Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Neural Computing and Applications
Published in: Neural Computing and Applications 6/2013

01-11-2013 | Original Article

Robustness analysis of global exponential stability of neural networks with Markovian switching in the presence of time-varying delays or noises

Authors: Song Zhu, Yi Shen

Published in: Neural Computing and Applications | Issue 6/2013

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

In this paper, we analyze the robustness of global exponential stability of neural networks with Markovian switching (NNwMS) subject to random disturbances or time-varying delays. Given a globally exponentially stable neural network with Markovian switching, the problems to be addressed herein are how much noises or time delays that the neural networks can remain to be globally exponentially stable. We characterize the upper bounds of the time delays or noise intensity for the NNwMS to sustain global exponential stability. Two numerical examples are provided to illustrate the theoretical results.
previous article Incorporating feature selection method into support vector regression for stock index forecasting
next article SREC: Discourse-level semantic relation extraction from text

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now
Literature
1.
2.
3.
Pham J, Pakdaman K, Virbert J (1998) Noise-induced coherent oscillations in randomly connected neural networks. Phys Rev E 58(3):3610–3622CrossRef
4.
Arik S (2002) An analysis of global asymptotic stability of delayed cellular neural networks. IEEE Trans Neural Netw 13(5):1239–1242CrossRef
5.
Cao J, Yuan K, Li H (2006) Global asymptotical stability of recurrent neural networks with multiple discrete delays and distributed delays. IEEE Trans Neural Netw 17(6):1646–1651CrossRef
6.
Cao J, Wang J (2003) Global asymptotic satbility of a general class of recurrent neural networks with time varying delays. IEEE Trans Neural Netw 50(1):34–44MathSciNet
7.
He Y, Wu M, She J (2006) Delay-dependent exponential stability for delayed neural networks with time-varying delay. IEEE Trans Circuits Syst II Exp Briefs 53(7):553–557CrossRef
8.
Liao X, Wang J, Zeng Z (2005) Global asymptotic stability and global exponential stability of delayed cellular neural networks. IEEE Trans Circuits Syst II Exp Briefs 52(7):403–409CrossRef
9.
Li C, Liao X (2004) Global robust asymptotical stability of multidelayed interval neural networks: an LMI approach. Phys Lett A 328(6):452–462MathSciNetCrossRefMATH
10.
Xu S, Lam J, Ho DWC (2006) A new LMI condition for delay dependent asymptotic stability of delayed Hopfield neural networks. IEEE Trans Circuits Syst II Exp Briefs 53(3):230–234CrossRef
11.
Zhang H, Wang Z, Liu D (2008) Global asymptotic stability of recurrent neural networks with multiple time-varying delays. IEEE Trans Neural Netw 19(5):855–873MathSciNetCrossRef
12.
13.
Zeng Z, Wang J (2006) Complete stability of cellular neural networks with time-varying delays. IEEE Trans Circuits Syst I Reg Papers 53(4):944–955MathSciNetCrossRef
14.
Zeng Z, Wang J, Liao X (2003) Global exponential stability of a general class of recurrent neural networks with time-varying delays. IEEE Trans Circuits Syst I Fundam Theory Appl 50(10):1353–1358MathSciNetCrossRef
15.
Zeng Z, Huang T, Zheng W (2010) Multistability of recurrent neural networks with time-varying delays and the piecewise linear activation function. IEEE Trans Neural Netw 21(8):1371–1377CrossRef
16.
Shen Y, Wang J (2008) An improved algebraic criterion for global exponential stability of recurrent neural networks with time-varying delays. IEEE Trans Neural Netw 19(3):528–531CrossRef
17.
Shen Y, Wang J (2012) Robustness analysis of global exponential stability of recurrent neural networks in the presence of time delays and random disturbances. IEEE Trans Neural Netw 23(1):87–96CrossRef
18.
Liang J, Wang Z, Liu X (2009) State estimation for coupled uncertain stochastic networks with missing measurements and time-varying times: the discrete-time case. IEEE Trans Neural Netw 20(5):781–793CrossRef
19.
Song Q (2008) Exponential stability of recurrent neural networks with both time varying delays and general activation functions via LMI approach. Neurocomputing 71(13–15):2823–2830CrossRef
20.
Liao X, Chen G, Sanchez EN (2002) Delay dependent exponential stability of delayed neural networks: an LMI approach. Neural Netw 15(7):855–866MathSciNetCrossRef
21.
Xu S, Lam J, Ho DWC, Zou Y (2004) Global robust exponential stability analysis for interval recurrent neural networks. Phys Lett A 352(2):124–133CrossRef
22.
Forti M, Nistri P, Papini P (2005) Global exponential stability and global convergence in finite time of delayed neural networks with infinite gain. IEEE Trans Neural Netw 16(6):1449–1463CrossRef
23.
Chen H, Zhang Y, Hu P (2010) Novel delay-dependent robust stability criteria for neutral stochastic delayed neural networks. Neurocomputing 73(13C15):2554–2561CrossRef
24.
25.
Liu Y, Wang Z, Liu X (2006) Global exponential stability of generalized recurrent neural networks with discrete and distributed delays. Neural Netw 19:667–675CrossRefMATH
26.
Wang Z, Zhang H, Yu W (2007) Robust exponential stability analysis of neural networks with multiple time delays. Neurocomputing 70:2534–2543CrossRef
27.
Hu L, Gao H, Zheng W (2008) Novel stability of cellular neural networks with interval time varying delay. Neural Netw 21(10):1458–1263CrossRefMATH
28.
29.
Meng X, Tian M, Hu S (2011) Stability analysis of stochastic recurrent neural networks with unbounded time-varying delays. Neurocomputing 74(6):949–953CrossRef
30.
Chen T, Rong L (2004) Robust global exponential stability of Cohen-Grossberg neural networks with time delays. IEEE Trans Neural Netw 15(1):203–206MathSciNetCrossRef
31.
Zhu S, Shen Y, Chen G (2010) Exponential passivity of neural networks with time-varying delay and uncertainty. Phys Lett A 375(2):136–142MathSciNetCrossRefMATH
32.
33.
Yuan K, Cao J, Li H (2006) Robust stability of switched Cohen-Grossberg neural networks with mixed time-varying delays. IEEE Trans Syst Man Cybern 36(6):1356–1363CrossRef
34.
Shen Y, Wang J (2009) Almost sure exponential stability of recurrent neural networks with Markovian switching. IEEE Trans Neural Netw 20(5):840–855MathSciNetCrossRef
35.
Huang H, Qu Y, Li H (2005) Robust stability analysis of switched Hopfield neural networks with time-varying delay under uncertainty. Phys Lett A 345:345–354CrossRefMATH
36.
Wang Z, Liu Y, Liu X (2009) State estimation for jumping recurrent neural networks with discrete and distributed delays. Neural Netw 22(1):41–48CrossRef
37.
Zhang H, Wang Y (2008) Stability analysis of markovian jumping stochastic Cohen-Grossberg neural networks with mixed time delays. IEEE Trans Neural Netw 19(2):366–370CrossRef
38.
Wu Z, Shi P, Su H, Chu J (2012) Stability analysis for discrete-time Markovian jump neural networks with mixed time-delays. Expert Syst Appl 39:6174–6181CrossRef
39.
Zhu S, Shen Y, Liu L (2010) Exponential stability of uncertain stochastic neural networks with Markovian switching. Neural Process Lett 32:293–309CrossRef
40.
Mao X (2006) Stochastic differential equations with Markovian switching. Imperial College Press, LondonCrossRefMATH
Metadata
Title
Robustness analysis of global exponential stability of neural networks with Markovian switching in the presence of time-varying delays or noises
Authors
Song Zhu
Yi Shen
Publication date
01-11-2013
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 6/2013
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-012-1105-0

Other articles of this Issue 6/2013

Neural Computing and Applications 6/2013 Go to the issue

Original Article

Process plan and part routing optimization in a dynamic flexible job shop scheduling environment: an optimization via simulation approach

Original Article

Compressing arrays of classifiers using Volterra-neural network: application to face recognition

Original Article

An intelligent hybrid optimistic/pessimistic concurrency control algorithm for centralized database systems using modified GSA-optimized ART neural model

Original Article

Estimation of modal damping ratio of impact-damped flexible beams using ANFIS

Original Article

A low-cost decision-aided channel estimation method for Alamouti OSTBC

Original Article

Antiviral therapy using a fuzzy controller optimized by modified evolutionary algorithms: a comparative study

Premium Partner

    Image Credits