Inverse System Identification of Nonlinear Systems Using LSSVM Based on Clustering | SpringerLink
Skip to main content
Springer Nature Link
Log in

Inverse System Identification of Nonlinear Systems Using LSSVM Based on Clustering

  • Conference paper
Advances in Neural Networks - ISNN 2008 (ISNN 2008)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5263))

Included in the following conference series:

  • 3115 Accesses

Abstract

In this paper we propose the algorithm of embedding fuzzy c-means (FCM) clustering in least square support vector machine (LSSVM). We adopt the method to identify the inverse system with immeasurable crucial variables and the inenarrable nonlinear character. In the course of identification, we construct the allied inverse system by the left inverse soft-sensing function and the right inverse system, and decide the number of clusters by a validity function, then utilize the proposed method to approach the nonlinear allied inverse system via offline training. Simulation experiments are performed and indicate that the proposed method is effective and provides satisfactory performance with excellent accuracy and low computational cost.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Dai, X., Liu, J., Feng, C., et al.: Neural network α-th order inverse system method for the control of nonlinear continuous systems. IEE Proc.-Control Theory Appl. 145, 519–523 (1998)

    Article  Google Scholar 

  2. Dai, X.: Multivariable nonlinear inverse control methods with neural networks. The Science Press, Beijing (2001)

    Google Scholar 

  3. Vapnik, V.: An overview of statistical learning theory. IEEE Transactions on Neural Networks 10, 955–999 (1999)

    Article  Google Scholar 

  4. Suykens, J.A.K., Vandewalle, J.: Least Squares Support Vector Machine Classifiers. Neural Processing Letter 9(3), 293–300 (1999)

    Article  MathSciNet  Google Scholar 

  5. Kim, P.J., Chang, H.J., Song, D.S., et al.: Fast support vector data description using k-means clustering. In: Liu, D., Fei, S., Hou, Z. (eds.) ISNN 2007. LNCS, vol. 4493, pp. 506–514. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  6. Yao, J., Dash, M., Tan, S.T.: Entropy-based fuzzy clustering and fuzzy modeling. Fuzzy Sets and Systems 113, 381–388 (2000)

    Article  MATH  Google Scholar 

  7. Dai, X.Z., Wang, W.C., Ding, Y.H., et al.: Assumed inherent sensor inversion based ANN dynamic soft-sensing method and its application in erythromycin fermentation process. Computers and Chemical Engineering 30, 1203–1225 (2006)

    Article  Google Scholar 

  8. Sun, C.Y., Song, J.Y.: An adaptive internal model control based on LS-SVM. In: Liu, D., Fei, S., Hou, Z. (eds.) ISNN 2007. LNCS, vol. 4493, pp. 479–485. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  9. Xing, S.W., Liu, H.B., Niu, X.X.: Fuzzy Support Vector Machines based on FCM Clustering. In: Proceedings of the Fourth International Conference on Machine Learning and Cybernetics, pp. 2608–2613. IEEE Press, Los Alamitos (2005)

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. College of Electrical Engineering, Hohai University, Nanjing, 210098, P.R. China

    Changyin Sun, Chaoxu Mu & Hua Liang

  2. School of Automation, Southeast University, Nanjing, 210096, P.R. China

    Changyin Sun

Authors
  1. Changyin Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chaoxu Mu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hua Liang
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Computer Science and Technology, Tsinghu University, 100084, Beijing, China

    Fuchun Sun

  2. Institute TAMS (Technical Aspects of Multimodal Systems), department of Informatics, University of Hamburg, Vogt-Koelln-Straße 30, 22527, Hamburg, Germany

    Jianwei Zhang

  3. Intel China Research Center, 8/F, Peking University, Department of Machine Intelligence, 100871, Beijing, China

    Ying Tan

  4. Department of Mathematics, Southeast University, 210096, Nanjing, China

    Jinde Cao

  5. Departamento de Control Automático, CINVESTAV-IPN, A.P. 14-740, Av.IPN 2508, 07360, México D.F., México

    Wen Yu

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Sun, C., Mu, C., Liang, H. (2008). Inverse System Identification of Nonlinear Systems Using LSSVM Based on Clustering. In: Sun, F., Zhang, J., Tan, Y., Cao, J., Yu, W. (eds) Advances in Neural Networks - ISNN 2008. ISNN 2008. Lecture Notes in Computer Science, vol 5263. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87732-5_76

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-87732-5_76

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-87731-8

  • Online ISBN: 978-3-540-87732-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation