Skip to main content
SpringerLink
Log in
SpringerLink
Log in

Global Exponential Convergence of Time-Varying Delayed Neural Networks with High Gain

  • Conference paper
Advances in Neural Networks – ISNN 2007 (ISNN 2007)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4491))

Included in the following conference series:

Abstract

This paper studies a general class of neural networks with time-varying delays and the neuron activations belong to the set of discontinuous monotone increasing functions. The discontinuities in the activations are an ideal model of the situation where the gain of the neuron amplifiers is very high. Because the delay in combination with high-gain nonlinearities is a particularly harmful source of potential instability, in the paper, conditions which ensure the global convergence of the neural network are derived.

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 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
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. Yi, Z., Heng, P.A., Vadakkepat, P.: Absolute Periodic and Absolute Stability of Delayed Neural Networks. IEEE Trans. Circuits and Systems I 49, 256–261 (2002)

    Article  MathSciNet  Google Scholar 

  2. Yi, Z., Heng, P.A., Leung, K.S.: Convergence Analysis of Celluar Neural Networks with Unbounded Delay. IEEE Trans. Circuits and Systems I 48, 680–687 (2001)

    Article  MATH  Google Scholar 

  3. Yi, Z., Heng, P.A., Fu, A.W.C.: Estimate of Exponential Convergence Rate and Exponential Stability of Neural Networks with Unbounded Delay. IEEE Trans. Neural Networks 10, 1487–1493 (1999)

    Article  Google Scholar 

  4. Liao, X., Wang, J.: Algebraic Criteria for Global Exponential Stability of Celluar Neural Networks with Multiple Time Delays. IEEE Trans. Circuits and Systems I 50, 268–285 (2003)

    Article  Google Scholar 

  5. Qi, H., Qi, L.: Deriving Sufficient Conditions for Global Asymptotic Stability of Delayed Neural Networks via Nonsmooth Analysis. IEEE Trans. Neural Networks 15, 99–109 (2004)

    Article  Google Scholar 

  6. Forti, M., Nistri, P.: Global Convergence of Neural Networks with Discontinuous Neural Activation. IEEE Trans. Circuits and Systems I 50, 1421–1435 (2003)

    Article  Google Scholar 

  7. Forti, M., Nistri, P., Papini, D.: Global Exponential Stability and Global Convergence in Finite Time of Delayed Neural Networks with Infinite Gain. IEEE Trans. Neural Networks 16, 1449–1463 (2005)

    Article  Google Scholar 

  8. Filippov, A.F.: Differential Equations with Discontinuous Right-Hand Side. Mathematics and its Applications (Soviet Series). Kluwer Academic Publishers, Boston (1988)

    Book  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

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

    Lei Zhang & Zhang Yi

Authors
  1. Lei Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhang Yi
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Electrical and Computer Engineering (M/C 154), University of Illinois at Chicago, 851 S. Morgan Street, 60607-7053, Chicago, IL, USA

    Derong Liu

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

    Shumin Fei

  3. Laboratory of Complex Systems, Institute of Automation, Chinese Adacemy of Sciences, 100080, Beijing, P. R. China

    Zeng-Guang Hou

  4. School of Information Science and Engineering, Northeast University, Shenyang, 110004, China

    Huaguang Zhang

  5. School of Electrical Engineering, Hohai University, Nanjing, 210098, China

    Changyin Sun

Rights and permissions

Reprints and permissions

Copyright information

© 2007 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Zhang, L., Yi, Z. (2007). Global Exponential Convergence of Time-Varying Delayed Neural Networks with High Gain. In: Liu, D., Fei, S., Hou, ZG., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4491. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72383-7_117

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-72383-7_117

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-72382-0

  • Online ISBN: 978-3-540-72383-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation