Optimization of a network with Gaussian kernel functions based on genetic algorithm

被引：0

作者：

Kil, RM ^{[1
]}

Song, YS ^{[1
]}

机构：

[1] Korea Adv Inst Sci & Technol, Sch Nat Sci, Yusong Gu, Taejon 305701, South Korea

来源：

PROGRESS IN CONNECTIONIST-BASED INFORMATION SYSTEMS, VOLS 1 AND 2 | 1998年

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：

This paper presents a new way of optimization based on genetic operators. It has been known that the canonical genetic algorithm (CGA) does not guarantee convergence to the global optimum and also has the problem of premature saturation (or convergence). In this sense, this paper suggests a new way of searching based on genetic operators in which convergence to the global optimum is guaranteed and the efficiency in searching is considered. To show the effectiveness of our approach, simulation for the learning of a network with Gaussian kernel functions for the prediction of Mackey-Glass chaotic time series has been performed.

引用

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页码：291 / 295

页数：5

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