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
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
收藏
页码:333 / 359
页数:27
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