Paper Info
Title
Interval Type-2 Recurrent Fuzzy Neural System Desing Via Stable Simultaneous Perturbation Stochastic Approximation Algorithm
Abstract
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.
Year
DOI
Venue
2011
10.1109/FUZZY.2011.6007489
IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ 2011)
Keywords
Field
DocType
Nonlinear systems, fuzzy neural system, type-2 fuzzy system, Lyapunov theorem, SPSA algorithm
Chua's circuit,Convergence (routing),Approximation algorithm,Nonlinear system,Simultaneous perturbation stochastic approximation,Control theory,Computer science,Algorithm,Stochastic process,Chaotic,Membership function
Conference
ISSN
Citations 
PageRank 
1098-7584
0
0.34
References 
Authors
28
2
Name
Order
Citations
PageRank
Feng-Yu Chang1412.10
Ching-Hung Lee259742.31