Abstract
In this study, we proposed genetically dynamic optimized Self-Organizing Fuzzy Polynomial Neural Networks (SOFPNN) with information granulation based Fuzzy Polynomial Neuron(FPN) (gdSOFPNN), develop a comprehensive design methodology involving mechanisms of genetic optimization. The proposed gdSOFPNN gives rise to a structurally and parametrically optimized network through an optimal parameters design available within the FPN (viz. the number of input variables, the order of the polynomial, input variables, the number of membership functions, and the apexes of membership function). Here, with the aid of the information granulation, we determine the initial location (apexes) of membership functions and initial values of polynomial function being used in the premised and consequence part of the fuzzy rules respectively. The performance of the proposed gdSOFPNN is quantified through experimentation that exploits standard data already used in fuzzy modeling.
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
Nie, J.H., Lee, T.H.: Rule-based modeling: Fast construction and optimal manipulation. IEEE Trans. Syst., Man, Cybern. 26, 728–738 (1996)
Ivahnenko, A.G.: Polynomial theory of complex systems. IEEE Trans. on Systems, Man and Cybernetics. SMC-12, 364–378 (1971)
Ahn, T.C., Ryu, S.M.: Fuzzy PNN Algorithm and its Application to nonlinear Processes. Int. J. of General Systems 30-4, 463–478 (2001)
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, 703–725 (2003)
Oh, S.K., Pedrycz, W.: Fuzzy Polynomial Neuron-Based Self-Organizing Neural Networks. Int. J. of General Systems 32, 237–250 (2003)
Zadeh, L.A.: Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy sets and Systems 90, 111–117 (1997)
Jong, D.K.A.: Are Genetic Algorithms Function Optimizers? In: Manner, R., Manderick, B. (eds.) Parallel Problem Solving from Nature 2. North-Holland, Amsterdam (1992)
Vachtsevanos, G., Ramani, V., Hwang, T.W.: Prediction of gas turbine NOx emissions using polynomial neural network. Technical Report. Georgia Institute of Technology, Atlanta (1995)
Oh, S.K., Pedrycz, W., Park, H.S.: Hybrid identification in fuzzy-neural networks. Fuzzy Sets and Systems 138, 399–426 (2003)
Oh, S.K., Pedrycz, W., Park, H.S.: Multi-FNN identification based on HCM clustering and evolutionary fuzzy granulation. Simulation Modeling Practice and Theory 11, 627–642 (2003)
Oh, S.K., Pedrycz, W., Park, H.S.: Multi-layer Hybrid Fuzzy Polynomial Neural Networks: A Design in the Framework of Computational Intelligence. Neurocomputing 64, 397–431 (2005)
Park, H.S., Oh, S.K., Ahn, T.C.: A Novel Self-Organizing Fuzzy Polynomial Neural Networks with Evolutionary FPNs: Design and Analysis. In: Wang, J., Yi, Z., Żurada, J.M., Lu, B.-L., Yin, H. (eds.) ISNN 2006. LNCS, vol. 3971, pp. 780–785. Springer, Heidelberg (2006)
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
Ahn, TC., Park, HS. (2006). Evolutionary Design of gdSOFPNN for Modeling and Prediction of NOx Emission Process. In: Corchado, E., Yin, H., Botti, V., Fyfe, C. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2006. IDEAL 2006. Lecture Notes in Computer Science, vol 4224. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11875581_16
Download citation
DOI: https://doi.org/10.1007/11875581_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45485-4
Online ISBN: 978-3-540-45487-8
eBook Packages: Computer ScienceComputer Science (R0)