Design of Rule-based Neurofuzzy Networks by means of genetic fuzzy set-based granulation

被引：0
|
作者：
Park, B [1 ]
Oh, S
机构：
[1] Wonkwang Univ, Sch Elect Elect & Informat Engn, Iri, Chollabuk Do, South Korea
[2] Univ Suwon, Dept Elect Engn, Hwaseong 445743, Gyeonggi Do, South Korea
来源：
ADVANCES IN NEURAL NETWORKS - ISNN 2005, PT 1, PROCEEDINGS | 2005年 / 3496卷
关键词：
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, new architectures and design methodologies of Rule based Neurofuzzy Networks (RNFN) are introduced and the dynamic search-based GAs is introduced to lead to rapidly optimal convergence over a limited region or a boundary condition. The proposed RNFN is based on the fuzzy set based neurofuzzy networks (NFN) with the extended structure of fuzzy rules being formed within the networks. In the consequence part of the fuzzy rules, three different forms of the regression polynomials such as constant, linear and modified quadratic are taken into consideration. The dynamic search-based GAs optimizes the structure and parameters of the RNFN.
引用
收藏
页码：422 / 427
页数：6
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