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Support vector machines based neuro-fuzzy control of nonlinear systems

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dc.contributor.author Iplikci, S.
dc.date.accessioned 2019-08-16T12:06:49Z
dc.date.available 2019-08-16T12:06:49Z
dc.date.issued 2010
dc.identifier.issn 09252312 (ISSN)
dc.identifier.uri http://acikerisim.pau.edu.tr:8080/xmlui/handle/11499/6388
dc.description.abstract In this work, a novel neuro-fuzzy control structure has been proposed for unknown nonlinear plants, which is referred to as the SVM-based ANFIS controller since it has been emerged from the fusion of adaptive network fuzzy inference system (ANFIS) and support vector machines (SVMs). In the proposed controller, an obtained SVM model of the plant is used to extract the gradient information and to predict the future behavior of the plant dynamics, which are necessary to find the additive correction term and to update the ANFIS parameters. The motivation behind the use of SVMs for modeling the plant dynamics is the fact that the SVM algorithms possess higher generalization ability and guarantee the global minima. The simulation results have revealed that the SVM-based ANFIS controller exhibits considerably high performance by yielding very small transient- and steady-state tracking errors and that it can maintain its performance under noisy conditions. © 2010 Elsevier B.V.
dc.language.iso English
dc.relation.isversionof 10.1016/j.neucom.2010.02.008
dc.subject Intelligent control
dc.subject Neuro-fuzzy systems
dc.subject Support vector machines
dc.subject Adaptive network fuzzy inference systems
dc.subject Generalization ability
dc.subject Global minima
dc.subject Gradient informations
dc.subject Neurofuzzy control
dc.subject Neurofuzzy system
dc.subject Nonlinear plant
dc.subject Plant dynamics
dc.subject Simulation result
dc.subject Steady state tracking
dc.subject SVM algorithm
dc.subject SVM model
dc.subject Adaptive control systems
dc.subject Controllers
dc.subject Fuzzy control
dc.subject Fuzzy inference
dc.subject Fuzzy systems
dc.subject Vectors
dc.subject article
dc.subject classification algorithm
dc.subject computer model
dc.subject computer simulation
dc.subject fuzzy system
dc.subject mathematical computing
dc.subject performance measurement system
dc.subject priority journal
dc.subject support vector machine
dc.subject system analysis
dc.title Support vector machines based neuro-fuzzy control of nonlinear systems
dc.type Article
dc.relation.journal Neurocomputing
dc.identifier.volume 73
dc.identifier.issue 10-12
dc.identifier.startpage 2097
dc.identifier.endpage 2107
dc.identifier.index Scopus

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