Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/15319
Title: Nonlinear system identification using optimized dynamic neural network
Authors: Xie, WF
Zhu, YQ
Zhao, ZY
Wong, YK
Keywords: Artificial neural network
Genetic algorithm
Nonlinear system identification
On-line adaptation
Issue Date: 2009
Publisher: Elsevier
Source: Neurocomputing, 2009, v. 72, no. 13-15, p. 3277-3287 How to cite?
Journal: Neurocomputing 
Abstract: In this paper, both off-line architecture optimization and on-line adaptation have been developed for a dynamic neural network (DNN) in nonlinear system identification. In the off-line architecture optimization, a new effective encoding scheme-Direct Matrix Mapping Encoding (DMME) method is proposed to represent the structure of neural network by establishing connection matrices. A series of GA operations are applied to the connection matrices to find the optimal number of neurons on each hidden layer and interconnection between two neighboring layers of DNN. The hybrid training is adopted to evolve the architecture, and to tune the weights and input delays of DNN by combining GA with the modified adaptation laws. The modified adaptation laws are subsequently used to tune the input time delays, weights and linear parameters in the optimized DNN-based model in on-line nonlinear system identification. The effectiveness of the architecture optimization and adaptation is extensively tested by means of two nonlinear system identification examples.
URI: http://hdl.handle.net/10397/15319
ISSN: 0925-2312
EISSN: 1872-8286
DOI: 10.1016/j.neucom.2009.02.004
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