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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 6/2013

01.05.2013 | Original Article

Two algebraic criteria for input-to-state stability of recurrent neural networks with time-varying delays

verfasst von: Song Zhu, Yi Shen

Erschienen in: Neural Computing and Applications | Ausgabe 6/2013

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Abstract

This paper presents two algebraic criteria for the input-to-state stability of recurrent neural networks with time-varying delays. The criteria which also ensure global exponential stability when the input u(t) is equal to 0 and is easy to be verified only with the connection weights of the recurrent neural networks. Two numerical examples are given to demonstrate the effectiveness of the proposed criteria.
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Literatur
1.
Hopfield JJ (1982) Neural networks and physical systems with emergent collective computational abilities. Proc Nat Acad Sci USA 79:2554−2558MathSciNetCrossRef
2.
3.
Cohen MA, Grossberg S (1983) Absolute stability of global pattern formation and parallel memory storage by competitive neural networks. IEEE Trans Syst Man Cybern 13(5):815–821MathSciNetMATHCrossRef
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.
Meng X, Tian M, Hu S (2011) Stability analysis of stochastic recurrent neural networks with unbounded time-varying delays. Neurocomputing 74(6):949–953CrossRef
9.
Liao X, Wang J (2003) Algebraic criteria for global exponential stability of cellular neural networks with multiple time delays. IEEE Trans Circuits Syst I Fundam Theory Appl 50(2):268–275MathSciNetCrossRef
10.
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
11.
Li C, Liao X (2004) Global robust asymptotical stability of multi-delayed interval neural networks: an LMI approach. Phys Lett A 328(6):452–462MathSciNetMATHCrossRef
12.
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
13.
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
14.
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
15.
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
16.
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
17.
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
18.
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
19.
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
20.
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
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.
Huang H, Qu Y, Li H (2005) Robust stability analysis of switched Hopfield neural networks with time-varying delay under uncertainty. Phys Lett A 345:345–354MATHCrossRef
24.
Shen Y, Wang J (2009) Almost sure exponential stability of recurrent neural networks with Markovian switching. IEEE Trans Neural Netw 20(5):840–855MathSciNetCrossRef
25.
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
26.
Zhu S, Shen Y, Chen G (2010) Exponential passivity of neural networks with time-varying delay and uncertainty. Phys Lett A 375:136–142MathSciNetMATHCrossRef
27.
28.
Sontag ED (2006) Input to state stability: basic concepts and results. In: Nistri N, Stefani G (eds) Nonlinear Optimal Control Theory, pp 163–220
29.
Sontag ED, Wang Y (1996) New characterizations of input to state stability. IEEE Trans Autom Control (41):1283–1294
30.
Jiang Z, Wang Y (2001) Input-to-state stability for discrete-time nonlinear systems. Automatica (37):857–869
31.
Cao C, Teel AR (2009) Characterizations of input-to-state stability for hybrid systems. Syst. Control Lett. 58:47–53CrossRef
32.
Huang L, Mao X (2009) On input-to-state stability of stochastic retarded systems with Markovian switching. IEEE Trans Automat Control 54 (8):1898–1902MathSciNetCrossRef
33.
Fridman E, Dambrine M, Yeganefar N (2008) On input-to-state stability of systems with time-delay: A matrix inequalities approach. Automatica (44):2364–2369
34.
Sontag ED (1995) On the input to state stability property. Eur J Control 1:1–24
35.
Sanchez EN, Perez JP (1999) Input-to-state stability(ISS) analysis for dynamic neural networks. IEEE Trans Circuits Syst I Fundam Theory Appl 46(11):1395–1398MathSciNetMATHCrossRef
36.
Guo Y (2008) New results on input-to-state convergence for recurrent neural networks with variable inputs. Nonlinear Anal RWA. (9):1558–1566
37.
Yu W, Li X (2001) Some stability properties of dynamic neural networks. IEEE Trans Circuits Syst I Fundam Theory Appl 48(2):256–259MATHCrossRef
38.
Ahn CK (2010) Passive learning and input-to-state stability of switched Hopfield neural networks with time-delay. Inform Sci (180):4582–4594
Metadaten
Titel
Two algebraic criteria for input-to-state stability of recurrent neural networks with time-varying delays
verfasst von
Song Zhu
Yi Shen
Publikationsdatum
01.05.2013
Verlag
Springer-Verlag
Erschienen in
Neural Computing and Applications / Ausgabe 6/2013
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-012-0882-9

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