Generating consistent fuzzy belief rule base from sample data

Research output: Contribution to report/book/conference proceedingsIn-proceedings paperpeer-review


  • Jun Liu
  • Luis Martinez
  • Da Ruan
  • Hui Wang

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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
Publication statusPublished - Nov 2009
EventThe 4th Int. ISKE Conf. on Intelligent Decision Making Systems - Hasselt, Belgium
Duration: 27 Nov 200928 Nov 2009

Publication series

NameComputer Engineering and Information Science


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


  • belief structure, fuzzy logic, fuzzy belief rule base, optimization

ID: 158237