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

Department of Agricultural Machinery, Faculty of Agriculture, University of Tarbiat Modarres, Tehran, Iran

and

Department of Electronics and Communication Engineering, PA College of Engineering, Nadu Padav, Monte Padav Post, Kairangala, Mangalore, 574 153, Karnataka, India

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https://doi.org/10.1142/S021969131100402XCited by:4 (Source: Crossref)

Abstract

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.

Keywords:

AMSC: 22E46, 53C35, 57S20

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