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dc.contributor.authorYılmaz, Sevcan
dc.contributor.authorOysal, Yusuf
dc.date.accessioned2019-10-21T20:10:54Z
dc.date.available2019-10-21T20:10:54Z
dc.date.issued2015
dc.identifier.isbn978-1-4799-8547-0
dc.identifier.urihttps://hdl.handle.net/11421/19950
dc.descriptionProceedings of the Science and Information Conference (SAI) -- JUL 28-30, 2015 -- Science and Information Organization, London, ENGLANDen_US
dc.descriptionWOS: 000380448800059en_US
dc.description.abstractThis paper introduces the architecture and learning procedure of dynamic fuzzy system (DFS) and its control application with linear quadratic regulator (LQR). Our DFS model is a Takagi-Sugeno type fuzzy system. IF parts of the rules are Gaussian type membership functions and THEN parts of the rules are differential equations with linear functions of inputs. We give bioreactor modeling and control results in order to show efficiency of the proposed model.en_US
dc.description.sponsorshipNvidia, IEEE, The Future & Emerging Technol FET at the European Comm, EUREKA, Cambridge Wireless, British Comp Soc, Digital Catapult, Springer, Media Partner for this conferenceen_US
dc.language.isoengen_US
dc.publisherIEEEen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectAnfisen_US
dc.subjectDynamic Adaptive Neuro-Fuzzy Inference Systemen_US
dc.subjectSystem Modelingen_US
dc.titleDynamic Fuzzy System Design for Modeling and Control of Nonlinear Dynamical Processesen_US
dc.typeconferenceObjecten_US
dc.relation.journal2015 Science and Information Conference (Sai)en_US
dc.contributor.departmentAnadolu Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.identifier.startpage463en_US
dc.identifier.endpage467en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.contributor.institutionauthorYılmaz, Sevcan
dc.contributor.institutionauthorOysal, Yusuf


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