An extended sliding mode learning algorithm for type-2 fuzzy neural networks

Authors:
Kostadin Shiev;Nikola Shakev;Andon V. Topalov;Sevil Ahmed;Okyay Kaynak
Affiliations:
Control Systems Department, TU - Sofia campus Plovdiv, Plovdiv, Bulgaria;Control Systems Department, TU - Sofia campus Plovdiv, Plovdiv, Bulgaria;Control Systems Department, TU - Sofia campus Plovdiv, Plovdiv, Bulgaria;Control Systems Department, TU - Sofia campus Plovdiv, Plovdiv, Bulgaria;Department of Electrical and Electronic Engineering, Bogazici University, Istanbul, Turkey
Venue:
ICAIS'11 Proceedings of the Second international conference on Adaptive and intelligent systems
Year:
2011

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Abstract

Type-2 fuzzy logic systems are an area of growing interest over the last years. The ability to model uncertainties in a better way than type-1 fuzzy logic systems increases their applicability. A new stable on-line learning algorithm for type-2 fuzzy neural networks is proposed in this paper. It can be considered as an extended version of the recently developed on-line learning approaches for type-2 fuzzy neural networks based on the Variable Structure System theory concepts. Simulation results from the identification of a nonlinear system with uncertainties have demonstrated the better performance of the proposed extended algorithm in comparison with the previously reported in the literature sliding mode learning algorithms for both type-1 and type-2 fuzzy neural structures.