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检索条件"主题词=Backpropagation Algorithms"

共 1892 条记录，以下是411-420 订阅

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An Immune and a Gradient-Based Method to Train Multi-Layer Perceptron Neural Networks

An Immune and a Gradient-Based Method to Train Multi-Layer P...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： R. Pasti L. Nunes de Castro Laboratory of Intelligent Systems Catholic University of Santos Sao Paulo Brazil

Multi-layer perceptron (MLP) neural network training can be seen as a special case of function approximation, where no explicit model of the data is assumed. In its simplest form, it corresponds to finding an appropriate set of weights that minimize the network training and generalization errors. Various methods can be used to determine these weights, from standard optimization methods (e.g., gradient-based algorithms) to bio-inspired heuristics (e.g., evolutionary algorithms). Focusing on the problem of finding appropriate weight vectors for MLP networks, this paper proposes the use of an immune algorithm and a second-order gradient-based technique to train MLPs. Results are obtained for classification and function approximation tasks and the different approaches are compared in relation to the types of problems they are more suitable for.

关键词： Multilayer perceptrons Neural networks Multi-layer neural network Immune system backpropagation algorithms Function approximation Machine learning algorithms Optimization methods Heuristic algorithms Evolutionary computation

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Support Vector Machine for Nonlinear System On-line Identification

Support Vector Machine for Nonlinear System On-line Identifi...

引用

International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE)

作者： Juan Angel Resendiz-trejo Wen Yu Xiaoou Li Departamento de Control Automatico CINVESTAV Mexicali Mexico Sección de Computación Departamento de Ingeniería Eléctrica CINVESTAV Mexicali Mexico

Neural networks is a very popular black-box identification tool. But it suffers some weaknesses for nonlinear on-line identification. For example, the learning process can only arrive local minima. The training algorithms are slow. Support vector machine (SVM) can overcome these problems. But the SVM needs all data to find optimal solution, it is not suitable for online identification. In this paper, we propose a new method to use SVM for on-line identification. We call it as recursive support vector machine (RSVM), where the kernel is not depended on all data, it is calculated by a recursive method, the SVM is also recursive. So we can realize on-line identification via SVM. Two examples are proposed to compare our RSVM with normal SVM

关键词： Support vector machines Nonlinear systems Neural networks Support vector machine classification Kernel Nonlinear control systems Control systems Convergence backpropagation algorithms Noise robustness

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Behavioral Modeling of Wideband RF PowerAmplifiers Using Complex-valued Wavelet Networks

Behavioral Modeling of Wideband RF PowerAmplifiers Using Com...

引用

2006 International Conference on Communications,Circuits and Systems(第四届国际通信、电路与系统学术会议)

作者： Zhe Jin Zhihuan Song Jiaming He College of Info Science and Engineering Zhejiang University Hangzhou China Institute of Communication Technologies Ningbo University Ningbo China

RF power amplifiers are important sources of nonlinearities in communication systems. Based on complexvalued wavelet networks, a novel behavioral model for wideband RF power amplifiers exhibiting memory effects is proposed as an improvement to existing feed-forward neural network models. The complex backpropagation algorithm is applied to training the network so as to extract the model parameters. The performance of the presented model is evaluated by a comparison with a feed-forward neural network model. The results in the time domain and frequency domain illustrate that the proposed behavioral model provides a faster convergence rate and more accurate approximation, when characterizing wideband RF power amplifiers.

关键词： Radio frequency Broadband amplifiers Power amplifiers Radiofrequency amplifiers Power system modeling Feedforward systems Neural networks Feedforward neural networks backpropagation algorithms Frequency domain analysis

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Accelerating the Multilayer Perceptron Learning with the Davidon Fletcher Powell Algorithm

Accelerating the Multilayer Perceptron Learning with the Dav...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： S. Abid A. Mouelhi F. Fnaiech ESSTT Tunis Tunisia

In this paper, the Davidon Fletcher Powell (DFP) algorithm for nonlinear least squares is proposed to train multilayer perceptron (MLP). Applied on both a single output layer perceptron and MLP, we find that this algorithm is faster than the Marquardt-Levenberg (ML) algorithm known as the fastest algorithm used to train MLP until now. The number of iterations required by DFP algorithm to converge is less than about 50% of what is required by the ML algorithm. Interpretations of these results are provided in the paper.

关键词： Acceleration Multilayer perceptrons Signal processing algorithms backpropagation algorithms Optimization methods Convergence Newton method Least squares methods Gradient methods Least squares approximation

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Evaluating Opinion filtered neural network trust model

Evaluating Opinion filtered neural network trust model

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IEEE Region 5 Annual Technical Conference

作者： Weihua Song Department of Technology and Computing Cameron University Lawton UK

This paper applies neural network training, validating and testing techniques in evaluating the performance of Opinion filtered neural network trust model (OFNN). OFNN model is used to filter heterogeneous trust recommendation systems and deceptive trust recommendations in P2P networks. The model is evaluated in terms of accuracy, convergence speed and reliability. Both simulation data and real data are applied in the evaluation. The results show that OFNN model has accuracy of 83.3% at a convergence speed of 17 training iterations on the real data. The model has accuracy of 93.75% with an average convergence speed of 4545 iterations based on the simulated trust systems in a P2P network.

关键词： Neural networks Convergence Bayesian methods Solids Computer networks Filters Social network services Availability System testing backpropagation algorithms

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FIR-MYRIAD Hybrid Filters for Signal Processing in Impulsive Noise Environments

FIR-MYRIAD Hybrid Filters for Signal Processing in Impulsive...

引用

International Conference on Communications and Networking in China (CHINACOM)

作者： Bin Lin ChengYu Zhang XuDong Wang Chen Gu Baisuo Wang Information Engineering College Dalian Maritime University Dalian Liaoning China

A new class of nonlinear filters called FIR-weighted myriad hybrid (FIR-WMyH) filters was introduced. These filters have a combination structure of linear FIR filters and weighted myriad filter, and can efficiently utilize the special strongpoint of two filters. Through the backpropagation algorithm used in the neural networks, we derive a stochastic gradient-based adaptive algorithm for determining optimal FIR-WMyH filters under the mean square error (MSE) criterion. In two impulsive noise models, the robust performance of this adaptive algorithm is demonstrated through the computer simulation results.

关键词： Signal processing Working environment noise Finite impulse response filter Nonlinear filters Adaptive algorithm Signal processing algorithms backpropagation algorithms Neural networks Stochastic resonance Adaptive filters

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Incipient Fault Diagnosis in Electrical Drives by Tuned Neural Networks

Incipient Fault Diagnosis in Electrical Drives by Tuned Neur...

引用

IEEE Instrumentation and Measurement Technology Conference

作者： A. Azzini L. Cristaldi M. Lazzaroni A. Monti F. Ponci A.G.B. Tettamanzi Dipartimento di Tecnologie dellE28099Informazione Università degli Studi di Milano Crema Italy Dipartimento di Elettrotecnica Politecnico di Milano Milan Italy Department of Electrical Engineering University of South Carolina Columbia SC USA

In order to identify any decrease in efficiency and any loss in industrial application a suitable monitoring system for processes is often required. With the proposed approach useful diagnostic indications can be obtained by a low-cost extension of the monitoring activity. In this way, the reliability of the obtained indications can be significantly increased considering the combination of advanced time-frequency transform, or time times scale, such as wavelets, and a new evolutionary optimisation approach based on artificial neural networks (ANNs). This paper describes an approach to the joint optimization of neural network structure and weights which can take advantage of the backpropagation algorithm as a specialized decoder. The presented approach has been successfully applied to a real-world machine fault diagnosis problem

关键词： Fault diagnosis Neural networks Artificial neural networks Monitoring Wavelet transforms backpropagation algorithms Decoding Instrumentation and measurement Evolutionary computation Pattern recognition

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Modified Fuzzy-CMAC Networks with Clustering-based Structure

Modified Fuzzy-CMAC Networks with Clustering-based Structure

引用

International Joint Conference on Neural Networks (IJCNN)

作者： G.S. Reis P.E.M. Almeida Serviço Nacional de Aprendizagem Industrial (SENAI/FIEMG) Sete Lagoas Brazil. e-mail: gsrjunior@fiemg.com.br Electrical Engineering Department CEFET-MG Brazil Electrical Engineering Department CEFET-MG

This work proposes a modified structure for the fuzzy-CMAC network to solve the curse of dimensionality problem, observed when systems with a high number of involved variables are being modeled with the aid of computational intelligence techniques. The approach is based on the fuzzy C-means clustering algorithm, which is used here to initialize the CMAC fuzzy input partitions. Also, the number of fuzzy-CMAC internal memories is drastically decreased by using the clusters defined by the fuzzy C-means algorithm. During network training phase, those fuzzy input partitions are adjusted together with the output layer weights of the network by means of the backpropagation algorithm. To assess the effectiveness of the modified fuzzy-CMAC network structure, the thermal modeling of a real world tube laminating process with nine input variables was accomplished. Obtained results are considered satisfactory when compared with those of classical artificial neural networks algorithms like an adaptive neuro-fuzzy inference system (ANFIS) network and a multilayer perceptron (MLP) network.

关键词： Clustering algorithms backpropagation algorithms Partitioning algorithms Multilayer perceptrons Vectors Neurons Intelligent systems Intelligent robots Computational intelligence Input variables

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Fast Incremental Learning Algorithm using Evolutionary Logic Networks for Real-Value Inputs

Fast Incremental Learning Algorithm using Evolutionary Logic...

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International Joint Conference on Neural Networks (IJCNN)

作者： Myoung Soo Park Jin Young Choi School of Electrical Engineering and Computcr Scinece Seoul National University Seoul South Korea

In this paper, we propose a new incremental learning algorithm which uses a new type of experience to reduce the computation time without the aid of addition information. The data structure, evolutionary logic networks for real-valued inputs (ELN-R) for storing and using this experience is defined and an incremental learning algorithm for ELN-R is described. The performance of the proposed learning algorithm is tested through experiments on two-spiral problem which is selected as a benchmark problem to compare the performance of the proposed algorithm with those of other algorithms.

关键词： Logic Stability backpropagation algorithms Data mining Computer science Neural networks Computer networks Data structures Benchmark testing Costs

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Identification of Nonlinear Dynamical Systems using A Higher Order Multi-Layer Neural Networks

Identification of Nonlinear Dynamical Systems using A Higher...

引用

International Conference on Signal Processing Proceedings (ICSP)

作者： Jiancheng Liu Xuping Tan Electronic Depar. Guangdong Agriculture-Industry-Business Polytechnic College Guangzhou China

A new neural network architecture, call a higher order multi-layer neural networks (HOMLNN) is presented. The architecture of an HOMLNN is a modified model of the evolved functional neural network (EFNN)with a hidden layer which is composed of self-evolve neurons and additional multiplication inputs between conventional inputs and self-evolve neurons. The authors drive a generalized dynamic backpropagation algorithm and show a new approach to the identification of dynamical systems by means of HOMLNN. Experiment result showed that the method is effective for the identification of dynamical systems

关键词： Nonlinear dynamical systems Multi-layer neural network Neural networks Recurrent neural networks System identification Neurons Artificial neural networks Feedforward neural networks backpropagation algorithms Feedback loop

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