Design of Fuzzy Set-based Polynomial Neural Networks involving Information Granules with the aid of multi-population Genetic Algorithms

In this paper, we propose a new design methodology of fuzzy-neural networks - fuzzy set-based polynomial neural networks (FSPNN) with symbolic genetic algorithms. Fuzzy set-based polynomial neural networks (FSPNN) are based on a fuzzy set-based polynomial neuron (FSPN) whose fuzzy rules include the information granules (about the real system) obtained through information granulation. The information granules are capable of show the specific characteristic of the system. We have developed a design methodology (genetic optimization using symbolic genetic algorithms) to find the optimal structure for fuzzy-neural networks that expanded from group method of data handling (GMDH). It is the number of input variables, the order of the polynomial, the number of membership functions, and a collection of the specific subset of input variables that are the parameters of FSPNN fixed by aid of symbolic genetic optimization that has search capability to find the optimal solution on the solution space. The augmented and genetically developed FPNN (gFPNN) results in a structurally optimized structure and comes with a higher level of flexibility in comparison to the one we encounter in the conventional FPNNs. The GA-based design procedure being applied at each layer of FPNN leads to the selection of the most suitable nodes (or FSPNs) available within the FPNN. Symbolic genetic algorithms are capable of reducing the solution space more than conventional genetic algorithms with binary genetype chromosomes. The performance of genetically optimized FSPNN (gFSPNN) with aid of symbolic genetic algorithms is quantified through experimentation where we use a number of modeling benchmarks data which are already experimented with in fuzzy or neurofuzzy modeling