Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Wang, Jing | en_US |
dc.contributor.author | Wang, Chi-Hsu | en_US |
dc.contributor.author | Chen, C. L. Philip | en_US |
dc.date.accessioned | 2017-04-21T06:50:09Z | - |
dc.date.available | 2017-04-21T06:50:09Z | - |
dc.date.issued | 2011 | en_US |
dc.identifier.isbn | 978-1-4577-0653-0 | en_US |
dc.identifier.issn | 1062-922X | en_US |
dc.identifier.uri | http://hdl.handle.net/11536/134815 | - |
dc.description.abstract | In 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..., etc | en_US |
dc.language.iso | en_US | en_US |
dc.subject | Neural Networks | en_US |
dc.subject | Fuzzy Logic | en_US |
dc.subject | Fuzzy Neural Networks | en_US |
dc.subject | Gradient Descent | en_US |
dc.subject | Back Propagations | en_US |
dc.title | On the BP Training Algorithm of Fuzzy Neural Networks (FNNs) via Its Equivalent Fully Connected Neural Networks (FFNNs) | en_US |
dc.type | Proceedings Paper | en_US |
dc.identifier.journal | 2011 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC) | en_US |
dc.citation.spage | 1376 | en_US |
dc.citation.epage | 1381 | en_US |
dc.contributor.department | 電子工程學系及電子研究所 | zh_TW |
dc.contributor.department | Department of Electronics Engineering and Institute of Electronics | en_US |
dc.identifier.wosnumber | WOS:000298615101098 | en_US |
dc.citation.woscount | 1 | en_US |
Appears in Collections: | Conferences Paper |