Time-variation nonlinear system identification based on Bayesian-Gaussian neural network

Authors:
Yijian Liu;Chen Peng
Affiliations:
School of Electrical and Automation Engineering, Nanjing Normal University, City of Nanjing, China;School of Electrical and Automation Engineering, Nanjing Normal University, City of Nanjing, China
Venue:
ICNC'09 Proceedings of the 5th international conference on Natural computation
Year:
2009

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Abstract

A Bayesian-Gaussian neural network (BGNN) method for nonlinear time variation system identification is proposed in this article. In the redefined BGNN training algorithms, the threshold matrix parameters are optimized by the swarm intelligence optimization algorithm(s) off-line and the sliding window data method are adopted for the BGNN on-line prediction. Some typical time-variation nonlinear systems are been used for the validation of the BGNN modeling effectiveness.