Robust modeling for nonlinear dynamic systems using a neurofuzzy approach with iterative optimization

Authors:
Shirong Liu;Simon X. Yang;Jinshou Yu
Affiliations:
Institute of Electrical Engineering and Automation, Ningbo University, Ningbo, Zhejiang, China;School of Engineering, University of Guelph, Guelph, Ontario, Canada;Research Institute of Automation, East China University of Science and Technology Shanghai, China
Venue:
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
Year:
2005

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Abstract

A neurofuzzy modeling approach for nonlinear dynamic systems is proposed in this paper. An iterative optimization approach for a class of neurofuzzy systems is developed, which integrates the model structure analysis and simplification, model parameter estimation, compatible cluster merging and redundant cluster deleting, performance evaluation for neurofuzzy models. The effectiveness of the proposed modeling approach is illustrated by the Mackey-Glass chaotic time series. The simulation studies show that the parsimonious neurofuzzy model is beneficial to the robustness of model.