Systems identification using type-2 fuzzy neural network (Type-2 FNN) systems

被引：0
|
作者：
Lee, CH [1 ]
Lin, YC [1 ]
Lai, WY [1 ]
机构：
[1] Yuan Ze Univ, Dept Elect Engn, Taoyuan 320, Taiwan
来源：
2003 IEEE INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE IN ROBOTICS AND AUTOMATION, VOLS I-III, PROCEEDINGS | 2003年
关键词：
fuzzy neural network; type-2 fuzzy sets; back-propagation algorithm;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper presents a type-2 fuzzy neural network system (type-2 FNN) and its learning algorithm using back-propagation algorithm. In our previous results, the FNN system using type-1 fuzzy logic systems (FLSs) is called type-1 FNN system. It has the properties of parallel computation scheme, easy to implement, fuzzy logic inference system, and parameters convergence. For considering the fuzzy rules uncertainties, we use the type-2 FLSs to develop a type-2 FNN system. The type-2 fuzzy sets let us model and minimize the effects of uncertainties in rule-base fuzzy logic systems (FLSs). In this paper, the previous results of type-1 FNN are extended to a type-2 one. In addition, the corresponding learning algorithm is derived by back-program algorithm. Several examples are presented to illustrate the effectiveness of our approach.
引用
收藏
页码：1264 / 1269
页数：6
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