Science & Engineering Research Support soCiety (SERSC)
In this paper, the authors introduce a design of fuzzy neural networks based on scatter space for nonlinear modeling. To design the networks, they partition the input space in the scatter form using Fuzzy C-Means (FCM) clustering algorithm which generates the fuzzy rules in the premise part of the proposed networks. The partitioned spaces express the fuzzy rules of the networks. Through this method, they are able to handle the high dimension problem. The consequence part of the rule is represented by polynomial functions whose coefficients are learned by standard back-propagation algorithm.