A probabilistic neural-fuzzy learning system for stochastic modeling

Han-Xiong Li, Zhi Liu

    Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

    97 Citations (Scopus)

    Abstract

    A probabilistic fuzzy neural network (PFNN) with a hybrid learning mechanism is proposed to handle complex stochastic uncertainties. Fuzzy logic systems (FLSs) are well known for vagueness processing. Embedded with the probabilistic method, an FLS will possess the capability to capture stochastic uncertainties. Further enhanced with the neural learning, it will be able to work under time-varying stochastic environment. Integrated with a statistical process control (SPC) based monitoring method, the PFNN can maintain the robust modeling performance. Finally, the successful simulation demonstrates the modeling effectiveness of the proposed PFNN under the time-varying stochastic conditions. © 2008 IEEE.
    Original languageEnglish
    Pages (from-to)898-908
    JournalIEEE Transactions on Fuzzy Systems
    Volume16
    Issue number4
    DOIs
    Publication statusPublished - 2008

    Research Keywords

    • Intelligent learning
    • Probabilistic fuzzy logic system (PFLS)
    • Probabilistic fuzzy neural networks (PFNNs)
    • Statistical process control (SPC)
    • Stochastic modeling

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