A New Approach to Self-Organizing Hybrid Fuzzy Polynomial Neural Networks: Synthesis of Computational Intelligence Technologies

摘要

We introduce a new category of fuzzy-neural networks-Hybrid Fuzzy Polynomial Neural Networks (HFPNN). These networks are based on a genetically optimized multi-layer perceptron with fuzzy polynomial neurons (FPNs) and polynomial neurons (PNs). 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 HFPNN leads to the selection of preferred nodes (FPNs or PNs) available within the HFPNN. In the sequel, two general optimization mechanisms are explored. First, the structural optimization is realized via GAs whereas the ensuing detailed parametric optimization is carried out in the setting of a standard least square method-based learning.