Genetically optimized hybrid fuzzy neural networks in modeling software data

Authors:
Sung-Kwun Oh;Byoung-Jun Park;Witold Pedrycz;Hyun-Ki Kim
Affiliations:
Department of Electrical Engineering, The University of Suwon, Hwaseong-si, Gyeonggi-do, South Korea;Department of Electrical, Electronic and Information Engineering, Wonkwang University, Chon-Buk, South Korea;Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada;Department of Electrical Engineering, The University of Suwon, Hwaseong-si, Gyeonggi-do, South Korea
Venue:
MDAI'05 Proceedings of the Second international conference on Modeling Decisions for Artificial Intelligence
Year:
2005

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Abstract

Experimental software data sets describing software projects in terms of their complexity and development time have been a subject of intensive modeling. In this study, a new architecture and comprehensive design methodology of genetically optimized Hybrid Fuzzy Neural Networks (gHFNN) are introduced and modeling software data is carried out. The gHFNN architecture results from a synergistic usage of the hybrid system generated by combining Fuzzy Neural Networks (FNN) with Polynomial Neural Networks (PNN). FNN contributes to the formation of the premise part of the overall network structure of the gHFNN. The consequence part of that is designed using genetic PNN.