Using fuzzy partitions to create fuzzy systems from input-output data and set the initial weights in a fuzzy neural network

被引：53

作者：

Lin, YH

Cunningham, GA

Coggeshall, SV

机构：

[1] NEW MEXICO INST MIN & TECHNOL,DEPT ELECT ENGN,SOCORRO,NM 87801

[2] LOS ALAMOS NATL LAB,DIV APPL THEORET PHYS,LOS ALAMOS,NM 87545

来源：

IEEE TRANSACTIONS ON FUZZY SYSTEMS | 1997年 / 5卷 / 04期

关键词：

fuzzy control; fuzzy systems; neural networks;

D O I：

10.1109/91.649913

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We create a set of fuzzy rules to model a system from input-output data by dividing the input space into a set of subspaces using fuzzy partitions, We create a fuzzy rule for each subspace as the input space is being divided, These rules are combined to produce a fuzzy rule based model from the input-output data, If more accuracy is required, we use the fuzzy rule-based model to determine the structure and set the initial weights in a fuzzy neural network, This network typically trains in a few hundred iterations, Our method is simple, easy, and reliable and it has worked well when modeling large ''real world'' systems.

引用

页码：614 / 621

页数：8