A novel structure which models the fuzzy inference mechanism based on neural units is proposed, to combine both the adaptive feature of neural networks and the transparency of fuzzy systems. It is shown how a perceptron with a sigmoidal activity function can perform the aggregation of premise antecedents and can thus implement conjunction or disjunction operations depending on the neuron's threshold. Knowledge-base parameters such as relevance weights of antecedents and priority weights of rules are introduced and discussed. The network topology is extracted by means of a coincidence learning law, the so-called Hebbian rule, in order to limit the problem of high dimensionality known by local classifiers. Two real-world problems are reported: monitoring of the state of a turbocharger of diesel engine on the basis of model-based symptoms, and the supervision of air pressure in vehicle wheels, based on physically extracted symptoms.


    Zugriff

    Zugriff über TIB

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Fuzzy systems design based on a hybrid neural structure and application to the fault diagnosis of technical processes


    Weitere Titelangaben:

    Neuro-Fuzzy-Klassifikation für die Fehlererkennung und deren Anwendung zur Prozeßüberwachung


    Beteiligte:
    Ayoubi, M. (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    1996


    Format / Umfang :

    8 Seiten, 13 Bilder, 19 Quellen




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Print


    Sprache :

    Englisch





    Fuzzy ARTMAP Neural Network and Its Application to Fault Diagnosis of Integrated Navigation Systems

    Zhang, H. Y. / Chan, C. W. / Cheung, K. C. et al. | British Library Conference Proceedings | 1999


    Modelbased fault diagnosis of technical systems.

    Isermann, R. | Online Contents | 1996


    Automated fault diagnosis using neuro-fuzzy systems

    Benkhedda, Hassen / Patton, Ron | AIAA | 1996