Genetic optimization-driven multi-layer hybrid fuzzy neural networks

In this study, we introduce a new architecture of hybrid fuzzy neural networks (gHFNNs) and offer a comprehensive design methodology that supports their development. The gHFNN rule-based architecture results from a synergistic usage of Fuzzy Neural Networks (FNNs) with Polynomial Neural Networks (PNNs). The FNN contributes to the formation of the premise part of the overall network of the gHFNN. The consequence part of the gHFNN is designed taking advantage of PNNs. The optimization of the FNN is realized with the aid of a standard back-propagation learning combined with genetic optimization. The development of the PNN dwells on the extended Group Method of Data Handling (GMDH) and Genetic Algorithms (GAs). Through the consecutive process of such structural and parametric optimization, an optimized topology of the PNN becomes generated in a dynamic fashion. The performance of the gHFNN is evaluated through a series of numeric experiments. A comparative analysis shows that the proposed gHFNN is characterized by higher accuracy as well as significant predictive capabilities when contrasted with other neurofuzzy models presented in the literature.