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dc.contributor.authorC. Treesatayapunen_US
dc.date.accessioned2018-09-10T03:40:44Z-
dc.date.available2018-09-10T03:40:44Z-
dc.date.issued2008-03-01en_US
dc.identifier.issn15684946en_US
dc.identifier.other2-s2.0-37249049900en_US
dc.identifier.other10.1016/j.asoc.2007.03.014en_US
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=37249049900&origin=inwarden_US
dc.identifier.urihttp://cmuir.cmu.ac.th/jspui/handle/6653943832/60301-
dc.description.abstractIn this paper, the discrete-time nonlinear systems identification and control based on an adaptive filter are introduced. This adaptive filter is implemented using the adaptive network called Multi Input Fuzzy Rules Emulated Network (MiFren). Inspired by the neuro-fuzzy network, the structure of MiFren resembles the human knowledge in the form of fuzzy If-Then rules. The initial value of MiFren's parameters can be easily selected based on the human knowledge. Then the on-line adaptive process is performed to fine tune these parameters, the convergence of the adaptive process is proven by using Lyapunov-theory-based Adaptive Filtering (LAF). In the control system application, MiFren is applied to control various selected nonlinear systems together with the proposed control law. Computer simulation results indicate that the proposed controller is able to control the target systems satisfactory. © 2007 Elsevier B.V. All rights reserved.en_US
dc.subjectComputer Scienceen_US
dc.titleFuzzy rules emulated network and its application on nonlinear control systemsen_US
dc.typeJournalen_US
article.title.sourcetitleApplied Soft Computing Journalen_US
article.volume8en_US
article.stream.affiliationsCentro de Investigacion y de Estudios Avanzadosen_US
article.stream.affiliationsChiang Mai Universityen_US
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