As a guest user you are not logged in or recognized by your IP address. You have
access to the Front Matter, Abstracts, Author Index, Subject Index and the full
text of Open Access publications.
This paper proposes a modified algorithm in Takagi-Sugeno (T-S) fuzzy modelling for a complex nonlinear system. First, the Gaussian kernel based fuzzy c-mean clustering method is presented to find a scalar offset of the linear function. Secondly, the fuzzy c-regression model (FCRM) and the weighted recursive least square (WRLS) algorithm are adopted to calculate the parameters of the linear model according to individual input variables. Finally, the proposed algorithm stops if the objective function is minimized, otherwise, returns to the first step. Two simulation examples are provided to demonstrate the effectiveness of the proposed T-S fuzzy modelling approach.
This website uses cookies
We use cookies to provide you with the best possible experience. They also allow us to analyze user behavior in order to constantly improve the website for you. Info about the privacy policy of IOS Press.
This website uses cookies
We use cookies to provide you with the best possible experience. They also allow us to analyze user behavior in order to constantly improve the website for you. Info about the privacy policy of IOS Press.