Self-Organizing Hybrid Neurofuzzy Networks

We introduce a concept of self-organizing Hybrid Neurofuzzy Networks (HNFN), a hybrid modeling architecture combining neurofuzzy (NF) and polynomial neural networks(PNN). The development of the Self-organizing HNFN dwells on the technologies of Computational Intelligence (CI), namely fuzzy sets, neural networks, and genetic algorithms. The architecture of the Self-organizing HNFN results from a synergistic usage of NF and PNN. NF contribute to the formation of the premise part of the rule-based structure of the Self-organizing HNFN. The consequence part of the Self-organizing HNFN is designed using Self-organizing PNN. We also distinguish between two types of the Self-organizing HNFN architecture showing how this taxonomy depends on connection points. Owing to the specific features of two combined architectures, it is possible to consider the nonlinear characteristics of process and to get output performance with superb predictive ability. The performance of the Self-organizing HNFN is quantified through experimentation that exploits standard data already used in fuzzy modeling. These results reveal superiority of the proposed networks over the existing fuzzy models. 1 Introductory Remarks With the continuously growing demand for models for complex systems inherently associated with nonlinearity, high-order dynamics, time-varying behavior, and imprecise measurements there is a need for a relevant modeling environment. Efficient modeling techniques should allow for a selection of pertinent variables and a formation of highly representative datasets. The models should be able to take advantage of the existing domain knowledge (such as a prior experience of human observers or operators) and augment it by available numeric data to form a coherent dataknowledge modeling entity. The omnipresent modeling tendency is the one that exploits techniques of CI by embracing fuzzy modeling, neurocomputing, and genetic optimization. In this study, we develop a hybrid modeling architecture, called Selforganizing Hybrid Neurofuzzy Networks (HNFN). In a nutshell, Self-organizing HNFN is composed of two main substructures, namely a neurofuzzy (NF) and a polynomial neural network(PNN). From a standpoint of rule-based architectures, one can