Fuzzy modelling and identification with genetic algorithm based learning

A GA based learning algorithm is proposed in this paper for the identification of TSK models. The algorithm consists of four blocks: Partition Block, GA Block, Tuning Block and Termination Block. The Partition Block is to determine an estimated partition of input variables. The GA Block is to optimi...

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Bibliographic Details
Published inFuzzy sets and systems Vol. 113; no. 3; pp. 351 - 365
Main Authors Wu, Baolin, Yu, Xinghuo
Format Journal Article
LanguageEnglish
Published Elsevier B.V 01.08.2000
Subjects
Artificial intelligence
Genetic algorithm
Modelling
System identification
Modelling
System identification
Genetic algorithm
Artificial intelligence
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ISSN0165-0114
1872-6801
DOI10.1016/S0165-0114(97)00408-9

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Summary:A GA based learning algorithm is proposed in this paper for the identification of TSK models. The algorithm consists of four blocks: Partition Block, GA Block, Tuning Block and Termination Block. The Partition Block is to determine an estimated partition of input variables. The GA Block is to optimise the structure of a TSK model. The Tuning Block is to fine tune the parameters of the TSK model using the gradient descent based approach and the Termination Block checks that the resultant TSK model is satisfactory. The proposed GABL algorithm has the advantage of simplicity, flexibility, high accuracy and automation. The presented numerical examples indicate that the GABL algorithm is effective in constructing a good TSK model for complex nonlinear systems.
ISSN:0165-0114
1872-6801
DOI:10.1016/S0165-0114(97)00408-9