標題: Soft-Boosted Self-Constructing Neural Fuzzy Inference Network
作者: Prasad, Mukesh
Lin, Chin-Teng
Li, Dong-Lin
Hong, Chao-Tien
Ding, Wei-Ping
Chang, Jyh-Yeong
資訊工程學系
電機工程學系
腦科學研究中心
Department of Computer Science
Department of Electrical and Computer Engineering
Brain Research Center
關鍵字: Fuzzy neural network;online learning system;parameter learning;soft boost;structure learning
公開日期: Mar-2017
摘要: This correspondence paper proposes an improved version of the self-constructing neural fuzzy inference network (SONFIN), called soft-boosted SONFIN (SB-SONFIN). The design softly boosts the learning process of the SONFIN in order to decrease the error rate and enhance the learning speed. The SB-SONFIN boosts the learning power of the SONFIN by taking into account the numbers of fuzzy rules and initial weights which are two important parameters of the SONFIN, SB-SONFIN advances the learning process by: 1) initializing the weights with the width of the fuzzy sets rather than just with random values and 2) improving the parameter learning rates with the number of learned fuzzy rules. The effectiveness of the proposed soft boosting scheme is validated on several real world and benchmark datasets. The experimental results show that the SB-SONFIN possesses the capability to outperform other known methods on various datasets.
URI: http://dx.doi.org/10.1109/TSMC.2015.2507139
http://hdl.handle.net/11536/133148
ISSN: 2168-2216
DOI: 10.1109/TSMC.2015.2507139
期刊: IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
Volume: 47
Issue: 3
起始頁: 584
結束頁: 588
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