Fuzzy-valued Evolution Strategy for Evolving Neural Networks with Fuzzy Weights and Biases

被引：0
|
作者：
Okada, Hidehiko [1 ]
Yamashita, Akira [1 ]
Matsuse, Takashi [1 ]
Wada, Tetsuya [1 ]
机构：
[1] Kyoto Sangyo Univ, Kita Ku, Kyoto 6038555, Japan
来源：
6TH INTERNATIONAL CONFERENCE ON SOFT COMPUTING AND INTELLIGENT SYSTEMS, AND THE 13TH INTERNATIONAL SYMPOSIUM ON ADVANCED INTELLIGENT SYSTEMS | 2012年
关键词：
evolutionary algorithms; evolution strategy; neural network; neuroevolution;
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, we propose an extension of evolution strategy (ES) for evolving fuzzy-valued neural networks (FNNs). In the proposed ES, values in the genotypes are not real numbers but fuzzy values. We apply our fuzzy-valued ES (FES) to the approximate modeling of fuzzy functions with FNNs. Experimental results showed that an FNN trained by our FES could approximate a hidden test function to a certain extent, despite t that the learning was not supervised.
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页码：277 / 280
页数：4
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