Genetically optimized self-organizing fuzzy polynomial neural networks based on information granulation

In this study, we introduce and investigate a genetically optimized self-organizing fuzzy polynomial neural network with the aid of information granulation (IG―gSOFPNN), develop a comprehensive design methodology involving mechanisms of genetic optimization. With the aid of the information granulation, we determine the initial location (apexes) of membership functions and initial values of polynomial function being used in the premised and consequence part of the fuzzy rules respectively. The GA-based design procedure being applied at each layer of IG―gSOFPNN leads to the selection of preferred nodes with specific local characteristics (such as the number of input variables, the order of the polynomial, a collection of the specific subset of input variables, and the number of membership function) available within the network.