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dc.contributor.authorWang, Jingen_US
dc.contributor.authorWang, Chi-Hsuen_US
dc.contributor.authorChen, C. L. Philipen_US
dc.date.accessioned2017-04-21T06:50:09Z-
dc.date.available2017-04-21T06:50:09Z-
dc.date.issued2011en_US
dc.identifier.isbn978-1-4577-0653-0en_US
dc.identifier.issn1062-922Xen_US
dc.identifier.urihttp://hdl.handle.net/11536/134815-
dc.description.abstractIn this paper, Fuzzy Neural Network (FNN) is first transformed into an equivalent fully connected three layer neural network, or FFNN. Based on the FFNN, BP training algorithm is derived to tune both the premise and consequent part of FNN. Illustrative examples are presented to check the validity of the proposed theory and algorithms. Simulation achieves satisfactory results. Developing BP training algorithm for FNN via its equivalent FFNN has its emerging values in all engineering applications using FNN, such as intelligent adaptive control, pattern recognition, and signal processing..., etcen_US
dc.language.isoen_USen_US
dc.subjectNeural Networksen_US
dc.subjectFuzzy Logicen_US
dc.subjectFuzzy Neural Networksen_US
dc.subjectGradient Descenten_US
dc.subjectBack Propagationsen_US
dc.titleOn the BP Training Algorithm of Fuzzy Neural Networks (FNNs) via Its Equivalent Fully Connected Neural Networks (FFNNs)en_US
dc.typeProceedings Paperen_US
dc.identifier.journal2011 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC)en_US
dc.citation.spage1376en_US
dc.citation.epage1381en_US
dc.contributor.department電子工程學系及電子研究所zh_TW
dc.contributor.departmentDepartment of Electronics Engineering and Institute of Electronicsen_US
dc.identifier.wosnumberWOS:000298615101098en_US
dc.citation.woscount1en_US
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