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    An evolvable self-organizing neuro-fuzzy multilayered classifier with group method data handling and grammar-based bio-inspired supervisors for fault diagnosis of hydraulic systems

    , Article International Journal of Intelligent Computing and Cybernetics ; Vol. 7, issue. 1 , 2014 , p. 38-78 Mozaffari, A ; Fathi, A ; Behzadipour, S ; Sharif University of Technology
    Abstract
    Purpose: The purpose of this paper is to apply a hybrid neuro-fuzzy paradigm called self-organizing neuro-fuzzy multilayered classifier (SONeFMUC) to classify the operating faults of a hydraulic system. The main motivation behind the use of SONeFMUC is to attest the capabilities of neuro-fuzzy classifier for handling the difficulties associated with fault diagnosis of hydraulic circuits. Design/methodology/approach: In the proposed methodology, first, the neuro-fuzzy nodes at each layer of the SONeFMUC are trained separately using two well-known bio-inspired algorithms, i.e. a semi deterministic method with random walks called co-variance matrix adaptation evolutionary strategy (CMA-ES) and...