Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
A clustering algorithm for fuzzy model identification
Fuzzy Sets and Systems
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
Fuzzy function approximation with ellipsoidal rules
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Genetically optimized fuzzy polynomial neural networks
IEEE Transactions on Fuzzy Systems
A new approach to fuzzy-neural system modeling
IEEE Transactions on Fuzzy Systems
Comparison of adaptive methods for function estimation from samples
IEEE Transactions on Neural Networks
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In this paper, we introduce a new architecture of genetically dynamic optimization based Fuzzy Polynomial Neural Networks (gdFPNN) and discuss its comprehensive design methodology involving mechanisms of genetic optimization, especially genetic algorithms (GAs). The proposed gdFPNN gives rise to a structurally and parametrically optimized network through an optimal parameters design available within FPN. Through the consecutive process of such structural and parametric optimization, an optimized and flexible gdFPNN is generated in a dynamic fashion. The performance of the proposed gdFPNN is quantified through experimentation that exploits standard data already used in fuzzy modeling. These results reveal superiority of the proposed networks over the existing fuzzy and neural models.