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Neural Computing and Applications

Erschienen in:

01.09.2014 | Original Article

Robustness of globally exponential stability of delayed neural networks in the presence of random disturbances

verfasst von: Song Zhu, Weiwei Luo, Jinyu Li, Yi Shen

Erschienen in: Neural Computing and Applications | Ausgabe 3-4/2014

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Abstract

This paper analyzes the robustness of globally exponential stability of time-varying delayed neural networks (NNs) subjected to random disturbances. Given a globally exponentially stable neural network, and in the presence of noise, we quantify how much noise intensity that the delayed neural network can remain to be globally exponentially stable. We characterize the upper bounds of the noise intensity for the delayed NNs to sustain globally exponential stability. The upper bounds of parameter uncertainty intensity are characterized by using transcendental equation. A numerical example is provided to illustrate the theoretical result.

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Haykin S (1994) Neural networks. Prentice Hall, New YorkMATH

Pham J, Pakdaman K, Virbert J (1998) Noise-induced coherent oscillations in randomly connected neural networks. Phys Rev E 58(3):3610–3622CrossRef

Arik S (2002) An analysis of global asymptotic stability of delayed cellular neural networks. IEEE Trans Neural Netw 13(5):1239–1242CrossRef

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

Chen T (2001) Global exponential stability of delayed Hopfield neural networks. Neural Netw 14(8):977–980CrossRef

Chua LO, Yang L (1988) Celluar neural networks: theory. IEEE Trans Circuits Syst 35(10):1257–1272CrossRef

Faydasicok O, Arik S (2012) Robust stability analysis of a class of neural networks with discrete time delays. Neural Netw 29(30):52–59CrossRef

Faydasicok O, Arik S (2013) A new robust stability criterion for dynamical neural networks with multiple time delays. Neurocomputing 99:290–297CrossRef

Hu L, Gao H, Zheng W (2008) Novel stability of cellular neural networks with interval time varying delay. Neural Netw 21(10):1458–1463MATHCrossRef

10.

Huang H, Ho DWC, Lam J (2005) Stochastic stability analysis of fuzzy Hopfield neural networks with time-varying delays. IEEE Trans Circuits Syst II Exp. Briefs 52(5):251–255CrossRef

11.

Liao X, Chen G, Sanchez EN (2002) Delay dependent exponential stability of delayed neural networks: an LMI approach. Neural Netw 15(7):855–866CrossRefMathSciNet

12.

Meng X, Tian M, Hu S (2011) Stability analysis of stochastic recurrent neural networks with unbounded time-varying delays. Neurocomputing 74(6):949–953CrossRef

13.

Liao X, Wang J (2003) Algebraic criteria for global exponential stability of cellular neural networks with multiple time delays. IEEE Trans Circuits Syst I 50(2):268–275CrossRefMathSciNet

14.

Shen Y, Wang J (2007) Noise-induced stabilization of the recurrent neural networks with mixed time varying delays and Markovian-switching parameters. IEEE Trans Neural Netw 18(6):1857–1862CrossRef

15.

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

16.

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

17.

Wang Z, Liu Y, Li M, Liu X (2006) Stability analysis for stochastic Cohen–Grossberg neural networks with mixed time delays. IEEE Trans Neural Netw 17(3):814–820CrossRef

18.

Zhu S, Shen Y, Liu L (2010) Exponential stability of uncertain stochastic neural networks with Markovian switching. Neural Process Lett 32:293–309CrossRef

19.

Zhu S, Shen Y (2013) Robustness analysis for connection weight matrix of global exponential stability recurrent neural networks. Neurocomputing 101:370–374CrossRef

20.

Zhu S, Shen Y, Chen G (2010) Exponential passivity of neural networks with time-varying delay and uncertainty. Phys Lett A 375(2):136–142MATHCrossRefMathSciNet

21.

Zeng Z, Wang J (2006) Complete stability of cellular neural networks with time-varying delays. IEEE Trans Circuits Syst I Reg Pap 53(4):944–955CrossRefMathSciNet

22.

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

23.

Zhang H, Wang Z, Liu D (2009) Global asymptotic stability and robust stability of a class of Cohen–Grossberg neural networks with mixed delays. IEEE Trans Circuits Syst I 56(3):616–629CrossRefMathSciNet

24.

Chen H, Zhang Y, Hu P (2010) Novel delay-dependent robust stability criteria for neutral stochastic delayed neural networks. Neurocomputing 73(13–15):2554–2561CrossRef

25.

Zhu S, Shen Y (2012) Robustness analysis of global exponential stability of neural networks with Markovian switching in the presence of time-varying delays or noises. Neural Comput Appl 23(6):1563–1671CrossRefMathSciNet

26.

Zhu S, Shen Y (2013) Two algebraic criteria for input-to-state stability of recurrent neural networks with time-varying delays. Neural Comput Appl 22:1163–1169CrossRefMathSciNet

27.

Mao X (2007) Stochastic differential equations and applications, 2 edn. Harwood, Chichester

28.

Mao X (2007) Stability and stabilization of stochastic differential delay equations. IET Control Theory Appl 1(6):1551–1566CrossRef

Titel: Robustness of globally exponential stability of delayed neural networks in the presence of random disturbances
verfasst von: Song Zhu
Weiwei Luo
Jinyu Li
Yi Shen
Publikationsdatum: 01.09.2014
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 3-4/2014
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-014-1547-7

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