A Hybrid Modeling Architecture in Software Engineering

S.-K. Oh (Korea), W. Pedrycz (Poland/Canada), B.-J. Park and T.-C. Ahn (Korea)


Self-organizing neuro fuzzy networks(SONFN),neuro fuzzy networks(NFN), polynomial neural networks(PNN), computational intelligence(CI), genetic algorithms(GAs), design methodology, software costestimation.


In this study, we introduce a concept of Self-organizing neurofuzzy networks(SONFN), a hybrid modeling architecture combining neurofuzzy networks(NFN) and polynomial neural networks(PNN). We discuss their comprehensive design methodology. The architecture of the SONFN results from a synergistic usage of neural fuzzy networks (NFN) and polynomial neural networks (PNN). We discuss two kinds of SONFN architectures and propose a comprehensive learning algorithm. It is shown that this network exhibits a dynamic structure. The experimental results include well-known software data such as the NASA dataset concerning software cost estimation and the one describing software modules of the Medical Imaging System(MIS).

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