Abstract
To design a Mamdani fuzzy system with good generalization ability in high dimensional feature space,a novel learning algorithm based on Least Squares Support Vector Regression(LSSVR)was presented in this paper.The structural risk was considered in the goal function to avoid overfitting in traditional algorithms and then the parameter estimation of a Mamdani fuzzy system was converted to a quadratic optimization problem.In the proposed algorithm,the fuzzy kernel generated by premise membership functions is proved to be a mercer kernel.Numerical experiments show that the presented algorithm improves the approximation ability and the generalization ability of Mamdani fuzzy systems.
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.
