Genetically optimized hybrid fuzzy polynomial neural networks based on polynomial and fuzzy polynomial neurons

We investigate a new category of fuzzy-neural networks-Hybrid Fuzzy Polynomial Neural Networks (HFPNN). These networks consist of genetically optimized multi-layer with two kinds of heterogeneous neurons that are fuzzy set based polynominal neurons (FSPNs) and polynomial neurons (M). The augmented genetically optimized HFPNN (namely gHFPNN) results in a structurally optimized structure and comes with a higher level of flexibility in comparison to the one we encounter in the conventional HFPNN. The GA-based design procedure being applied at each layer of gHFPNN leads to the selection leads to the selection of preferred nodes (FSPNs or PNs) available within the HFPNN. The performance of the gHFPNN is quantified through experimentation using a benchmarking dataset-synthetic and experimental data already experimented with in fuzzy or neurofuzzy modeling.