Abstract
In this paper, we propose a architecture of Genetic Algorithms (GAs)-based Polynomial Neural Networks(PNN), discuss a comprehensive design methodology and carry out a series of numeric experiments. GA-based design procedure at each stage (layer) of PNN leads to the selection of preferred nodes (or PNs) with optimal parameters (such as the number of input variables, input variables, and the order of the polynomial) available within PNN. A detailed design procedure is discussed in detail. To evaluate the performance of the GA-based PNN, the model is experimented with by using Medical Imaging System (MIS) data for application to Multi-variable software process. A comparative analysis shows that the proposed GA-based PNN is model with higher accuracy as well as more superb predictive capability than other intelligent models presented previously.
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
Cherkassky, V., Gehring, D., Mulier, F.: Comparison of adaptive methods for function estimation from samples. IEEE Trans. Neural Networks. 7, 969–984 (1996)
Dicherson, J.A., Kosko, B.: Fuzzy function approximation with ellipsoidal rules. IEEE Trans. on Systems, Man and Cybernetics, Part B 26, 542–560 (1996)
Oh, S.-K., Pedrycz, W.: The design of self-organizing Polynomial Neural Networks. Information Science 141, 237–258 (2002)
Oh, S.-K., Pedrycz, W., Park, B.-J.: Polynomial Neural Networks Architecture: Analysis and Design. Computers and Electrical Engineering 29(6), 703–725 (2003)
Lyu, M.R. (ed.): Handbook of Software Reliability Engineering. McGraw-Hill and IEEE Computer Society Press, New York and Washington (1995)
Park, H.-S., Park, B.-J., Kim, H.-K., Oh, S.-K.: Self-Organizing Polynomial Neural Networks Based on Genetically Optimized Multi-Layer Perceptron Architecture. International Journal of Control, Automation, and Systems 2(4), 423–434 (2004)
Goldberg, D.E.: Genetic Algorithm in search, Optimization & Machine Learning. Addison wesley, Boston (1989)
Kenneth, A., De, J.: Are Genetic Algorithms Function Optimizers? In: Manner, R., Manderick, B. (eds.) Parallel Problem Solving from Nature 2, North-Holland, Amsterdam (1992)
Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs, 3rd edn. Springer, Heidelberg (1996)
Oh, S.-K., Pedrycz, W.: Identification of Fuzzy Systems by means of an Auto-Tuning Algorithm and Its Application to Nonlinear Systems. Fuzzy Sets and Systems 115(2), 205–230 (2000)
Oh, S.-K., Pedrycz, W., Park, B.-J.: Relation-based Neurofuzzy Networks with Evolutionary Data Granulation. Methematical and Computer Modeling 40(7-8), 891–921 (2004)
Oh, S.-K., Pedrycz, W.: Fuzzy Polynomial Neuron-Based Self-Organizing Neural Networks. Int. J. of General Systems. 32(3), 237–250 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Oh, SK., Pedrycz, W., Kim, WS., Kim, HK. (2006). GA-Based Polynomial Neural Networks Architecture and Its Application to Multi-variable Software Process. In: Yang, Q., Webb, G. (eds) PRICAI 2006: Trends in Artificial Intelligence. PRICAI 2006. Lecture Notes in Computer Science(), vol 4099. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-36668-3_90
Download citation
DOI: https://doi.org/10.1007/978-3-540-36668-3_90
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-36667-6
Online ISBN: 978-3-540-36668-3
eBook Packages: Computer ScienceComputer Science (R0)