Neuro-fuzzy systems for diagnosis

被引：47

作者：

Ayoubi, M

Isermann, R

机构：

[1] Darmstadt University of Technology, Institute of Automatic Control, Laboratory of Control Engineering and Process Automation, 64283 Darmstadt

来源：

FUZZY SETS AND SYSTEMS | 1997年 / 89卷 / 03期

关键词：

neuro-fuzzy systems; fault detection and diagnosis; automatic rule extraction;

D O I：

10.1016/S0165-0114(97)00011-0

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Knowledge-based fault detection and diagnosis is described from the analytic and heuristic symptom generation to diagnostic reasoning. The extension of the knowledge-based approach by adaptive neural networks allows us to tune the knowledge base in order to investigate undetermined parameters just as membership functions, relevance weights of antecedents and priority factors of rules. An overview of design methodologies of neuro-fuzzy systems is provided with a special focus on a hybrid neuro-fuzzy network with a neural logical operator. Finally, an application of the neuro-fuzzy system to the on-line monitoring of air pressure in vehicle wheels is described. (C) 1997 Elsevier Science B.V.

引用

页码：289 / 307

页数：19

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