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

2011 ◽  
Author(s):  
Feng-Yu Chang ◽  
Ching-Hung Lee
Keyword(s):  
Approximation Algorithm ◽  
Stochastic Approximation ◽  
Neural System ◽  
Simultaneous Perturbation Stochastic Approximation ◽  
Stochastic Approximation Algorithm ◽  
Fuzzy Neural ◽  
Interval Type

scholarly journals Interval Type-2 Recurrent Fuzzy Neural System for Nonlinear Systems Control Using Stable Simultaneous Perturbation Stochastic Approximation Algorithm

2011 ◽  
Vol 2011 ◽  
pp. 1-21 ◽  
Author(s):  
Ching-Hung Lee ◽  
Feng-Yu Chang
Keyword(s):  
Nonlinear Systems ◽  
Membership Function ◽  
Stochastic Approximation ◽  
Neural System ◽  
Fuzzy Membership Function ◽  
Simultaneous Perturbation Stochastic Approximation ◽  
Comparison Results ◽  
Fuzzy Neural ◽  
Interval Type

This paper proposes a new type fuzzy neural systems, denoted IT2RFNS-A (interval type-2 recurrent fuzzy neural system with asymmetric membership function), for nonlinear systems identification and control. To enhance the performance and approximation ability, the triangular asymmetric fuzzy membership function (AFMF) and TSK-type consequent part are 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 results for the chaotic system identification and the control of Chua's chaotic circuit are shown to illustrate the feasibility and effectiveness of the proposed method.

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