A Functional-Link based Interval Type-2 Compensatory Fuzzy Neural Network for Nonlinear System modeling

被引：0
|
作者：
Chang, Jyh-Yeong [1 ]
Lin, Yang-Yin [1 ]
Han, Ming-Feng [1 ]
Lin, Chin-Teng [1 ]
机构：
[1] Natl Chiao Tung Univ, Dept Elect & Control Engn, Hsinchu, Taiwan
来源：
IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ 2011) | 2011年
关键词：
type-2 fuzzy systems; compensatory operation; structure learning; on-line fuzzy clustering; SUPPORT-VECTOR REGRESSION; SETS;
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：
In this paper, the Functional-Link based Interval Type-2 Compensatory Fuzzy Neural Network (FLIT2CFNN) is a six-layer structure, which combines compensatory fuzzy reasoning method, and the consequent part is combined the proposed functional-link neural network with interval weights. The compensatory fuzzy reasoning method uses adaptive fuzzy operations of neuro-fuzzy systems that can make the fuzzy logic system more adaptive and effective. Initially, there is no rule in the FLIT2CFNN. A FLIT2CFNN is constructed using concurrent structure and parameter learning. The advantages of this learning algorithm are that it converges quickly and the obtained fuzzy rules are more precise. All of the antecedent part parameters and compensatory degree values are learned by gradient descent algorithm. Several simulation results show that the FLIT2CFNN achieves better performance than other feedforword type-1 and type-2 FNNs.
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页码：939 / 943
页数：5
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