Interval type-2 recurrent fuzzy neural system desing via stable simultaneous perturbation stochastic approximation algorithm

This paper proposes a new type fuzzy neural systems, denotes IT2RFNS-A (interval type-2 recurrent fuzzy neural system with asymmetric membership function), for nonlinear systems control. To enhance the performance and approximation ability, the TSK-type consequent part is adopted for IT2RFNS-A. The gradient information of the IT2RFNS-A is not easy to obtain due to the asymmetric membership functions and interval valued sets. The corresponding stable learning is derived by simultaneous perturbation stochastic approximation (SPSA) algorithm which guarantees the convergence and stability of the closed-loop systems. Simulation and comparison on the control of Chua´s chaotic circuit is done to show the feasibility and effectiveness of proposed method.