Using genetic algorithms to extract rules from trained neural networks

被引：0

作者：

Keedwell, E ^{[1
]}

Narayanan, A ^{[1
]}

Savic, D ^{[1
]}

机构：

[1] Univ Exeter, Sch Engn & Comp Sci, Exeter EX4 4QF, Devon, England

来源：

GECCO-99: PROCEEDINGS OF THE GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE | 1999年

关键词：

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

This paper describes a navel method of extracting rules from trained artificial neural networks. The system which uses genetic algorithms to find an optimal path through the network for a classification. It is claimed that this system compares favourably to traditional rule extraction algorithms due to its ability to extract rules from very large and complex networks without an exponential increase-in computational complexity.

引用

页码：793 / 793

页数：1

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