Abstract
In this paper, we introduce a neo scheme of fuzzy-neural networks – Fuzzy Polynomial Neural Networks (FPNN) with a new fuzzy set-based polynomial neurons (FSPNs) whose fuzzy rules include the information granules (about the real system) obtained through Information Granulation(IG). We investigate the proposed networks from two different aspects to improve the performance of the fuzzy-neural networks. First, We have developed a design methodology (genetic optimization using Genetic Algorithms) to find the optimal structure for fuzzy-neural networks that expanded from Group Method of Data Handling (GMDH). It is the number of input variables, the order of the polynomial, the number of membership functions, and a collection of the specific subset of input variables that are the parameters of FPNN fixed by aid of genetic optimization that has search capability to find the optimal solution on the solution space. Second, we have been interested in the architecture of fuzzy rules that mimic the real world, namely sub-model (node) composing the fuzzy-neural networks. We adopt fuzzy set-based fuzzy rules as substitute for fuzzy relation-based fuzzy rules and apply the concept of Information Granulation to the proposed fuzzy set-based rules. The performance of genetically optimized FPNN (gFPNN) with fuzzy set-based polynomia neurons (FSPNs) composed of fuzzy set-based rules is quantified through experimentation where we use a number of modeling benchmarks data which are already experimented with in fuzzy or neurofuzzy modeling.
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Oh, S.K., Pedrycz, W.: Self-organizing Polynomial Neural Networks Based on PNs or FPNs: Analysis and Design, Fuzzy Sets and Systems (2003) (in press)
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 (1992)
Oh, S.K., Pedrycz, W.: Fuzzy Polynomial Neuron-Based Self-Organizing Neural Networks. Int. J. of General Systems 32, 237–250 (2003)
Wang, L.X., Mendel, J.M.: Generating fuzzy rules from numerical data with applications. IEEE Trans. Systems, Man, Cybern. 22(6), 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)
Oh, S.K., Pedrycz, W., Ahn, T.C.: Self-organizing neural networks with fuzzy polynomial neurons. Applied Soft Computing 2, 1–10 (2002)
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)
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)
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© 2005 Springer-Verlag Berlin Heidelberg
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Oh, SK., Roh, SB., Pedrycz, W., Lee, JB. (2005). IG-Based Genetically Optimized Fuzzy Polynomial Neural Networks. In: Cabestany, J., Prieto, A., Sandoval, F. (eds) Computational Intelligence and Bioinspired Systems. IWANN 2005. Lecture Notes in Computer Science, vol 3512. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11494669_51
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DOI: https://doi.org/10.1007/11494669_51
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26208-4
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