Abstract
We investigate a new fuzzy-neural networks-Hybrid Fuzzy set based polynomial Neural Networks (HFSPNN). These networks consist of genetically optimized multi-layer with two kinds of heterogeneous neurons that are fuzzy set based polynomial neurons (FSPNs) and polynomial neurons (PNs). We have developed a comprehensive design methodology to determine the optimal structure of networks dynamically. The augmented genetically optimized HFSPNN (namely gHFSPNN) results in a structurally optimized structure and comes with a higher level of flexibility in comparison to the one we encounter in the conventional HFPNN. The GA-based design procedure being applied at each layer of gHFSPNN leads to the selection leads to the selection of preferred nodes (FSPNs or PNs) available within the HFSPNN. In the sequel, the structural optimization is realized via GAs, whereas the ensuing detailed parametric optimization is carried out in the setting of a standard least square method-based learning. The performance of the gHFSPNN is quantified through experimentation where we use a number of modeling benchmarks-synthetic and experimental data already experimented with in fuzzy or neurofuzzy modeling.
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References
Ivahnenko, A.G.: Polynomial theory of complex systems. IEEE Trans. on Systems Man and Cybernetics. SMC 12, 364–378 (1971)
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., Kim, D.W.: Hybrid Fuzzy Polynomial Neural Networks. Int. J. of Uncertainty. Fuzziness and Knowledge-Based Systems 10, 257–280 (2002)
Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs. Springer, Heidelberg (1996)
Jong, D., K.A.: Are Genetic Algorithms Function Optimizers? In: Manner, R., Manderick, B. (eds.) Parallel Problem Solving from Nature 2, North-Holland, Amsterdam
Wang, L.X., Mendel, J.M.: Generating fuzzy rules from numerical data with applications. IEEE Trans. Systems, Man, Cybern. 22, 1414–1427 (1992)
Jang, J.S.R.: ANFIS: Adaptive-Network-Based Fuzzy Inference System. IEEE Trans. System, Man, and Cybern. 23, 665–685 (1993)
Maguire, L.P., Roche, B., McGinnity, T.M., McDaid, L.J.: Predicting a chaotic time series using a fuzzy neural network. Information Sciences 112, 125–136 (1998)
James Li, C., Huang, T.Y.: Automatic structure and parameter training methods for modeling of mechanical systems by recurrent neural networks. Applied Mathematical Modeling 23, 933–944 (1999)
Oh, S.K., Pedrycz, W., Ahn, T.C.: Self-organizing neural networks with fuzzy polynomial neurons. Applied Soft Computing 2, 1–10 (2002)
Lapedes, A.S., Farber, R.: Non-linear Signal Processing Using Neural Networks: Prediction and System Modeling. Technical Report LA-UR-87-2662, Los Alamos National Laboratory. Los Alamos, New Mexico 87545 (1987)
Mackey, M.C., Glass, L.: Oscillation and chaos in physiological control systems. Science 197, 287–289 (1977)
Park, B.J., Lee, D.Y., Oh, S.K.: Rule-based Fuzzy Polynomial Neural Networks in Modeling Software Process Data. Int. J. of Control, Automations, and Systems 1(3), 321–331 (2003)
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, Automations, and Systems 2(4), 423–434 (2004)
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Oh, SK., Roh, SB., Pedrycz, W. (2005). A New Approach to Genetically Optimized Hybrid Fuzzy Set-Based Polynomial Neural Networks with FSPNs and PNs. 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_32
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DOI: https://doi.org/10.1007/11526018_32
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