A TS-Type Maximizing-Discriminability-Based Recurrent Fuzzy Network for Classification Problems

This work proposes a Takagi-Sugeno (TS)-type maximizing-discriminability-based recurrent fuzzy network (MDRFN) that can classify highly confusable patterns. The discriminative capability plays a significant role in determining classification performance. To increase the discriminative capability, th...

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Veröffentlicht in:	IEEE transactions on fuzzy systems 2011-04, Vol.19 (2), p.339-352
Hauptverfasser:	Wu, Gin-Der, Zhu, Zhen-Wei, Huang, Pang-Hsuan
Format:	Artikel
Sprache:	eng
Schlagworte:	Artificial neural networks Classification Discriminability Firing gradient-descent method Input variables minimum classification error (MCE) minimum training error (MTE) Principal component analysis recurrent fuzzy network (RFN) singleton type Studies Support vector machine classification Training TS type
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