Abstract
In an earlier paper (1996), we proposed a set of generalized defuzzification strategies which can be characterized as single-mode-oriented strategies. A single-mode-oriented defuzzification strategy, although useful in many research projects and real world applications, cannot be applied to a multimode situation where two or more distinct possibility peaks exist in its membership function distribution. In this paper, for multimode-oriented generalized defuzzification applications, a multimode-oriented polynomial transformation based defuzzification strategy (M-PTD) is introduced. The new M-PTD strategy, which uses the Kalman filter in parameter learning procedure, offers a constraint-free and self-renewal defuzzification solution.
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 callMore From: IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)
Disclaimer: 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.
