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.

引用

页码：277 / 280

页数：4

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