Skip to main content

Convergence of Chaos Injection-Based Batch Backpropagation Algorithm For Feedforward Neural Networks

  • Conference paper
  • 3599 Accesses

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 7951)

Abstract

This paper considers the convergence of chaos injection-based backpropagation algorithm. Both the weak convergence and strong convergence results are theoretically established.

Keywords

  • Convergence
  • Backpropagation algorithm
  • Chaos

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-642-39065-4_8
  • Chapter length: 7 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   99.00
Price excludes VAT (USA)
  • ISBN: 978-3-642-39065-4
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   131.00
Price excludes VAT (USA)

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Haykin, S.: Neural Networks and Learning Machines. Prentice Hall (2008)

    Google Scholar 

  2. Karnin, E.D.: A simple procedure for pruning back-propagation trained neural networks. IEEE Trans. Neural Netw. 1, 239–242 (1990)

    CrossRef  Google Scholar 

  3. Fine, T.L., Mukherjee, S.: Parameter convergence and learning curves for neural networks. Neural Computat. 11, 747–769 (1999)

    CrossRef  Google Scholar 

  4. Wu, W., Feng, G., Li, Z., Xu, Y.: Deterministic convergence of an online gradient method for bp neural networks. IEEE Trans. Neural Netw. 16, 533–540 (2005)

    CrossRef  Google Scholar 

  5. Wu, W., Wang, J., Chen, M.S., Li, Z.X.: Convergence analysis on online gradient method for BP neural networks. Neural Networks 24(1), 91–98 (2011)

    MATH  CrossRef  Google Scholar 

  6. Wang, J., Wu, W., Zurada, J.M.: Deterministic convergence of conjugate gradient mehtod for feedforward neural networks. Neurocomputing 74, 2368–2376 (2011)

    CrossRef  Google Scholar 

  7. Zhang, H.S., Wu, W., Liu, F., Yao, M.: Boundedness and convergence of online gadient method with penalty for feedforward neural networks. IEEE Trans. Neural Netw. 20(6), 1050–1054 (2009)

    CrossRef  Google Scholar 

  8. Zhang, H.S., Wu, W., Yao, M.C.: Boundedness and convergence of batch back-propagation algorithm with penalty for feedforward neural networks. Neurocomputing 89, 141–146 (2012)

    CrossRef  Google Scholar 

  9. Shao, H.M., Zheng, G.F.: Boundedness and convergence of online gradient method with penalty and momentum. Neurocomputing 74, 765–770 (2011)

    CrossRef  Google Scholar 

  10. Sum, J.P., Leung, C.S., Ho, K.I.: On-line node fault injection training algorithm for mlp networks: objective function and convergence analysis. IEEE Transactions on Neural Networks and Learning Systems 23(2), 211–222 (2012)

    CrossRef  Google Scholar 

  11. Ahmed, S.U., Shahjahan, M., Murase, K.: Injecting chaos in feedforward neetworks. Neural Process. Lett. 34, 87–100 (2011)

    CrossRef  Google Scholar 

  12. Bertsekas, D.P., Tsitsiklis, J.N.: Gradient convergence in gradient methods with errors. SIAM J. Optim. 3, 627–642 (2000)

    MathSciNet  CrossRef  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Zhang, H., Liu, X., Xu, D. (2013). Convergence of Chaos Injection-Based Batch Backpropagation Algorithm For Feedforward Neural Networks. In: Guo, C., Hou, ZG., Zeng, Z. (eds) Advances in Neural Networks – ISNN 2013. ISNN 2013. Lecture Notes in Computer Science, vol 7951. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39065-4_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-39065-4_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-39064-7

  • Online ISBN: 978-3-642-39065-4

  • eBook Packages: Computer ScienceComputer Science (R0)