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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.issued2010
dc.identifier.issn1045-9227
dc.identifier.issn1941-0093
dc.identifier.urihttps://dx.doi.org/10.1109/TNN.2010.2066285
dc.identifier.urihttps://hdl.handle.net/11421/19952
dc.descriptionWOS: 000283369400007en_US
dc.descriptionPubMed ID: 20813638en_US
dc.description.abstractThis paper presents fuzzy wavelet neural network (FWNN) models for prediction and identification of nonlinear dynamical systems. The proposed FWNN models are obtained from the traditional Takagi-Sugeno-Kang fuzzy system by replacing the THEN part of fuzzy rules with wavelet basis functions that have the ability to localize both in time and frequency domains. The first and last model use summation and multiplication of dilated and translated versions of single-dimensional wavelet basis functions, respectively, and in the second model, THEN parts of the rules consist of radial function of wavelets. Gaussian type of activation functions are used in IF part of the fuzzy rules. A fast gradient-based training algorithm, i.e., the Broyden-Fletcher- Goldfarb-Shanno method, is used to find the optimal values for unknown parameters of the FWNN models. Simulation examples are also given to compare the effectiveness of the models with the other known methods in the literature. According to simulation results, we see that the proposed FWNN models have impressive generalization ability.en_US
dc.language.isoengen_US
dc.publisherIEEE-Inst Electrical Electronics Engineers Incen_US
dc.relation.isversionof10.1109/TNN.2010.2066285en_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectFuzzy Wavelet Neural Networksen_US
dc.subjectSystem Identificationen_US
dc.subjectTime-Series Predictionen_US
dc.subjectWaveleten_US
dc.subjectWavelet Neural Networksen_US
dc.titleFuzzy Wavelet Neural Network Models for Prediction and Identification of Dynamical Systemsen_US
dc.typearticleen_US
dc.relation.journalIEEE Transactions On Neural Networksen_US
dc.contributor.departmentAnadolu Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.identifier.volume21en_US
dc.identifier.issue10en_US
dc.identifier.startpage1599en_US
dc.identifier.endpage1609en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.contributor.institutionauthorYılmaz, Sevcan
dc.contributor.institutionauthorOysal, Yusuf


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