Neuro-fuzzy systems for diagnosis

作     者：Ayoubi, M Isermann, R 

作者机构：Darmstadt University of Technology Institute of Automatic Control Laboratory of Control Engineering and Process Automation Landgraf-Georg 4 64283 Darmstadt Germany 

出 版 物：《FUZZY SETS AND SYSTEMS》 (模糊集与系)

年 卷 期：1997年第89卷第3期

页      面：289-307页

核心收录：

学科分类：07[理学] 0714[理学-统计学（可授理学、经济学学位）] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 070101[理学-基础数学] 

主　　题：neuro-fuzzy systems fault detection and diagnosis automatic rule extraction 

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.