Dynamic T-S fuzzy systems identification based on sparse regularization

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-sparseFIS). Due to two different estimation approaches of fuzzy rule consequence parameter, i.e., global estimation and local estimation, D-sparseFIS.local and D-sparseFIS.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.