Identification of nonlinear systems based on adaptive fuzzy systems embedding Quasi-ARMAX model

This paper addresses the issues related to identification of nonlinear systems based on Adaptive Fuzzy Systems (AFSs) Embedding Quasi-ARMAX Model. A general nonlinear system can be represented as a Quasi-ARMAX form, in which the parameters ā_i and b̄_i contain two parts: linear part and nonlinear part. By introducing and Adaptive Fuzzy System (AFS) to the nonlinear part of each parameter, we propose an AFSs Embedding Quasi-ARMAX Model for identification of nonlinear systems. Since the Quasi-ARMAX model consists of a combination of AFSs and ARMAX model, a priori information about system structure besides input-output data can be incorporated into the identification. Simulation results show that the proposed Quasi-ARMAX model has better performance than Neural Networks (NN) model.