TUNING OF FUZZY MODELS BY FUZZY NEURAL NETWORKS

被引：17

作者：

LEE, KM ^{[1
]}

KWAK, DH ^{[1
]}

LEEKWANG, H ^{[1
]}

机构：

[1] SAMSUNG ELECTR CO LTD,DIV SYST,SEOUL,SOUTH KOREA

来源：

FUZZY SETS AND SYSTEMS | 1995年 / 76卷 / 01期

关键词：

FUZZY NEURAL NETWORK; FUZZY MODELING; FUZZY INFERENCE;

D O I：

10.1016/0165-0114(95)00027-I

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

It is relatively easy to construct a rough fuzzy model with expert knowledge. It is difficult, however, to fine-tune the parameters of the fuzzy model in order to get improved behavior. For the purpose of tackling this problem, we propose a fuzzy neural network model. The proposed model utilizes a prior expert knowledge for target systems, and embodies fuzzy models which consist of fuzzy rules whose antecedent and consequent are fuzzy sets. The model is equipped with a fuzzy inferencing and tuning mechanism for model parameters by learning. It allows us to tune such parameters of fuzzy models as linguistic terms and relative rule importance. In addition, to show its applicability, we perform some experiments and present the results.

引用

页码：47 / 61

页数：15

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