Fuzzy Sets and Systems - Special issue: fuzzy sets: where do we stand? Where do we go?
The design of self-organizing polynomial neural networks
Information Sciences—Informatics and Computer Science: An International Journal
Hybrid identification in fuzzy-neural networks
Fuzzy Sets and Systems - Theme: Learning and modeling
A novel self-organizing fuzzy polynomial neural networks with evolutionary FPNs: design and analysis
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Rule-based modeling: fast construction and optimal manipulation
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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In this study, we proposed genetically dynamic optimized Self-Organizing Fuzzy Polynomial Neural Networks (SOFPNN) with information granulation based Fuzzy Polynomial Neuron(FPN) (gdSOFPNN), develop a comprehensive design methodology involving mechanisms of genetic optimization. The proposed gdSOFPNN gives rise to a structurally and parametrically optimized network through an optimal parameters design available within the FPN (viz. the number of input variables, the order of the polynomial, input variables, the number of membership functions, and the apexes of membership function). Here, 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 performance of the proposed gdSOFPNN is quantified through experimentation that exploits standard data already used in fuzzy modeling.