Abstract
In this paper, we introduce new fuzzy-neural networks – Fuzzy Set – based Polynomial Neural Networks (FSPNN) with a new fuzzy set-based polynomial neuron (FSPN) whose fuzzy rules include the information granules obtained through Information Granulation. We investigate the proposed networks from two different aspects to improve the performance of the fuzzy-neural networks. First, We have developed genetic optimization using Genetic Algorithms to find the optimal structure for fuzzy-neural networks. Second, we have been interested in the architecture of fuzzy rules that mimic the real world, namely sub-model 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 FSPNN (gFSPNN) with fuzzy set-based neural neuron (FSPN) involving information granules is quantified through experimentation.
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References
Oh, S.K., Pedrycz, W.: Self-organizing Polynomial Neural Networks Based on PNs or FPNs: Analysis and Design. Fuzzy Sets and Systems 142, 163–198 (2003)
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
Oh, S.K., Pedrycz, W.: Fuzzy Polynomial Neuron-Based Self-Organizing Neural Networks. Int. J. of General Systems 32, 237–250 (2003)
Jang, J.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)
Mackey, M.C., Glass, L.: Oscillation and Chaos in Physiological Control Systems. Science 197, 287–289 (1977)
Oh, S.K., Pedrycz, W.: The Design of Self-organizing Polynomial Neural Networks. Information Science 141, 237–258 (2002)
Sugeno, M., Yasukawa, T.: A Fuzzy-Logic-Based Approach to Qualitative Modeling. IEEE Trans. Fuzzy Systems 1, 7–31 (1993)
Park, B.J., Pedrycz, W., Oh, S.K.: Fuzzy Polynomial Neural Networks: Hybrid Architectures of Fuzzy Modeling. IEEE Transaction on Fuzzy Systems 10, 607–621 (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)
Zadeh, L.A.: Toward a Theory of Fuzzy Information Granulation and Its Centrality in Human Reasoning and Fuzzy Logic. Fuzzy Sets Syst 90, 111–117 (1197)
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Oh, S., Roh, S., Kim, Y. (2005). Design of Genetic Fuzzy Set-Based Polynomial Neural Networks with the Aid of Information Granulation. In: Wang, J., Liao, X., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3496. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427391_68
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DOI: https://doi.org/10.1007/11427391_68
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25912-1
Online ISBN: 978-3-540-32065-4
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