Skip to main content
SpringerLink
Log in

A New Adaptive Ridgelet Neural Network

  • Conference paper
Advances in Neural Networks – ISNN 2005 (ISNN 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3496))

Included in the following conference series:

Abstract

In this paper, a new kind of neural network is proposed by combining ridgelet with feed-forward neural network (FNN). The network adopts ridgelet as the activation function in hidden layer of a three-layer FNN. Ridgelet is a good basis for describing the directional information in high dimension and it proves to be optimal in representing the functions with hyperplane singularity. So the network can approximate quite a wide range of multivariate functions in a more stable and efficient way, especially those with certain kinds of spatial inhomogeneities. Both theoretical analysis and experimental results of function approximation prove its superiority to wavelet neural network.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. De Vore, R.A., Oskolkov, K.I., Petrushev, P.P.: Approximation by Feed-forward Neural Networks. Ann. Numer. Math. 4, 261–287 (1997)

    MathSciNet  Google Scholar 

  2. Cybenko, G.: Approximation by Superpositions of a Sigmoidal Function. Math.Control Signals System 2, 303–314 (1989)

    Article  MATH  MathSciNet  Google Scholar 

  3. Daugman, J.G.: Complete Discrete 2-d Gabor Transforms by Neural Networks for Image Analysis and Compression. IEEE Trans on Acoustics, Speech, and SP 36(7), 1169–1179 (1988)

    Article  MATH  Google Scholar 

  4. Moody, J.: Fast Learning in Networks of Locally-tuned Processing Units. Neural Computation 1, 281–294 (1989)

    Article  Google Scholar 

  5. Benveniste, A.Q.: Wavelet Networks. IEEE Trans on NN 3, 899–898 (1992)

    Google Scholar 

  6. Narendra, K.S., Parthasarathy, K.: Identification and Control of Dynamical Systems Using Neural Networks. IEEE Trans. Neural Networks 1, 4–27 (1990)

    Article  Google Scholar 

  7. Chow, M.Y.: A Neural Network Approach to Real Time Condition Monitoring of Induction Machines. IEEE Trans. on Industrial Electronics 38, 48–453 (1991)

    Article  Google Scholar 

  8. Friedman, W.: Projection Pursuit Regression. J. Amer. Statist. Assoc. 76, 817–823 (1981)

    Article  MathSciNet  Google Scholar 

  9. Candes, E.J.: Ridgelet: Theory and Applications. Ph.D. Dissertation. Stanford Univ (1998)

    Google Scholar 

  10. Candes, E.J.: Ridgelets: A Key to Higher-Dimensional Intermittency. Technical Report, Stanford Univ. (1999)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Intelligence Information Processing, Xidian University, Xi’an, 710071, China

    Shuyuan Yang, Min Wang & Licheng Jiao

Authors
  1. Shuyuan Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Min Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Licheng Jiao
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  2. The Key Laboratory of Optoelectric Technology & Systems, Ministry of Education, China

    Xiaofeng Liao

  3. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China

    Zhang Yi

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Yang, S., Wang, M., Jiao, L. (2005). A New Adaptive Ridgelet Neural Network. In: Wang, J., Liao, X., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3496. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427391_61

Download citation

  • DOI: https://doi.org/10.1007/11427391_61

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25912-1

  • Online ISBN: 978-3-540-32065-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation