Abstract
Modeling nonlinear systems in terms of fuzzy rules often encounters a few problems such as the conflict between overfitting and underfitting, and low reliability that increases the number of the necessary fuzzy rules. To deal with these problems, we propose a hybrid fuzzy-neural modeling technique. Performance of the proposed approach is compared to that of the conventional approach for the case of forecasting the time series. Result shows that the pro-posed method is more efficient and accurate in terms of the number of fuzzy rules and its generalization.
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Chen, J.Q., Xi, X.G.: Nonlinear System Modeling by Competitive Learning and Adaptive Fuzzy Inference System. IEEE Trans. Syst., Man, Cybern. 28, 231–238 (1998)
Takagi, T., Sugeno, M.: Fuzzy Identification of Systems and Its Applications to Modeling and Control. IEEE Trans. Syst. Man Cybern. 15, 116–132 (1985)
Oh, S.K., Kim, D.W., Park, B.J.: A Study on the Optimal Design of Polynomial Neural Networks Structure. Trans. KIEE 49D, 145–156 (2000)
Ivakhnenko, A.G.: Polynomial theory of complex systems. IEEE Trans. Syst. Man Cybern., 364–378 (1971)
Ivakhnenko, A.G., Krotov, G.I., Ivakhnenko, N.A.: Identification of the mathematical model of a complex system by the self-organization method. In: Halfon, E. (ed.) Theoretical Systems Ecology: Advances and Case Studies. Academic, New York (1970)
Farlow, S.J.: Self-Organizing Methods in Modeling, GMDH Type-Algorithms. Marcel Dekker, New York (1984)
Jang, J.S.: ANFIS: Adaptive-Networks-Based Fuzzy Inference System. IEEE Trans. Syst. Man Cybern. 23, 665–685 (1993)
Jang, J.S., Sun, C.T., Mizutani, E.: Neuro-Fuzzy AND Soft Computing: A Computational Approach to Learning and Machine Intelligence. Prentice Hall, Englewood Cliffs (1997)
Oh, S.K., Pedrycz, W.: The design of self-organizing Polynomial Neural Networks. Inf. Sci. 141, 237–258 (2002)
Sugeno, M., Yasukawa, T.: A fuzzy-logic-based approach to qualitative modeling. IEEE Trans. Fuzzy Syst. 1, 7–31 (1993)
Xu, C.W., Zailu, Y.: Fuzzy model identification self-learning for dynamic system. IEEE Trans. Syst. Man Cybern. 17, 683–689 (1987)
Box, G.E.P., Jenkins, F.M.: Time Series Analysis: Forecasting and Control, 2nd edn. Holden-day (1976)
Sugeno, M., Tanaka, K.: Successive identification of a fuzzy model and its applications to prediction of a complex system. Fuzzy Sets Syst. 42, 315–334 (1991)
Kim, E., Lee, H., Park, M., Park, M.: A simple identified Sugeno-type fuzzy model via double clustering. Inf. Sci. 110, 25–39 (1998)
Lin, Y., Cunningham III, G.A.: A new approach to fuzzy-neural modeling. IEEE Trans. Fuzzy Syst. 3, 190–197 (1995)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kim, D., Seo, SJ., Park, GT. (2004). Hybrid Fuzzy-Neural Architecture and Its Application to Time Series Modeling. In: Negoita, M.G., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2004. Lecture Notes in Computer Science(), vol 3215. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30134-9_81
Download citation
DOI: https://doi.org/10.1007/978-3-540-30134-9_81
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23205-6
Online ISBN: 978-3-540-30134-9
eBook Packages: Springer Book Archive