Abstract
This paper presents an approach which is useful for the identification of discrete dynamic systems based on fuzzy relational models. If the number of input variables and fuzzy sets increases, a fuzzy system gets increasingly intractable. A concept based on the decomposition of multivariable rule-base is presented. Two decomposed fuzzy models based on the simplified inference break up method are proposed and applied to a dynamic systems modelling. Evolution of identification algorithms for the decomposed fuzzy model is suggested. A comparative study of the dynamic system identification with the conventional relational model and the decomposed relational model is presented for Box-Jenkins data.
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.
