Neuro-fuzzy systems for function approximation

被引：153

作者：

Nauck, D ^{[1
]}

Kruse, R ^{[1
]}

机构：

[1] Univ Magdeburg, Fac Comp Sci, D-39106 Magdeburg, Germany

来源：

FUZZY SETS AND SYSTEMS | 1999年 / 101卷 / 02期

关键词：

neuro-fuzzy system; function approximation; structure learning; parameter learning;

D O I：

10.1016/S0165-0114(98)00169-9

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

We present a neuro-fuzzy architecture for function approximation based on supervised learning. The learning algorithm is able to determine the structure and the parameters of a fuzzy system. The approach is an extension to our already published NEFCON and NEFCLASS models which are used for control or classification purposes. The proposed extended model, which we call NEFPROX, is more general and can be used for any application based on function approximation. (C) 1999 Elsevier Science B.V. All rights reserved.

引用

页码：261 / 271

页数：11

共 35 条

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