Abstract
We introduce a consecutive identification of ANFIS-based fuzzy systems with the aid of genetic data granulation to carry out the model identification of complex and nonlinear systems. The proposed model implements system structure and parameter identification with the aid of information granulation and genetic algorithms. The design methodology emerges as a hybrid structural optimization and parametric optimization. Information granulation realized with HCM clustering algorithm help determine the initial parameters of fuzzy model such as the initial apexes of the membership functions in the premise and the initial values of polynomial functions in the consequence. And the structure and the parameters of fuzzy model are identified by GAs and the membership parameters are tuned by GAs. In this case we exploit a consecutive identification. The numerical example is included to evaluate the performance of the proposed model.
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
Tong, R.M.: Synthesis of Fuzzy Models for Industrial Processes. Int. J. Gen Syst. 4, 143–162 (1978)
Pedrycz, W.: Numerical and Application Aspects of Fuzzy Relational Equations. Fuzzy Sets Syst. 11, 1–18 (1983)
Takagi, T., Sugeno, M.: Fuzzy Identification of Systems and Its Applications to Modeling and Control. IEEE Trans Syst, Cybern. SMC-15(1), 116–132 (1985)
Sugeno, M., Yasukawa, T.: Linguistic Modeling Based on Numerical Data. In: IFSA 1991 Brussels, Computer, Management & System Science, pp. 264–267 (1991)
Oh, S.K., Pedrycz, W.: Identification of Fuzzy Systems By Means of an Auto-Tuning Algorithm and Its Application to Nonlinear Systems. Fuzzy Sets and Syst. 115(2), 205–230 (2000)
Zadeh, L.A.: Toward A Theory of Fuzzy Information Granulation and Its Centrality in Human Reasoning and Fuzzy Logic. Fuzzy Sets and Syst. 90, 111–117 (1997)
Pderycz, W., Vukovich, G.: Granular Neural Networks. Neurocomputing 36, 205–224 (2001)
Krishnaiah, P.R., Kanal, L.N. (eds.): Classification, Pattern Recognition, and Reduction of Dimensionality. Volume 2 of Handbook of Statistics. North-Holland, Amsterdam (1982)
Golderg, D.E.: Genetic Algorithm in Search, Optimization & Machine Learning. Addison Wesley, Reading (1989)
Mackey, M.C., Glass, L.: Oscillation and Chaos in Physiological Control Systems. Science 197, 287–289 (1977)
Wang, L.X., Mendel, J.M.: Generating Fuzzy Rules from Numerical Data with Applications. IEEE Trans. Systems, Man, Cybern. 22(6), 1414–1427 (1992)
Crowder III, R.S.: Predicting the Mackey-Glass Time Series with Cascade-Correlation Learning. In: Touretzky, D., Hinton, G., Sejnowski, T. (eds.) Proceedings of the 1990 Connectionist Models Summer School. Carnegie Mellon University, pp. 117–123 (1990)
Jang, J.R.: ANFIS: Adaptive-Network-Based Fuzzy Inference System. IEEE Trans. System, Man, and Cybern. 23(3), 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)
Li, C.J., 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)
Park, H.S., Oh, S.K.: Fuzzy Relation-based Fuzzy Neural-Networks Using a Hybrid Identification Algorithm. International Journal of Control, Automations, and Systems 1(3), 289–300 (2003)
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
Oh, SK., Park, KJ., Pedrycz, W. (2006). Consecutive Identification of ANFIS-Based Fuzzy Systems with the Aid of Genetic Data Granulation. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3972. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760023_121
Download citation
DOI: https://doi.org/10.1007/11760023_121
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34437-7
Online ISBN: 978-3-540-34438-4
eBook Packages: Computer ScienceComputer Science (R0)