Soft-Boosted Self-Constructing Neural Fuzzy Inference Network

Mukesh Prasad, Chin-Teng Lin, Dong Lin Li, Chao-Ting Hong, Wei-Ping Ding, Jyh-Yeong Chang

Research output: Contribution to journalArticlepeer-review

19 Scopus citations


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.
Original languageEnglish
Pages (from-to)584-588
Number of pages5
JournalIEEE Transactions on Systems, Man, and Cybernetics: Systems
Issue number3
StatePublished - Mar 2017


  • Fuzzy neural network; online learning system; parameter learning; soft boost; structure learning

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