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Optimization for a class of self-adaptation neuro-fuzzy models and its application to CSTR modeling

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3 Author(s)
Liu Shirong ; Dept. of Autom. & Comput. Technol., Ningbo Univ., China ; Yu Zheng ; Yu JinShou

We study the model structure and parameter optimization for Takagi-Sugeno type neuro-fuzzy models based on the statistical information criteria, matrix singular value decomposition, rule elimination method and rule merging methods. Some novel parameter self-adaptation algorithms are presented in this paper, which can be used to modify cluster center values, cluster radius values and parameters of consequent functions of the neuro-fuzzy models. The neuro-fuzzy model and methods presented have been successfully applied to modeling a continuous stirred tank reactor (CSTR)

Published in:

Intelligent Control and Automation, 2000. Proceedings of the 3rd World Congress on  (Volume:5 )

Date of Conference:

2000

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