document: Fuzzy polynomial neural networks : hybrid architectures of fuzzy modeling We introduce a concept of fuzzy polynomial neural networks -LRB- FPNNs -RRB- , a hybrid modeling architecture combining polynomial neural networks -LRB- PNNs -RRB- and fuzzy neural networks -LRB- FNNs -RRB- . The development of the FPNNs dwells on the technologies of computational intelligence -LRB- CI -RRB- , 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 -LRB- GMDH -RRB- 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
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