SECURING INTERPRETABILITY OF FUZZY MODELS FOR MODELING NONLINEAR MIMO SYSTEMS USING A HYBRID OF EVOLUTIONARY ALGORITHMS

In this study, a Multi-Objective Genetic Algorithm is utilized to extract interpretable and compact fuzzy rule bases for modeling nonlinear Multi-input Multi-output (MIMO) systems. In the process of nonlinear system identification, structure selection, parameter estimation, model performance and model validation are important objectives. Furthermore, securing low-level and high-level interpretability requirements of fuzzy models is especially a complicated task in case of modeling nonlinear MIMO systems. Due to these multiple and conicting objectives, MOGA is applied to yield a set of candidates as compact, transparent and valid fuzzy models. Also, MOGA is combined with a powerful search algorithm namely Differential Evolution (DE). In the proposed algorithm, MOGA performs the task of membership function tuning as well as rule base identification simultaneously while DE is utilized only for linear parameter identification. Practical applicability of the proposed algorithm is examined by two nonlinear system modeling problems used in the literature. The results obtained show the effectiveness of the proposed method.