title: Fuzzy polynomial neural networks: hybrid architectures of fuzzy modeling
abstract: We introduce a concept of fuzzy polynomial neural networks (FPNNs), a hybrid modeling architecture combining polynomial neural networks (PNNs) and fuzzy neural networks (FNNs). The development of the FPNNs dwells on the technologies of computational intelligence (CI), namely fuzzy sets, neural networks, and genetic algorithms. The structure of the FPNN results from a synergistic usage of FNN and PNN. FNNs contribute to the formation of the premise part of the rule-based structure of the FPNN. The consequence part of the FPNN is designed using PNNs. The structure of the PNN is not fixed in advance as it usually takes place in the case of conventional neural networks, but becomes organized dynamically to meet the required approximation error. We exploit a group method of data handling (GMDH) to produce this dynamic topology of the network. The performance of the FPNN is quantified through experimentation that exploits standard data already used in fuzzy modeling. The obtained experimental results reveal that the proposed networks exhibit high accuracy and generalization capabilities in comparison to other similar fuzzy models
keyphrases: fuzzy polynomial neural networks hybrid architectures fuzzy modeling computational intelligence fuzzy sets genetic algorithms group method of data handling GMDH dynamic topology highly nonlinear rule-based models fuzzy inference method learning standard backpropagation membership functions learning rates momentum coefficients genetic optimization