A New Hybrid Model Based On Neuro Fuzzy Network Soft Switching Mechanism For System Identification

This paper aims to improve a new hybrid model for system identification area. The proposed hybrid model consists of an adaptive Hammerstein model, an adaptive Wiener model, and a Neuro-Fuzzy (NF) network based soft-switching mechanism (SSM). SSM structure in hybrid model increases the success of block model by selecting the best results of Hammerstein and Wiener model outputs. In literature, there are various studies about NF based on Hammerstein or Wiener model types applied to system identification. In the proposed model, Hammerstein and Wiener models with NF network are used together different from the literature. In simulation studies, five different type of systems are identified with different models (Hammerstein, Wiener and the proposed hybrid model) optimized by Recursive Least Square (RLS). Then the performances of these models are compared. Simulation results reveal the effectiveness and robustness of the proposed identification model