An Extended Sliding Mode Learning Algorithm for Type-2 Fuzzy Neural Networks

被引：0
|
作者：
Shiev, Kostadin [1 ]
Shakev, Nikola [1 ]
Topalov, Andon V. [1 ]
Ahmed, Sevil [1 ]
Kaynak, Okyay [2 ]
机构：
[1] Control Syst Dept, TU Sofia Campus Plovdiv,25 Tsanko Dustabanov Str, Plovdiv 4000, Bulgaria
[2] Bogazici Univ, Dept Elect & Elect Engn, Istanbul, Turkey
来源：
ADAPTIVE AND INTELLIGENT SYSTEMS | 2011年 / 6943卷
关键词：
type-2 fuzzy logic systems; artificial neural networks; variable structure systems; sliding mode control; LOGIC; UNCERTAINTY;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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.
引用
收藏
页码：52 / +
页数：3
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