Backpropagation Learning Method with Interval Type-2 Fuzzy Weights in Neural Networks

被引：0
|
作者：
Gaxiola, Fernando [1 ]
Melin, Patricia [1 ]
Valdez, Fevrier [1 ]
Castillo, Oscar [1 ]
机构：
[1] Tijuana Inst Technol, Tijuana, Mexico
来源：
2013 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) | 2013年
关键词：
Neural Networks; Type-2 Fuzzy Weights; Backpropagation Algorithm; Type-2 fuzzy system; ALGORITHM; SYSTEMS; LOGIC; OPTIMIZATION; DESIGN;
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 a neural network learning method with lower and upper type-2 fuzzy weight adjustment is proposed. The general mathematical analysis of the proposed learning method architecture and the adaptation of the interval type-2 fuzzy weights are presented. The proposed method is based on research of recent methods that manage weight adaptation and especially type-2 fuzzy weights. In this paper the neural network architecture managing lower and upper type-2 fuzzy weights and the obtained lower and upper final results are presented. The proposed approach is applied to a case of Mackey-Glass time series prediction.
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页数：6
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