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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Pedrycz, W.: Computational Intelligence. CRC Press, FL (1998)
Pedrycz, W., Peters, J.F.: Computational Intelligence and Software Engineering. World Scientific, Singapore (1998)
Oh, S.K., Pedrycz, W., Park, H.S.: Hybrid Identification in Fuzzy-Neural Networks. Fuzzy Sets and Systems 138(2), 399–426 (2003)
Oh, S.K., Pedrycz, W., Park, B.J.: Polynomial Neural Networks Architecture: Analysis and Design. Computers and Electrical Engineering 29(6), 653–725 (2003)
Ivahnenko, A.G.: The group method of data handling: a rival of method of stochastic approximation. Soviet Automatic Control 13(3), 43–55 (1968)
Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs. Springer, Heidelberg (1996)
Shin, M., Goel, A.G.: Empirical Data Modeling in Software Engineering Using Radial Basis Functions. IEEE Trans on Software Engineering 26(6), 567–576 (2000)
Oh, S.K., Pedrycz, W., Park, B.J.: Self-organizing neurofuzzy networks in modeling software data. Fuzzy set and systems 145(1), 165–181 (2004)
Oh, S.K., Pedrycz, W., Park, B.J.: Relation-based Neurofuzzy Networks with Evolutionary Data Granulation. Mathematical and Computer Modelling 40(7-8), 891–921 (2004)
Bishop, C.M.: Neural Networks for Pattern Recognition. Oxford University Press, Oxford (1995)
Kearns, M., Ron, D.: Algorithmic Stability and Sanity-Check Bounds for Leave-One-Out Cross-Validation. In: Proc. 10th Ann. Conf. Computational Learning Theory, pp. 152–162 (1997)
Kemerer, C.F.: An Empirical Validation of Software Cost Estimation Models. Comm. ACM 30(5), 416–429 (1987)
Park, H.S., Oh, S.K.: Rule-based Fuzzy-Neural Networks Using the Identification Algorithm of the GA Hybrid Scheme. International journal of Control, Automations, and Systems 1(1), 101–110 (2003)
Park, H.S., Oh, S.K.: Multi-FNN Identification Based on HCM Clustering and Evolutionary Fuzzy Granulation. International Journal of Control, Automation and Systems 1(2), 194–202 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Oh, SK., Park, BJ., Pedrycz, W., Kim, HK. (2005). Genetically Optimized Hybrid Fuzzy Neural Networks in Modeling Software Data. In: Torra, V., Narukawa, Y., Miyamoto, S. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2005. Lecture Notes in Computer Science(), vol 3558. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11526018_33
Download citation
DOI: https://doi.org/10.1007/11526018_33
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-27871-9
Online ISBN: 978-3-540-31883-5
eBook Packages: Computer ScienceComputer Science (R0)