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dc.contributor.authorDenai, Mouloud
dc.contributor.authorPalis, F.
dc.contributor.authorZeghbib, A.
dc.identifier.citationDenai , M , Palis , F & Zeghbib , A 2007 , ' Modeling and control of nonlinear systems using soft computing techniques ' , Applied Soft Computing , vol. 7 , no. 3 , pp. 728-738 .
dc.identifier.otherPURE: 2916129
dc.identifier.otherPURE UUID: cc8506ef-8c17-46d0-9c7d-45efb65df649
dc.identifier.otherScopus: 34047254534
dc.description.abstractThis work is an attempt to illustrate the utility and effectiveness of soft computing approaches in handling the modeling and control of complex systems. Soft computing research is concerned with the integration of artificial intelligent tools (neural networks, fuzzy technology, evolutionary algorithms, …) in a complementary hybrid framework for solving real world problems. There are several approaches to integrate neural networks and fuzzy logic to form a neuro-fuzzy system. The present work will concentrate on the pioneering neuro-fuzzy system, Adaptive Neuro-Fuzzy Inference System (ANFIS). ANFIS is first used to model non-linear knee-joint dynamics from recorded clinical data. The established model is then used to predict the behavior of the underlying system and for the design and evaluation of various intelligent control strategiesen
dc.relation.ispartofApplied Soft Computing
dc.titleModeling and control of nonlinear systems using soft computing techniquesen
dc.contributor.institutionSchool of Physics, Engineering & Computer Science
dc.contributor.institutionDepartment of Engineering and Technology
dc.contributor.institutionCentre for Engineering Research
dc.contributor.institutionCommunications and Intelligent Systems
dc.description.statusPeer reviewed
rioxxterms.typeJournal Article/Review

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