Generating consistent fuzzy belief rule base from sample data

Jun Liu, Luis Martinez, Da Ruan, Hui Wang, Özgür Kabak

    Research outputpeer-review


    A fuzzy rule-based evidential reasoning approach and it corresponding optimization algorithm have been proposed recently, where a fuzzy rule-base with a belief structure, called a fuzzy belief rule base (FBRB), forms a basis in the inference mechanism. In this paper, a new learning method for optimally generating a consistent FBRB based on the given data is proposed. The main focus is given on the consistency of FBRB knowing that the consistency conditions are often violated if the system is generated from real world data. The measurement of inconsistency of FBRB is provided and finally is incorporated in the objective function of the optimization algorithm. This process is formulated as a nonlinear constraint optimization problem and solved using the optimization tool provided in MATLAB. A numerical example is provided to demonstrate the effectiveness of the proposed algorithm.
    Original languageEnglish
    Title of host publicationIntelligent Decision Making Systems
    Place of PublicationSingapore, Singapore
    StatePublished - Nov 2009
    EventThe 4th Int. ISKE Conf. on Intelligent Decision Making Systems - Hasselt
    Duration: 27 Nov 200928 Nov 2009

    Publication series

    NameComputer Engineering and Information Science


    ConferenceThe 4th Int. ISKE Conf. on Intelligent Decision Making Systems

    Cite this