Rule-Based Fuzzy Polynomial Neural Networks in Modeling Software Process Data
Byoung‐Jun Park, Dongyoon Lee, Sung‐Kwun Oh · 2003
Abstract: Experimental software datasets describing software projects in terms of their com-plexity and development time have been the subject of intensive modeling. A number of various modeling methodologies and modeling designs have been proposed including such approaches as neural networks, fuzzy, and fuzzy neural network models. In this study, we introduce the con-cept of the Rule-based fuzzy polynomial neural networks (RFPNN) as a hybrid modeling archi-tecture and discuss its comprehensive design methodology. The development of the RFPNN dwells on the technologies of Computational Intelligence (CI), namely fuzzy sets, neural net-works, and genetic algorithms. The architecture of the RFPNN results from a synergistic usage of RFNN and PNN. RFNN contribute to the formation of the premise part of the rule-based structure of the RFPNN. The consequence part of the RFPNN is designed using PNN. We dis-cuss two kinds of RFPNN architectures and propose a comprehensive learning algorithm. In particular, 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 estima-tion and the one describing software modules of the Medical Imaging System (MIS).