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: | 三月-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 |
顯示於類別: | 期刊論文 |