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检索条件"主题词=Backpropagation Algorithms"

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Speaker independent vowel recognition using neural tree networks

Speaker independent vowel recognition using neural tree netw...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： A. Sankar R.J. Mammone CAIP Center and Department of Electrical Engineering Rutgers University Piscataway NJ USA

A novel approach to the problem of speaker-independent vowel recognition is presented. A novel neural architecture and learning algorithm called neural tree networks (NTNs) are developed. This network uses a tree structure with a neural network at each tree node. The NTN architecture offers a very efficient hardware implementation as compared to MLPs (multilayer perceptrons). The NTN algorithm grows the neurons while learning as opposed to backpropagation, for which the number of neurons must be known before learning can begin. The proposed algorithm is guaranteed to converge on the training set whereas backpropagation can get stuck in local minima. Simulation results on a speaker-independent vowel-recognition task are presented which show that the new method is superior to both the MLP and decision tree methods.< >

关键词： Speech recognition Decision trees Neural networks Neurons Pattern recognition backpropagation algorithms Multilayer perceptrons Classification tree analysis Testing Feedforward neural networks

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Quantization level increase in human face images using multilayer neural network

Quantization level increase in human face images using multi...

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International Joint Conference on Neural Networks (IJCNN)

作者： K. Nakayama Y. Kimura H. Katayama Department of Electrical and Computer Engineering Faculty of Tech. Kanazawa University Kanazawa Japan

In this paper, quantization level increase in human face images using a multilayer neural network (NN) is investigated. Basically, it is impossible to increase quality without any other information. However, when images are limited to some category, image restoration could be possible, based on the common properties in this category. The multilayer NN is trained using human face images of 32/spl times/32 pixels with 8-levels as the input data, and 256-level images as the targets. The standard backpropagation algorithm is employed. 20, 40 and 100 training data are examined. By increasing the training data, a general function of regenerating missing information can be achieved. The internal structure of the trained NN is analyzed using some special input images. As a result, it has been confirmed that the NN regards the input image as the human face, and extracts features of the face. The input image is transformed using these features and the common properties of the training data, extracted and held on the connection weights, to the human face image.

关键词： Multi-layer neural network Quantization Humans Face Neural networks Training data Data mining Image restoration Pixel backpropagation algorithms

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A neural network trained with the extended Kalman algorithm used for the equalization of a binary communication channel

A neural network trained with the extended Kalman algorithm ...

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IEEE Workshop on Neural Networks for Signal Processing

作者： M. Birgmeier Institut für Nachrichtentechnik und Hochfrequenztechnik Technische Universität Wien Vienna Austria

This paper describes a feedforward neural network architecture trained with the extended Kalman filter algorithm instead of the standard (LMS) method. It presents a simplified recursive procedure for calculating the necessary derivatives. The resulting algorithm is then used to train a network to adapt to the decision boundary of an optimal receiver for a binary communication channel, resulting in increased convergence speed and better approximation properties.< >

关键词： Neural networks Kalman filters Feedforward neural networks Communication channels Least squares approximation Artificial neural networks Convergence backpropagation algorithms Partitioning algorithms Equations

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Considering adequacy in neural network learning

Considering adequacy in neural network learning

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International Conference on Neural Networks

作者： C.S. Herrmann F. Reine Technische Hochschule Darmstadt Fachgebiet Intellektik Darmstadt Germany Technische Hochschule Darmstadt Institut für Datentechnik Darmstadt Germany

We propose a new learning strategy to consider aspects of cognitive adequacy during the training of artificial neural networks instead of merely taking the overall error into account. Well known learning algorithms for neural networks can be adapted in a way that leads to an adequate behaviour by using a fuzzy system to provide pattern specific learning rates based on a predetermined measure of pattern difficulty and the current classification error. First experiments with adequate backpropagation show that adequate learning provides faster generalization-error convergence than its conventional counterpart.

关键词： Neural networks Humans Artificial intelligence Artificial neural networks Psychology backpropagation algorithms Fuzzy systems Current measurement Convergence Biological system modeling

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A building of the genetic-neural network for sinter's burning through point

A building of the genetic-neural network for sinter's burnin...

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International Conference on Information and Automation (ICIA)

作者： Wushan Cheng Minrui Fei Shanghai University Engineering of Science Shanghai China School of Mechatronical Engineering & Automation Shanghai University Shanghai China

ISBN: (纸本)0780386299

This paper presents the genetic-neural network for sinter's burning through point since BTP control is the most important, which is tightly coupled with sinter ore quality. In offline, advanced genetic algorithm (GA) is used to optimize the original connection weights and thresholds, and during online, hybrid neural network (HNN) inherited from the principle of backpropagation is used to train the map parameters and improve the system precision in each sampling period. The results obtained from the actual process demonstrate that the performance and capability of the proposed system are superior.

关键词： Neural networks Temperature control Ignition Genetic engineering Genetic algorithms Sampling methods backpropagation algorithms Velocity control Predictive models Automation

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A New Method to Improve the Gradient Based Search Direction to Enhance the Computational Efficiency of Back Propagation Based Neural Network algorithms

A New Method to Improve the Gradient Based Search Direction ...

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Asia International Conference on Modeling & Simulation

作者： Nazri Mohd Nawi Rajesh S. Ransing Meghana R. Ransing Faculty of Information Technology and Multimedia Universiti Tun Hussein Onn Malaysia Batu Pahat Johor Malaysia

Neural-network techniques, particularly back- propagation algorithms, have been widely used as a tool for discovering a mapping function between a known set of input and output examples. Neural networks learn from the known example set by adjusting its internal parameters, referred to as weights, using an optimisation procedure based on the 'least square fit principle'. The optimisation procedure normally involves thousands of iterations to converge to an acceptable solution. Hence, improving the computational efficiency of a neural- network algorithm is an active area of research. It has been shown in the existing literature that the variation of the gain parameter improves the learning efficiency of the gradient-descent method. However, it can be concluded from previous researchers' claims that the adaptive-gain variation improved the learning rate and hence the efficiency. It was discovered in this research that the gain variation has no influence on the learning rate; however, it actually influences the search direction. A novel technique of integrating the adaptive-learning rate method coupled with an improved search direction for improving the computational efficiency of neural networks is presented. The new approach had shown that this modification can significantly enhance the computational efficiency of training process.

关键词： Computational efficiency Neural networks Optimization methods backpropagation algorithms Equations Error correction Approximation algorithms Asia Computational modeling Information technology

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Real-time electricity pricing using a neural network approach

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

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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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Dynamic neural network-based estimator for fault diagnosis in reaction wheel actuator of satellite attitude control system

Dynamic neural network-based estimator for fault diagnosis i...

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International Joint Conference on Neural Networks (IJCNN)

作者： E.S. Tehrani K. Khorasani S. Tafazoli Concordia University Montreal QC CA

This paper presents an approach to simultaneous fault detection and isolation in the reaction wheel actuator of the satellite attitude control system. A model-based adaptive nonlinear parameter estimation technique is used based on a highly accurate reaction wheel dynamical model while each parameter is an indication of a specific type of fault in the system. The estimation is based on the nonlinear finite-memory filtering strategy that is solved for optimal estimation functions. To make the optimization feasible for on-line application, the optimal estimation functions are approximated by MLP neural networks thus reducing the functional optimization problem to a nonlinear programming problem, namely, the optimization of the neural weights. The well-known standard back-propagation algorithm and backpropagation through-time algorithm were employed inside the neural adaptation algorithms to obtain the required gradients. Simulation results show the effectiveness of the methodology for the proposed application.

关键词： Neural networks Fault diagnosis Wheels Actuators Satellites backpropagation algorithms Nonlinear dynamical systems Fault detection Adaptive systems Parameter estimation

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Mixed gradient based fast learning algorithm for MLP with its applications to MQAM digital mobile radio receptions

Mixed gradient based fast learning algorithm for MLP with it...

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

作者： Z. Xiang G. Bi Dept. of Radio Eng. Southeast Univ. Nanjing China Department of Radio Engineering South-East University Nanjing China

A new fast adaptive learning algorithm called a mixed gradient based fast learning algorithm with variable learning gain and selective weight updates is proposed which uses the steepest descent method combined with the conjugate gradient method. The line search unconstrained optimization method is adopted to adaptively adjust the learning gain eta . The derivation of the fast learning algorithm is given. Computer simulation results are included.< >

关键词： backpropagation algorithms Cost function Gradient methods Computational complexity Gain Optimization methods Convergence Neurons Land mobile radio Bismuth

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A neural network sidelobe suppression filter for a pulse-compression radar with powers-of-two weights

A neural network sidelobe suppression filter for a pulse-com...

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Mediterranean Electrotechnical Conference (MELECON)

作者： Yu.P. Grishin A. Zankiewicz Department of Electrical Engineering Bialystok Technical University Poland

A new compression filter for binary coded wideband digital signals with sidelobe reduction capabilities using a feedforward time-delay neural network is considered. This filter uses powers-of-two synaptic weights and the backpropagation learning algorithm. Using digital hardware implementation of such a filter for pulse compression is easier owing to elimination of multipliers. The simulation results showed that the time sidelobes of the output signal can be reduced up to about 60 dB. The quantization of the filter weights causes additional losses of about 6-14 dB depending on the input signal to noise ratio.

关键词： Neural networks Radar Digital filters Wideband Feedforward neural networks backpropagation algorithms Hardware Pulse compression methods Quantization Signal to noise ratio

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