Abstract
A neurofuzzy modeling approach for nonlinear dynamic systems is proposed in this paper. An iterative optimization approach for a class of neurofuzzy systems is developed, which integrates the model structure analysis and simplification, model parameter estimation, compatible cluster merging and redundant cluster deleting, performance evaluation for neurofuzzy models. The effectiveness of the proposed modeling approach is illustrated by the Mackey-Glass chaotic time series. The simulation studies show that the parsimonious neurofuzzy model is beneficial to the robustness of model.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Takagi, T., Sugeno, M.: Fuzzy Identification of Systems and Its Applications to Modeling and Control. IEEE Trans. on Systems, Man and Cybernetics 15, 116–132 (1985)
Jang, J.-S., Roger, S.C.-T.: Functional Equivalence between Radial Basis Function Networks and Inference System. IEEE Trans. Neural Networks 4, 156–158 (1993)
Hunt, K.J., Haas, R., Brown, M.: On the Functional Equivalence of Fuzzy Inference Systems and Spline-based Networks. Intl. J Neural Systems (1995)
Hunt, K.J., Haas, R., Murray-Smith, R.M.: Extended the Functional Equivalence of Radial Basis Function Networks and Fuzzy Inference Systems. IEEE Trans. Neural Networks 7, 776–781 (1996)
Wang, L.X., Mendel, J.M.: Fuzzy Basis Functions, Universal Approximation, and Orthogonal Least Squares Learning. IEEE Trans. Neural Networks 3, 807–814 (1992)
Golub, G.H., Van Loan, C.F.: Matrix Computations, 2nd edn. John Hopkins Univ. Press, Baltimore (1989)
Kanjilal, P.P., Banerjee, D.N.: On the Application of Orthogonal Transformation for the Design and Analysis of Feedforward Networks. IEEE Trans. on Neural Networks 6, 1061–1070 (1995)
Mouzouris, G.C., Mendel, J.M.: Designing Fuzzy Logic Systems for Uncertain Environments Using a Singular-Value-QR Decomposition Method. In: Proceedings of 1996 IEEE International Conference on Fuzzy Systems, pp. 295–301 (1996)
Yen, J., Wang, L.: Application of Statistical Information Criteria for Optimal Fuzzy Model Construction. IEEE Trans. on Fuzzy Systems 6, 362–372 (1998)
Liu, S., Yu, J.: Model Construction Optimization for a Class of Fuzzy Models. Chinese J. Computers 24, 164–172 (2001)
Liu, S., Yu, J.J., Lin, W., Yu, J.: Heuristic Fuzzy Cluster Learning and Its Applications in Function Approximation and Nonlinear System Modeling. Pattern Recognition and Artificial Intelligence 16, 230–235 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Liu, S., Yang, S.X., Yu, J. (2005). Robust Modeling for Nonlinear Dynamic Systems Using a Neurofuzzy Approach with Iterative Optimization. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3497. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427445_68
Download citation
DOI: https://doi.org/10.1007/11427445_68
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25913-8
Online ISBN: 978-3-540-32067-8
eBook Packages: Computer ScienceComputer Science (R0)