Genetic Optimization of Fuzzy Polynomial Neural Networks

In this paper, we introduce a new topology of fuzzy-neural networks-fuzzy-set-based polynomial neural networks (FSPNNs). The two underlying design mechanisms of such networks involve genetic optimization and information granulation (IG). The resulting constructs come in the form of fuzzy polynomial neural networks with fuzzy-set-based polynomial neurons, regarded as their generic processing elements. First, we introduce a comprehensive design methodology using which we determine the optimal structure of the FSPNNs. This methodology hinges on the extended group method of data handling and fuzzy-set-based rules. It concerns the optimization of the FSPNN-related parameters such as the number of input variables, the order of the polynomial, the number of membership functions, and a collection of a specific subset of input variables realized through the mechanism of genetic optimization. Second, the fuzzy rules used in the networks exploit a notion of information granules defined over a system's variables and formed through the process of IG. This granulation is realized with the aid of the hard C-means clustering algorithm. The performance of the network is quantified through experimentation in which we use a number of modeling benchmarks already experimented with in the realm of fuzzy or neurofuzzy modeling.