Data-driven fuzzy modeling for nonlinear dynamic system

In this paper, A new method for dynamic learning of Takagi-Sugeno (T-S) model based on input-output data is presented. It is based on a novel learning algorithm that recursively updates T-S model structure and parameters by combining supervised and unsupervised learning. The rule-base and parameters of the T-S model continually evolve by adding new rules with more summarization power and by modifying existing rules and parameters. To reduce the complexity of fuzzy models while keeping good model accuracy, orthogonal least squares (OLS) method algorithm is used to remove redundant fuzzy rules, at the same time the consequent parameters of the T-S model are identified and optimized. The approach has been successfully applied to T-S models of non-linear dynamical system modeling.