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Robust Modeling for Nonlinear Dynamic Systems Using a Neurofuzzy Approach with Iterative Optimization

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Advances in Neural Networks – ISNN 2005 (ISNN 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3497))

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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.

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References

  1. 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)

    MATH  Google Scholar 

  2. 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)

    Article  Google Scholar 

  3. Hunt, K.J., Haas, R., Brown, M.: On the Functional Equivalence of Fuzzy Inference Systems and Spline-based Networks. Intl. J Neural Systems (1995)

    Google Scholar 

  4. 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)

    Article  Google Scholar 

  5. Wang, L.X., Mendel, J.M.: Fuzzy Basis Functions, Universal Approximation, and Orthogonal Least Squares Learning. IEEE Trans. Neural Networks 3, 807–814 (1992)

    Article  Google Scholar 

  6. Golub, G.H., Van Loan, C.F.: Matrix Computations, 2nd edn. John Hopkins Univ. Press, Baltimore (1989)

    MATH  Google Scholar 

  7. 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)

    Article  Google Scholar 

  8. 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)

    Google Scholar 

  9. Yen, J., Wang, L.: Application of Statistical Information Criteria for Optimal Fuzzy Model Construction. IEEE Trans. on Fuzzy Systems 6, 362–372 (1998)

    Article  Google Scholar 

  10. Liu, S., Yu, J.: Model Construction Optimization for a Class of Fuzzy Models. Chinese J. Computers 24, 164–172 (2001)

    Google Scholar 

  11. 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)

    Google Scholar 

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© 2005 Springer-Verlag Berlin Heidelberg

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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

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  • 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)

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