pre-process the data (reconstruction phase) and an ART network, to recognize different object typology (classification phase).

关键词： Neural networks Sensor arrays Image sensors Tactile sensors Polymers Tensile stress Multilayer perceptrons backpropagation algorithms Filters Image reconstruction

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Median and neural networks hybrid filters

Median and neural networks hybrid filters

引用

International Conference on Neural Networks

作者： A. Taguchi M. Muneyasu T. Hinamoto Dept. of Electr. & Electron. Eng. Musashi Inst. of Technol. Tokyo Japan Faculty of Engineering Hiroshima University Higashihiroshima Hiroshima Japan

In this paper we present a new nonlinear filter with neural networks for signal processing in a mixed noise environment, where both Gaussian noise and impulsive noise may be present. Mean filters can effectively remove the Gaussian noise and order statistics filters can effectively remove the impulsive noise. However it is difficult to combine these filters to remove the mixed noise in an image processing environment without blurring the image details or edges. In order to remove a mixed noise while preserving edges and details, we develop a novel prototype filter which is composed of two stages. The purpose of the first and second stages is to remove the impulse noise and Gaussian noise, respectively. The prototype filter can be shown by a network structure. This network can be extended and generalized to the median and neural networks hybrid (MNNH) filter. The coefficients of the MNNH filter are learned by the backpropagation algorithm.

关键词： Neural networks Gaussian noise Working environment noise Prototypes Nonlinear filters Signal processing Statistics Image processing backpropagation algorithms

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Dynamic structure adaptation in feedforward neural networks-an example of plant monitoring

Dynamic structure adaptation in feedforward neural networks-...

引用

International Conference on Neural Networks

作者： R. Kozma M. Kitamura Department Nuclear Engineering University of Tohoku Sendai Japan

In the paper artificial neural networks are introduced which are capable of adapting their structure in response to changes in the environment. Feedforward neural networks with multi-layer architecture were trained by modified backpropagation algorithm with forgetting of the connection weights. The applied training algorithm results in a skeleton network structure which can be used for knowledge acquisition. In the authors' algorithm, the decayed weights are not deleted but fluctuate around zero with a magnitude proportional to the rate of forgetting. Small fluctuations of the weights can grow into a structural evolution in the neural net if properties of the input clusters change. This feature is especially advantageous to on-line system monitoring applications when a rigid neural network structure could lead to mis-interpretation of measurements among dynamically changing conditions. Structural adaptation features and improved generalization capability of the proposed method are illustrated using an example of system state identification in a nuclear reactor.

关键词： Neural networks Feedforward neural networks Artificial neural networks backpropagation algorithms Multi-layer neural network Skeleton Knowledge acquisition Clustering algorithms Fluctuations Condition monitoring

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A circuit for learning in fuzzy logic-based controllers

A circuit for learning in fuzzy logic-based controllers

引用

IEEE International Symposium on Circuits and Systems (ISCAS)

作者： F. Colodro A. Torralba L.G. Franquelo Departmento de Ingeniería Electrónica Escuela Superior de Ingenieros Seville Spain

This paper presents a chip intended for learning in fuzzy logic-based controllers. It uses the weight perturbation method to tune the parameters of the controller. This circuit can be used as a co-processor of any fuzzy logic-based controller whose internal parameters (e.g, output singletons and input membership function parameters) are externally accessible. The chip has been designed using a commercial 1.0 /spl mu/m CMOS technology.

关键词： Circuits Fuzzy control Fuzzy logic Pulse width modulation Space vector pulse width modulation Signal generators Pulse amplifiers Clocks backpropagation algorithms Perturbation methods

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Handwritten Hindi character recognition using multilayer perceptron and radial basis function neural networks

Handwritten Hindi character recognition using multilayer per...

引用

International Conference on Neural Networks

作者： B.K. Verma Department of Computer Science Warsaw University of Technology Warsaw Poland Department of Electrical and Electronic Engineering Centre for Intelligent Information Processing Systems Visitor University of Western Australia WA Australia

This paper compares the multilayer perceptron (MLP) networks and the radial basis function (RBF) networks in the task of handwritten Hindi character recognition (HCR). The error backpropagation algorithm was used to train the MLP networks. An automatic HCR system using MLP and RBF networks is presented. The experiments were carried out on two hundred forty five samples of five writers. The results showed that the MLP networks trained by the error backpropagation algorithm were superior in recognition accuracy and memory usage. However, they suffered from long training time than that of RBF networks.

关键词： Character recognition Multilayer perceptrons Radial basis function networks Neural networks Natural languages backpropagation algorithms Multi-layer neural network Feature extraction Pattern recognition Pixel

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Generalized gradient adaptive step sizes for stochastic gradient adaptive filters

Generalized gradient adaptive step sizes for stochastic grad...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： S.C. Douglas Department of Electrical Engineering University of Utah Salt Lake UT USA

We derive new adaptive step size algorithms for two general classes of modified stochastic gradient adaptive filters that include the sign-error, sign-data, sign-sign, and normalized gradient adaptive filters as specific cases. These computationally-simple parameter adjustment algorithms are based on stochastic gradient approximations of steepest descent procedures for the unknown parameters. Analyses of the algorithms show that the stationary points of the steepest descent procedures yield the optimum step size values at each time instant as obtained from statistical analyses of the adaptive filter updates. Simulations verify the theoretical results and indicate that near-optimal tracking performance can be obtained from each of the adaptive step size algorithms without any knowledge of the rate of change of the unknown system.

关键词： Stochastic processes Adaptive filters Signal processing algorithms Convergence Finite impulse response filter Least squares approximation backpropagation algorithms Cities and towns Algorithm design and analysis Statistical analysis

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Real-time electricity pricing using a neural network approach

Real-time electricity pricing using a neural network approac...

引用

Canadian Conference on Electrical and Computer Engineering (CCECE)

作者： M.G. Dondo M.E. El-Hawary Department of Electrical Engineering Technical University of Nova Scotia Halifax NS Canada

An approach for the determination of real-time electricity prices on-line using artificial neural networks is presented. This method considers the real-time load and automatically determines the real-time electricity rate at that particular instant for a given consumer. A feedforward backpropagation artificial neural network is trained for a particular power system network using data and results from an optimal power flow based spot pricing algorithm.

关键词： Pricing Neural networks Artificial neural networks Power system modeling Load flow Energy consumption backpropagation algorithms Reactive power Power generation Costs

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