Neural Network with Lower and Upper Type-2 Fuzzy Weights using the Backpropagation Learning Method

被引：0
|
作者：
Gaxiola, Fernando [1 ]
Melin, Patricia [1 ]
Valdez, Fevrier [1 ]
机构：
[1] Tijuana Inst Technol, Tijuana, Mexico
来源：
PROCEEDINGS OF THE 2013 JOINT IFSA WORLD CONGRESS AND NAFIPS ANNUAL MEETING (IFSA/NAFIPS) | 2013年
关键词：
Neural Networks; Type-2 Fuzzy Weights; Backpropagation Algorithm; Type-2 fuzzy system; SYSTEMS; ALGORITHM; LOGIC; OPTIMIZATION; DESIGN; SPEED;
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 lower and upper type-2 fuzzy weight adjustment applied in a neural network performing the learning method is proposed. The mathematical representation of the adaptation of the interval type-2 fuzzy weights and the proposed learning method architecture are presented. This research is based in the analysis of the recent methods that manage weight adaptation and implementing this analysis in the adaptation of these methods with type-2 fuzzy weights. In this paper, we work with type-2 fuzzy weights lower and upper in the neural network architecture and the lower and upper final results obtained are presented in the final. The proposed approach is applied to a case of Mackey-Glass time series prediction.
引用
收藏
页码：637 / 642
页数：6
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