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2012 (Vol. 4, Issue: 09)
Article Information:

A Comparison between Neural Networks and Wavelet Networks in Nonlinear System Identification

Hamed Khodadadi, S. Ehsan Razavi and Hossein Ahmadi-Noubari
Corresponding Author:  Hamed Khodadadi 

Key words:  Nonlinear identification, nonlinear ARX model, orthogonal least squares, radial basis function, wavelet network, ,
Vol. 4 , (09): 1021-1026
Submitted Accepted Published
October 23, 2011 December 09, 2011 May 01, 2012

In this study, identification of a nonlinear function will be presented by neural network and wavelet network methods. Behavior of a nonlinear system can be identified by intelligent methods. Two groups of the most common and at the same time the most effective of neural networks methods are multilayer perceptron and radial basis function that will be used for nonlinear system identification. The selected structure is series - parallel method that after network training by a series of training random data, the output is estimated and the nonlinear function is compared to a sinusoidal input. Then, wavelet network is used for identification and we will use Orthogonal Least Squares (OLS) method for wavelet selection to reduce the volume of calculations and increase the convergence speed.
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  Cite this Reference:
Hamed Khodadadi, S. Ehsan Razavi and Hossein Ahmadi-Noubari, 2012. A Comparison between Neural Networks and Wavelet Networks in Nonlinear System Identification.  Research Journal of Applied Sciences, Engineering and Technology, 4(09): 1021-1026.
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ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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