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检索条件"主题词=Backpropagation Algorithms"

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On-line fuzzy neural modeling with structure and parameters updating

On-line fuzzy neural modeling with structure and parameters ...

引用

IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (CIMSA)

作者： A. Ferreyra W. Yu Departamento de Electrónica Sección de Instrumentación Universidad Autonoma Metropolitana Azcapotzalco Mexicali Mexico Departamento de Control Automático CINVESTAV Mexicali Mexico

In this paper we propose a novel online clustering approach which can be applied in a general class of fuzzy neural networks. Both structure identification and parameters learning are online. The new clustering method for the structure identification can separate input-output data into different groups (rulenumber) by online input/output data. For the parameter learning, our algorithm has two advantages over the others. First, the normal methods for parameter identification are based on a fixed structure and whole data, for example ANFIS, but after clustering we know each group corresponds to one rule, so we train each rule by its group data, it is more effective. Second, we give a time-varying learning rate for the common used backpropagation algorithm, we prove that the new algorithm is stable and faster than backpropagation algorithm.

关键词： Clustering algorithms backpropagation algorithms Fuzzy neural networks Fuzzy systems Humans Optimization methods Instruments Neural networks Input variables Parameter estimation

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Local minima free neural network learning

Local minima free neural network learning

引用

International IEEE Conference on Intelligent Systems, IS

作者： I.N. Jordanov T.A. Rafik Department of Computer Science and Software Engineering University of Portsmouth Portsmouth UK School of Product and Engineering Design University of Wales Institute of Science and Technology Cardiff UK

Global optimization algorithm applied for feedforward neural networks (NN) supervised learning is investigated. The network weights are determined by minimizing the traditional backpropagation error function. The difference is that the optimization based learning algorithm utilizes stochastic technique, based on the use of low discrepancy sequences. This technique searches the parameter space, defined by the network weights, to define initial regions of attraction with candidates for local minima, and then exploits each region to locate the minima, and to determine a global minimum. The proposed technique is initially tested on multimodal mathematical functions and subsequently applied for training NN with moderate size for solving simple benchmark problems. Finally, the results are analysed, discussed, and compared with others.

关键词： Neural networks Stochastic processes Convergence backpropagation algorithms Supervised learning Benchmark testing Pattern recognition Function approximation Speech recognition Robot control

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Conjugate gradient algorithm for series cascade nonlinear adaptive filters

Conjugate gradient algorithm for series cascade nonlinear ad...

引用

Midwest Symposium on Circuits and Systems (MWSCAS)

作者： C. Radhakrishnan W.K. Jenkins A. Garga Department of Electrical Engineering Pennsylvania State University University Park PA USA Applied Research Laboratory Pennsylvania State University University Park PA USA

This paper considers series-cascade nonlinear filter architectures consisting of a linear FIR input filter, a memoryless polynomial nonlinearity, and a linear FIR/IIR output filter (LNL). Earlier publications reported on the development of the LMS and RLS backpropagation algorithms for training this same adaptive filter structure. In this paper the conjugate gradient backpropagation algorithm is derived for the joint adaptation of the LNL structure. An echo cancellation example is considered to study the algorithm in terms of its learning characteristics and computational complexity.

关键词： Adaptive filters backpropagation algorithms Finite impulse response filter Nonlinear filters IIR filters Polynomials Least squares approximation Resonance light scattering Echo cancellers Computational complexity

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Harmonic restraint differential protection of power transformer based MRBFN

Harmonic restraint differential protection of power transfor...

引用

International Universities Power Engineering Conference (UPEC)

作者： Z. Moravej Moshanir Company Iran

This paper presents a minimal radial basis function neural network (MRBFNN) scheme for harmonic restraint differential protection of power transformers. The minimal resource allocation network ( M-RAN) learning algorithm which is a sequential learning radial basis function neural network is shown to realize networks with far fewer hidden neurons with the better or same approximation/classification accuracy without resorting to trial and error. Performance of this model is compared with the usual one, i.e., the feedforward backpropagation (FFBP) model. The results show that this new algorithm is better in terms of accuracy and speed with respect to detection of faults and requires less training time. The proposed protection scheme has been evaluated using simulated data obtained through the EMTP/ATP package.

关键词： Power system harmonics Protection Power transformers Radial basis function networks backpropagation algorithms Resource management Approximation algorithms Neurons Fault detection EMTP

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Importance sampling in neural detector training phase

Importance sampling in neural detector training phase

引用

Proceedings of the Biannual World Automation Congress

作者： D. Andina J. Martinez-Antorrena I. Melgar UNIV. POLITéCNICA DE MADRID SPAIN

One of the mayor limitations associated to Neural Networks (NNs) learning algorithms is the number of input-output pattern pairs needed to obtain the desired classilication pcrlbmancc. In many practical cases, the number of pairs used to design the NN is much lower than necessary. Either bccause of the excessivc price of data acquisition or, simply, for availability reasons. An Importance Sampling (IS) technique is applied in this paper to NN training a in order to drastically improve the training cfticieiicy of the given training patterns. For this purpose, the Mean Square Error (MSE) objective function of a Backpropagalion algorithm has adequately been moditied hy applying a suboptimal IS fiinction. The application of the IS fiinction accelerates training convergence whereas it inaintains NN perromance.

关键词： Monte Carlo methods Phase detection Detectors Sampling methods backpropagation algorithms Discrete event simulation Ores Mean square error methods Neural networks Algorithm design and analysis

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Comparative study of connectionist techniques for implementing the pattern recognition system of an artificial nose

Comparative study of connectionist techniques for implementi...

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International Joint Conference on Neural Networks (IJCNN)

作者： M.B. de Almeida M.C.P. de Souto T.B. Ludermir Centre of Informatics Federal University of Pernambuco (UFPE) Recife Pernambuco Brazil Department of Informatics Federal University of Rio Grande do Norte Natal Rio Grande do Norte Brazil

This work presents an experimental analysis on the use of different neural model for the implementation of the pattern recognition system of an artificial nose. The models investigated are the multi-layer perceptrons with, respectively, the backpropagation, tabu search and resilient backpropagation; and the radial basis function networks.

关键词： Pattern recognition Nose backpropagation algorithms Informatics Sensor systems Sensor arrays Electronic mail Topology Radial basis function networks Testing

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On the effectiveness of MFCCs and their statistical distribution properties in speaker identification

On the effectiveness of MFCCs and their statistical distribu...

引用

IEEE International Symposium on Virtual Environments, Human-Computer Interfaces and Measurement Systems (VECIMS)

作者： K.I. Molla K. Hirose Department of Frontier Informatics University of Tokyo Tokyo Japan

This paper presents a study on the effectiveness of mel-frequency cepstrum coefficients (MFCCs) and some of their statistical distribution properties (skewness, kurtosis, standard deviation) as the features for text-dependent speaker identification. Multi-layer neural network with backpropagation learning algorithm is used here as the classification tool. The MFCCs representing the speaker characteristics of a speech segment are computed by nonlinear filterbank analysis and discrete cosine transform. The speaker identification efficiency and the convergence speed of the neural network are investigated for different combinations of the proposed features. The result shows that the first MFCC degrades the identification competence and statistical distribution parameters enhance the training speed of the neural network.

关键词： Statistical distributions Speech analysis Neural networks Cepstrum Multi-layer neural network backpropagation algorithms Filter bank Cepstral analysis Discrete cosine transforms Convergence

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Fuzzy neural networks for identification and control of DC drive systems

Fuzzy neural networks for identification and control of DC d...

引用

IEEE Conference on Control Technology and Applications (CCTA)

作者： M.S. Mostafa M.A. El-Bardini S.M. Sharaf M.M. Sharaf Department of Industrial Electronics and Control Faculty of Electronic Engineering Minufiya University Egypt Electrical Machines and Power Engineering Faculty of Engineering Helwan University Egypt

This paper demonstrates the application of fuzzy neural networks (FNN's) in identification and control of DC motor drive system. This technique compensates the drawbacks of the fuzzy-logic controllers (FLC) with fixed membership function and quantization levels. The membership function and quantization levels are adapted according to the system operating condition changes. Two FNN are proposed with different learning rates. The first is FNN identifier to provide the sensitivity inference about the drive system changes. The second is FNN controller with adaptive ability to regulate the drive system against the operating condition changes and disturbances. An online backpropagation algorithm is used to achieve both FNN identifier and controller objectives. Experimental setup of the suggested technique is developed of the DC drive system. Comparison between the developed technique and FLC is highlighted and the experimental test results are listed.

关键词： Fuzzy neural networks Fuzzy control Control systems Quantization DC motors Programmable control Adaptive control Drives backpropagation algorithms Testing

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Predictive fuzzy clustering model for natural streamflow forecasting

Predictive fuzzy clustering model for natural streamflow for...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： M.H. Magalhaes R. Ballini R. Goncalves F. Gomide DCA-FEEC State University of Campinas-UNICAMP Campinas Brazil DTA-IE State University of Campinas-UNICAMP Campinas Brazil

Planning of hydroelectric systems is a complex and difficult task once it involves non-linear production characteristics and depends on numerous variables. A key variable is the natural streamflow. The streamflow values covering the entire planning period must be accurately forecasted because they strongly influence energy production. Currently, streamflow prediction using Box and Jenkins methodology prevails in the electric power industry. This paper suggests a fuzzy prediction model based on fuzzy clustering as an alternative for streamflow forecast. The model uses fuzzy c-means clustering to explore past data structure, and a median and pattern recognition procedures to capture similarities between streamflow history and data used for prediction. Computational experiments with actual data suggest that the predictive clustering approach performs globally better than periodic auto regressive moving average models, the current streamflow forecasting methodology adopted by many hydroelectric systems worldwide.

关键词： Predictive models Autoregressive processes Fuzzy neural networks Neural networks Load forecasting Water resources Power system modeling Clustering algorithms backpropagation algorithms Production planning

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Training quasi-ARX neural network model by homotopy approach

Training quasi-ARX neural network model by homotopy approach

引用

SICE Annual Conference

作者： Jinglu Hu Xibin Lu K. Hirasawa Graduate School of Information Production and Systems Waseda University Fukuoka Japan

ISBN: (纸本)4907764227

Quasi-ARX neural networks (NN) are NN based nonlinear models that not only have linear structures similar to linear ARX models, but also have useful interpretation in part of their parameters. However when applying an ordinary backpropagation (BP) for the training, it has potential risk that the BP algorithm is stuck at a local minimum, which results in a poorly trained model. In this paper, a homotopy continuation method is introduced to improve the convergence performance of BP training. The idea is to start the BP training with the criterion function for linear ARX model, which is gradually deformed first into one for quasi-ARX NN model with linear node functions, and then into the actual one for quasi-ARX NN with sigmoid node functions. By building the deformation into a usual recursive procedure for BP training of quasi-ARX NN model with adaptable node functions so that the proposed homotopy based BP algorithm is able to achieve improved convergence performance without much increase in the computation load. Numerical simulation results show that the proposed homotopy based BP has better performance than an ordinary BP.

关键词： Neural networks Deformable models backpropagation algorithms Convergence Production systems Numerical simulation System identification Nonlinear control systems Control systems Control system synthesis

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