Skip to main content
SpringerLink
Log in

Recurrent Neural Network Inference of Internal Delays in Nonstationary Data Network

  • Conference paper
Advances in Neural Networks - ISNN 2006 (ISNN 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3973))

Included in the following conference series:

  • 1590 Accesses

Abstract

By applying tomography theory which is highly developed in fieldssuchas medical computerized tomography and seismic tomography to communication network, network tomography has become one of the focused new technologies, which can infer the internal performance of the network by external end-to-end measurement. In this paper, we propose a novel Inference algorithm based on the recurrent multilayer perceptron (RMLP) network capable of tracking nonstationary network behavior and estimating time-varying, internal delay characteristics. Simulation experiments demonstrate the performance of the RMLP 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 119.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

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. Liang, G., Yu, B.: Maximum Pseudo Likelihood Estimation in Network Tomography. IEEE Trans. Signal Processing 51, 2043–2053 (2003)

    Article  Google Scholar 

  2. Tsang, Y., Coates, M. (ed.): Network Delay Tomography. IEEE Trans. Signal Processing 51, 2125–2136 (2003)

    Article  Google Scholar 

  3. Coates, M.J., Nowak, R.D.: Sequential Monte Carlo Inference of Internal Delays in Nonstationary Data Networks. IEEE Trans. Signal Processing 50(2), 366–376 (2002)

    Article  Google Scholar 

  4. Li, Y., Lei, X.: Gas-water Recognition in Carbonate Rock Fracture Systems by use of Neural Network. Tianranqi Gongye/Natural Gas Industry 20, 32–36 (2000)

    Google Scholar 

  5. Feldkamp, L.A., Puskorius, G.V.: Training Controller for Robustness: Multi-stream DEKF. IEEE International Conference on Neural Network, 2377–2382 (1994)

    Google Scholar 

  6. Werbos, P.J.: Backpropagation Through Time: What It Does and How To Do it. Proc. IEEE 78, 1550–1560 (1990)

    Article  Google Scholar 

  7. Li, S.: Wind Power Prediction Using Recurrent Multilayer Perceptron Neural Networks. IEEE Power Engineering Society General Meeting 4, 13–17 (2003)

    Google Scholar 

  8. Harihara, P.: Real-time Implementation of a Neural Networks-Based Motor Speed Filter Using a Digital Signal Processor. M.S. thesis, Texas A&M Univ, TX (2002)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Key Lab of Broadband Optical Fiber Transmission and Communication Networks, University of Electronic Science and Technology of China, Chengdu, 610054, China

    Feng Qian, Guang-min Hu, Xing-miao Yao & Le-min Li

Authors
  1. Feng Qian
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Guang-min Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xing-miao Yao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Le-min Li
    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. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China

    Zhang Yi

  3. Department of Electrical Engineering, University of Louisville, 40292, Louisville, KY, U.S.A

    Jacek M. Zurada

  4. Laboratory for Computational Biology, Shanghai Center for Systems Biomedicine, 800 Dong Chuan Rd, 200240, Shanghai, China

    Bao-Liang Lu

  5. School of Electrical and Electronic Engineering, University of Manchester, UK

    Hujun Yin

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Qian, F., Hu, Gm., Yao, Xm., Li, Lm. (2006). Recurrent Neural Network Inference of Internal Delays in Nonstationary Data Network. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3973. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760191_28

Download citation

  • DOI: https://doi.org/10.1007/11760191_28

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34482-7

  • Online ISBN: 978-3-540-34483-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation