An evolutionary fuzzy modeling approach for ANFIS architecture

This paper proposes a cooperative evolutionary method for optimizing the properties of an ANFIS-architecture-based model where only the input-output data of the identified system are available. The primary tasks of fuzzy modeling are structure identification and parameter optimization: the former determines the numbers of membership functions and fuzzy if-then rules while the latter identifies a feasible set of parameters under the given structure. The proposed approach manages all mentioned attributes simultaneously. Particularly, number of rules and parameters of membership functions are realized by applying a novel approach using genetic programming and genetic algorithm whereas consequent parameters are tuned by using least-squares estimation. Finally, two examples of nonlinear system are given to illustrate the effectiveness of the proposed approach.