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The design of TSK‐type fuzzy controllers using a new hybrid learning approach
Author(s) -
Lin ChengJian,
Xu YongJi
Publication year - 2006
Publication title -
international journal of adaptive control and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.73
H-Index - 66
eISSN - 1099-1115
pISSN - 0890-6327
DOI - 10.1002/acs.882
Subject(s) - fuzzy logic , controller (irrigation) , computer science , cluster analysis , mutation , fuzzy control system , population , artificial intelligence , control theory (sociology) , control (management) , biology , biochemistry , demography , sociology , agronomy , gene
In this paper, the TSK‐type fuzzy controllers with a novel hybrid learning approach (TFC‐HLA) are proposed. The proposed hybrid learning approach consists of the self‐clustering algorithm (SCA) and the dynamic‐form symbiotic evolution (DSE). First, the proposed SCA is used to identify a parsimonious internal structure. Secondly, the proposed DSE uses the sequential‐search‐based dynamic evolution method. Better combinations of the chromosomes will be initially generated while better mutation points will be determined to perform dynamic mutation. The advantages of the proposed learning algorithm are that, first, it converges quickly and the obtained fuzzy rules are more precise. Second, the proposed TFC‐HLA controller only requires a few population sizes. Third, the DSE constructs only four fuzzy controllers every generation. The performance of the TFC‐HLA controller compares excellently with other existing models in our control simulations. Copyright © 2005 John Wiley & Sons, Ltd.