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检索条件"主题词=Backpropagation Algorithms"

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Embedding fuzzy knowledge into neural networks for control applications

Embedding fuzzy knowledge into neural networks for control a...

引用

IEEE Symposium on Intelligent Vehicle

作者： S.K. Kenue Vehicle Systems Department General Motors Research and Development Center Warren MI USA

Fuzzy control has recently emerged as a new technique of knowledge-based intelligent control in which precise knowledge of control algorithms is not required. The control knowledge is expressed in terms of membership functions for control parameters and a given rule set which defines the relationship among various parameters. Although this technique is robust, it cannot learn and adapt as parameters change over time. Neural network control uses learning for defining mapping between input and output data. By using fuzzy logic rule/membership knowledge and neural network learning capability, this work proposes a new method for combining the two intelligent control methods. The proposed neuro-fuzzy method embeds fuzzy rule and membership knowledge into a neural network for training via a backpropagation algorithm. Results based on fuzzy control, the Iwata-Machida-Toda method, and this proposed method are given for cart-pole problems. The proposed method has the best response time and the smallest magnitude of oscillations near the setpoint.

关键词： Fuzzy control Fuzzy neural networks Neural networks Control systems Fuzzy logic Intelligent control backpropagation algorithms Process control Optimal control Vehicles

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Hybrid adaptive learning control of nonlinear system

Hybrid adaptive learning control of nonlinear system

引用

American Control Conference (ACC)

作者： Ping Zhang Y. Sankai M. Ohta Graduate School in Engineering University of Tsukuba Japan Institute of Engineering Mechanics University of Tsukuba Tsukuba Japan

A hybrid adaptive learning control for nonlinear dynamical systems is proposed. Feedforward multilayer neural networks are used to construct a controller. Parameters of the neural networks are adjusted by a dynamic backpropagation algorithm and a genetic algorithm. The genetic algorithm manages to escape local minima and reach the neighborhood of the global minimum on the squared error surface. The dynamic backpropagation algorithm is used to search the global minimum from its neighborhood. Computer simulations show that the tracking control performance of nonlinear dynamical systems can be enhanced by the proposed method.

关键词： Programmable control Adaptive control Nonlinear control systems Control systems Nonlinear systems Nonlinear dynamical systems Multi-layer neural network Neural networks backpropagation algorithms Genetic algorithms

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Application of a fast real time recurrent learning algorithm to text-to-phoneme conversion

Application of a fast real time recurrent learning algorithm...

引用

International Conference on Neural Networks

作者： Yee-Ling Lu Man-Wai Mak Wan-Chi Siu Department of Electronic Engineering Hong Kong Polytechnic University Hong Kong China

This paper attempts to perform text-to-phoneme conversion by using recurrent neural networks trained with the real time recurrent learning (RTRL) algorithm. As recurrent neural networks deal well with spatial temporal problems, they are proposed to tackle the problem of converting English text streams into their corresponding phonetic transcriptions. We found that, due to the high computational complexity, the original RTRL algorithm takes a long time to finish the learning. We propose a fast RTRL algorithm (FRTRL), with a lower computational complexity, to shorten the time consumed in the learning process.

关键词： Recurrent neural networks Computational complexity Neural networks Speech synthesis backpropagation algorithms Signal processing Neurofeedback State feedback Network synthesis Education

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Maximizing the target-pattern cross-correlation for training time-delay neural networks

Maximizing the target-pattern cross-correlation for training...

引用

International Conference on Neural Networks

作者： F. Lavagetto D.I.S.T. University of Genoa Italy

In this paper experimental conclusions are reported on the verification of a new learning procedure for training time delay neural networks (TDNN), based on the maximization of the cross-correlation between the output of the network (pattern) and the target reference sequence. This functional has been used for training a TDNN encharged of estimating the aperture of the speaker's mouth from the acoustic analysis of his speech. Performances have been compared to those reported in a previous paper obtained with classical MSE-based back-propagation. Experimental results provide clear evidence of the improvements, both in terms of convergence speed and estimation fidelity, achievable through this new training algorithm.

关键词： Neural networks Neurons Delay effects Speech analysis Finite impulse response filter Buffer storage Apertures Mouth Convergence backpropagation algorithms

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Neural networks for the identification of a three component distillation column

Neural networks for the identification of a three component ...

引用

IEEE International Caracas Conference on Devices, Circuits and Systems

作者： E. Margaglio M. Uria J.J. Rodriguez Dpto. dc Proccsos y Sistemas Universidad Simón Bolívar Caracas Venezuela Intevep S. A. Los Teques Miranda Venezuela

The identification of a three-component distillation column was performed using a multilayered neural network trained with the backpropagation algorithm. To find an appropriate network size, several adjustment tests were carried out during the experimentation. These tests included changing the number of hidden layers and number of the nodes in the hidden layer. Validation of the resulting neural model was made by comparison of network and process responses to inputs different from those used during training. The network adequately identified the system. Also, it was observed that the network is able to approximate the nonlinearities of the process with greater accuracy than an ARX model whose parameters were estimated using the classical least squares method.

关键词： Neural networks Distillation equipment Testing Multi-layer neural network backpropagation algorithms Least squares approximation Parameter estimation Least squares methods Feeds Valves

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A fuzzy inference network for classification

A fuzzy inference network for classification

引用

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

作者： Y. Cai H.K. Kwan Department of Electrical Engineering University of Windsor Windsor ONT Canada

A fuzzy inference network (FIN) is proposed. The proposed FIN preserves the advantages of both fuzzy classification algorithm and neural networks. It can learn membership functions directly from training samples and classify patterns according to the membership values. As efficient self-organizing learning algorithm is also presented.

关键词： Fuzzy neural networks Inference algorithms Classification algorithms backpropagation algorithms Neurons Neural networks Training data

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Interactive pattern classification by means of artificial neural networks

Interactive pattern classification by means of artificial ne...

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： M. Betrouni S. Delsert D. Hamad Centre d&3x2019 Automatique de Lille USTL Villeneuve d'Ascq France

In this paper, we deal with the visualization of multivariate data by nonlinear projection methods performed by several multilayer neural networks. As the visualization is the first step in interactive pattern classification, we provide the operator with a set of tools to manipulate the data. The results have been applied to a real biometrical example of the Guadeloupe honeybees races.

关键词： Pattern classification Artificial neural networks Multi-layer neural network Neural networks backpropagation algorithms Multidimensional systems Data visualization Data analysis Humans Principal component analysis

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Principal-feature classification

Principal-feature classification

引用

IEEE Workshop on Neural Networks for Signal Processing

作者： D.W. Tufts Q. Li Department of Electrical and Computer Engineering University of Rhode Island Kingston RI USA

The concept of classification using principal features is presented. The principal features defined in this paper are analogous to principal components in statistics and linear algebra. Neural network training can be done by sequential identification of principal features and corresponding pruning of the training data. Two neural network simplification algorithms, lossless and lossy simplifications, make the the classifier design more efficient. The design procedure is compared with other classifier design algorithms.

关键词： Training data Algorithm design and analysis Vectors Neural networks Statistics Algebra Performance analysis Testing Principal component analysis backpropagation algorithms

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An adaptive and modular recurrent neural network based power system load forecaster

An adaptive and modular recurrent neural network based power...

引用

International Conference on Neural Networks

作者： A. Khotanzad A. Abaye D. Maratukulam Electrical Engineering Department Southern Methodist University Dallas TX USA Pattem Recognition Technologies Richardson TX USA Power Delivery Group Electric Power Research Institute Inc. Palo Alto CA USA

This paper describes a recurrent neural network (RNN) based hourly load forecaster for hourly prediction of power system loads. The system is modular, consisting of 24 RNNs, one for each hour of the day. The RNNs considered are sigmoid type neural networks with a single hidden layer. Two types of recurrency are considered: one has connections between the hidden layer nodes, and the other has feedback from output to hidden layer nodes. The hours of the day are divided into four categories and a different set of load and temperature input variables is defined for the RNNs of each category. The RNNs are trained with Pineda's recurrent backpropagation algorithm. To handle non-stationarities, an adaptive scheme is used to adjust the RNN weights during the online forecasting phase. The performance of the forecaster was evaluated on real data from two electric utilities with excellent results.

关键词： Recurrent neural networks Power systems Load forecasting Neural networks Output feedback Neurofeedback Temperature Input variables backpropagation algorithms Power industry

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Investigation of generalization ability by using noise to enhance MLP performance

Investigation of generalization ability by using noise to en...

引用

International Conference on Neural Networks

作者： Y. Tsukuda H. Kurokawa S. Mori Department of Elect rical Engineering Keio University Yokohama Japan

The multilayer perceptron (MLP) is successfully used in many nonlinear signal processing applications. The backpropagation learning algorithm is very useful for various problems. But the MLP obtains low generalization ability if the number of hidden units is very large in training. In this paper, the authors show that if the MLP is trained with adding noise to hidden units, it obtains good generalization ability for any number of hidden units.

关键词： Signal processing algorithms backpropagation algorithms Noise generators Nonhomogeneous media Signal processing Pattern recognition Ear

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