A LEARNING PROCEDURE TO IDENTIFY WEIGHTED RULES BY NEURAL NETWORKS

被引：10

作者：

BLANCO, A

DELGADO, M

REQUENA, I

机构：

[1] Departamento de Ciencias de la Computación e Inteligencia Artificial, la Universidad de Granada, Facultad de Ciencias

来源：

FUZZY SETS AND SYSTEMS | 1995年 / 69卷 / 01期

关键词：

NEURAL NETWORK; WEIGHT OF RULE;

D O I：

10.1016/0165-0114(94)00250-B

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

In many cases the identification of systems by means of fuzzy rules is given by taking these rules from a predetermined set of possible ones. In this case, the correct description of the system is to be given by a finite set of rules each with an associated weight which assesses its correctness or accuracy. Here we present a method to learn this consistence level or weight by a neural network. The design of this neural network as well as the features of the training models are discussed. The paper concludes with an example.

引用

页码：29 / 36

页数：8