Evolutionary fuzzy models for nonlinear identification

This paper proposes a new method for identification problems for industrial applications based on a Takagi-Sugeno (T-S) fuzzy model. The learning of the T-S model is performed from input/output data to approximate unknown nonlinear processes by a coevolationary genetic algorithm (GA). The proposed method is an automatic tool since it does not require any prior knowledge concerning the structure (e.g. the number of rules) and the database (e.g. antecedent fuzzy sets) of the T-S fuzzy model, and concerning the selection of the adequate input variables and their respective time delays. The proposed methodology is able to design all the parts of the T-S fuzzy prediction model and it is composed by five hierarchical levels. To validate and demonstrate the performance and effectiveness of the proposed algorithm, it is applied on Box-Jenkins benchmark problem.