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Abstract
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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
Submitted: October 23, 2011
Accepted: December 09, 2011
Published: May 01, 2012 |
Abstract:
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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.
Key words: Nonlinear identification, nonlinear ARX model, orthogonal least squares, radial basis function, wavelet network, ,
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Abstract
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Cite this Reference:
Hamed Khodadadi, S. Ehsan Razavi and Hossein Ahmadi-Noubari, . A Comparison between Neural Networks and Wavelet Networks in Nonlinear System Identification. Research Journal of Applied Sciences, Engineering and Technology, (09): 1021-1026.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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