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Dynamic T‐S Fuzzy Systems Identification Based on Sparse Regularization
Author(s) -
Luo Minnan,
Sun Fuchun,
Liu Huaping
Publication year - 2015
Publication title -
asian journal of control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.769
H-Index - 53
eISSN - 1934-6093
pISSN - 1561-8625
DOI - 10.1002/asjc.890
Subject(s) - fuzzy logic , regularization (linguistics) , mathematics , neuro fuzzy , fuzzy set operations , fuzzy clustering , fuzzy control system , system identification , mathematical optimization , adaptive neuro fuzzy inference system , cluster analysis , fuzzy associative matrix , algorithm , computer science , artificial intelligence , data mining , measure (data warehouse)
Fuzzy system identification suffers from rules explosion, i.e. , a large number of fuzzy rules are required for fuzzy systems with high dimension input variable. In this paper, a dynamic algorithm is proposed to address T‐S fuzzy system identification with both sparsity and dynamic clustering, named as dynamic sparse fuzzy inference systems ( D ‐sparse FIS ). Due to two different estimation approaches of fuzzy rule consequence parameter, i.e. , global estimation and local estimation, D ‐sparse FIS .local and D ‐sparse FIS .global methods are exploited with local least squares and global least squares estimation based on sparse regularization. Both two dynamic algorithms can guarantee a minimal number of fuzzy rules and nonzero consequence parameters are equipped in T‐S fuzzy system. Finally, some numerical experiments are presented to illustrate the effectiveness of the proposed algorithms.