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 ofthat is designed using genetic PNN. © Springer-Verlag Berlin Heidelberg 2005.
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CITATION STYLE
Oh, S. K., Park, B. J., Pedrycz, W., & Kim, H. K. (2005). Genetically optimized hybrid fuzzy neural networks in modeling software data. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3558 LNAI, pp. 338–345). Springer Verlag. https://doi.org/10.1007/11526018_33
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