WAVELET NEURO-FUZZY MODEL WITH HYBRID LEARNING ALGORITHM OF GRADIENT DESCENT AND GENETIC ALGORITHM

被引：4

作者：

Banakar, Ahmad ^{[1
]}

Azeem, Mohammad Fazle ^{[2
]}

机构：

[1] Univ Tarbiat Modarres, Dept Agr Machinery, Fac Agr, Tehran, Iran

[2] PA Coll Engn, Dept Elect & Commun Engn, Mangalore 574153, Karnataka, India

来源：

INTERNATIONAL JOURNAL OF WAVELETS MULTIRESOLUTION AND INFORMATION PROCESSING | 2011年 / 9卷 / 02期

关键词：

Activation network; wavelet network; Neuro-Fuzzy model; genetic algorithm; gradient descent; NETWORK; IDENTIFICATION; SYSTEMS;

D O I：

10.1142/S021969131100402X

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

In this paper, a Wavelet Neuro-Fuzzy model has been proposed. The proposed work caters an application of wavelet network used in fuzzy systems for forecasting of dynamic systems. A wavelet network approximates the consequent part of each fuzzy rule. The wavelet network is a feed-forward neural network with one hidden layer that uses a combination of Wavelet and Sigmoid Activation Function. A hybrid learning method composed of genetic algorithm and gradient descent is proposed to tune the learning parameters of the proposed Wavelet Neuro-Fuzzy model. Further, an analysis regarding the convergence and stability of gradient descent learning is presented for the proposed Wavelet Neuro-Fuzzy model. To evaluate the effectiveness of proposed model and learning strategy, three different classes of benchmark problems have been considered.

引用

页码：333 / 359

页数：27

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