文章基本信息

标题：Comparison of T-Norms and S-Norms for Interval Type-2 Fuzzy Numbers in Weight Adjustment for Neural Networks
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作者：Fernando Gaxiola ; Patricia Melin ; Fevrier Valdez 等
期刊名称：Information
电子版ISSN：2078-2489
出版年度：2017
卷号：8
期号：3
页码：114-134
DOI：10.3390/info8030114
出版社：MDPI Publishing
摘要：A comparison of different T-norms and S-norms for interval type-2 fuzzy number weights is proposed in this work. The interval type-2 fuzzy number weights are used in a neural network with an interval backpropagation learning enhanced method for weight adjustment. Results of experiments and a comparative research between traditional neural networks and the neural network with interval type-2 fuzzy number weights with different T-norms and S-norms are presented to demonstrate the benefits of the proposed approach. In this research, the definitions of the lower and upper interval type-2 fuzzy numbers with random initial values are presented; this interval represents the footprint of uncertainty (FOU). The proposed work is based on recent works that have considered the adaptation of weights using type-2 fuzzy numbers. To confirm the efficiency of the proposed method, a case of data prediction is applied, in particular for the Mackey-Glass time series (for τ = 17). Noise of Gaussian type was applied to the testing data of the Mackey-Glass time series to demonstrate that the neural network using a interval type-2 fuzzy numbers method achieves a lower susceptibility to noise than other methods.
关键词：fuzzy numbers; type-2 fuzzy weights; neural networks; backpropagation; time series prediction fuzzy numbers ; type-2 fuzzy weights ; neural networks ; backpropagation ; time series prediction