Abstract
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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
