Rule generation for hierarchical fuzzy systems

被引：14

作者：

Holve, R

机构：

来源：

1997 ANNUAL MEETING OF THE NORTH AMERICAN FUZZY INFORMATION PROCESSING SOCIETY - NAFIPS | 1997年

关键词：

D O I：

10.1109/NAFIPS.1997.624082

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 a new method of rule generation for hierarchical fuzzy systems (Hierarchical Fuzzy Associative Memory, HIFAM) is described. A HIFAM is structured as a binary tree and overcomes the exponential growth of the rulebases when the number of inputs increases. The training algorithm for HIFAM is suited for approximation and classification problems. Several benchmarks demonstrate that the proposed method compares well with existing learning techniques like artificial neural networks or decision trees.

引用

页码：444 / 449

页数：6