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International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 1/2014

01.02.2014 | Original Article

Dynamic analysis of discrete-time BAM neural networks with stochastic perturbations and impulses

verfasst von: R. Raja, U. Karthik Raja, R. Samidurai, A. Leelamani

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 1/2014

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Abstract

This paper addresses the problem of stability analysis for a class of uncertain discrete-time stochastic BAM neural networks with time-varying delays and impulses. In this paper, we assume that stochastic disturbances are described by the Brownian motion and jumping parameters are generated from discrete-time discrete-state homogeneous Markov process. By employing the Lyapunov–Krasovskii functional and stochastic analysis theory, a set of novel sufficient conditions are derived to guarantee the robust global exponential stability of the equilibrium point in the mean square. The obtained results are shown to be less conservative than the existing one in the literature. Note that the obtained results are formulated in terms of linear matrix inequality (LMI) that can efficiently solved by the LMI toolbox in Matlab. Numerical examples are given to show that the proposed result significantly improve the allowable upper bounds of delays over some existing results in the literature.
Vorheriger Artikel Robust exponential stability analysis for interval Cohen–Grossberg type BAM neural networks with mixed time delays
Nächster Artikel Online neural network model for non-stationary and imbalanced data stream classification

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Literatur
1.
Arik S (2006) Global asymptotic stability of hybrid BAM neural networks with time delays. Phys Lett A 351:85–91CrossRefMATH
2.
Barakat M, Lefebvre D, Khalil M, Druaux F, Mustapha O (2013) Parameter selection algorithm with self adaptive growing neural network classifier for diagnosis issues. Int J Mach Learn Cybern 4(3): 217–233CrossRef
3.
Cao J, Ho DWC, Huang X (2007) LMI-based criteria for global robust stability of bidirectional associative memory networks with time delay. Nonlinear Anal 66:1558–1572CrossRefMATHMathSciNet
4.
Cao J (2003) Global asymptotic stability of delayed bi-directional associative memory neural networks. Appl Math Comput 142:333–339CrossRefMATHMathSciNet
5.
Chen A, Huang L, Cao J (2003) Existence and stability of almost periodic solution for BAM neural networks with delays. Appl Math Comput 137:177–193CrossRefMATHMathSciNet
6.
Gao M, Cui BT (2009) Global robust exponential stability of discrete-time interval BAM neural networks with time-varying delays. Appl Math Model 33:1270–1284CrossRefMATHMathSciNet
7.
Gan Q (2013) Synchronization of competitive neural networks with different time scales and time-varying delay based on delay partitioning approach. Int J Mach Learn Cybern 4(4):327–337CrossRef
8.
Huang X, Cao J, Huang DS (2005) LMI-Based approach for delay-dependent exponential stability analysis of BAM neural networks. Chaos Solitons Fractals 24:885–898CrossRefMATHMathSciNet
9.
Ho DWC, Liang JL, Lam J (2006) Global exponential stability of impulsive high-order BAM neural networks with time-varying delays. Neural Netw 19:1581–1590CrossRefMATH
10.
Kosko B (1987) Adaptive bidirectional associative memories. Appl Opt 26:4947–4960CrossRef
11.
12.
Liang JL, Cao JD (2004) Exponential stability of continuous-time and discrete-time bidirectional associative memory networks with delays. Chaos Solitons Fractals 22:773–785CrossRefMATHMathSciNet
13.
Li K, Zhang L, Zhang X, Li Z (2010) Stability in impulsive Cohen–Grossberg-type BAM neural networks with distributed delays. Appl Math Comput 215:3970–3984CrossRefMATHMathSciNet
14.
Liu Y, Wang Z, Liu X (2007) Robust stability of discrete-time stochastic neural networks with time-varying delays. Neurocomputing 71:823–833CrossRef
15.
Liu Y, Wang Z, Liang J, Liu X (2008) Synchronization and state estimation for discrete-time complex networks with distributed delays. IEEE Trans Syst Man Cybern Part B 38:1314–1325CrossRef
16.
Liu X, Tang M, Martin R, Liu X (2007) Discrete-time BAM neural networks with variable delays. Phys Lett A 367:322–330CrossRefMATH
17.
Lou X, Cui B (2006) On the global robust asymptotic stability of BAM neural networks with time-varying delays. Neurocomputing 70:273–279CrossRef
18.
Liang J, Wang Z, Liu Y, Liu X (2008) Global synchronization control of general delayed discrete-time networks with stochastic coupling and disturbances. IEEE Trans Syst Man Cybern Part B 38:1073–1083CrossRef
19.
Liang J, Wang Z, Liu X (2008) Exponential synchronization of stochastic delayed discrete-time complex networks. Nonlinear Dyn 53:153–165CrossRefMATHMathSciNet
20.
Mohamad S (2001) Global exponential stability in continuous-time and discrete-time delayed bidirectional neural networks. Physica D 159:233–251CrossRefMATHMathSciNet
21.
Park JH, Kwon OM (2008) On improved delay-dependent criterion for global stability of bidirectional associative memory neural networks with time- varying delays. Appl Math Comput 199(2):435–446CrossRefMATHMathSciNet
22.
Park JH (2006) Robust stability of bidirectional associative memory neural networks with time delays. Phys Lett A 349:494–499CrossRef
23.
Raja R, Marshal Anthoni S (2011) Global exponential stability of BAM neural networks with time-varying delays: the discrete-time case. Commun Nonlinear Sci Numer Simul 16:613–622CrossRefMATHMathSciNet
24.
Samidurai R, Sakthivel R, Anthoni SM (2009) Global asymptotic stability of BAM neural networks with mixed delays and impulses. Appl Math Comput 212:113–119CrossRefMATHMathSciNet
25.
Sakthivel R, Samidurai R, Marshal Anthoni S (2010) Exponential stability for stochastic neural networks of neutral type with impulsive effetcs. Modern Phys Lett B 24:1099–1110CrossRefMATHMathSciNet
26.
Sakthivel R, Samidurai R, Marshal Anthoni S (2010) Asymptotic stability of stochastic delayed recurrent neural networks with impulsive effects. J Optim Theory Appl 147(3):583–596CrossRefMATHMathSciNet
27.
Sakthivel R, Samidurai R, Marshal Anthoni S (2010) New exponential stability criteria for stochastic BAM neural networks with impulses. Physica Scripta 82:045802CrossRef
28.
Song Q, Cao JD (2007) Dynamics of bidirectional associative memory networks with distributed delays and reaction-diffusion terms. Nonlinear Anal Real World Appl 8:345–361CrossRefMATHMathSciNet
29.
Sarlin P (2012) Visual tracking of the millennium development goals with a fuzzified self-organizing neural network. Int J Mach Learn Cybern 3(3):233–245CrossRef
30.
Song Q, Wang Z (2007) A delay-dependent LMI approach to dynamics analysis of discrete-time recurrent neural networks with time-varying delays. Phys Lett A 368:134–145CrossRef
31.
Zhang BY, Xu SY, Zou Y (2008) Improved delay-dependent exponential stability criteria for discrete-time recurrent neural networks with time-varying delays Neurocomputing 72:321–330CrossRef
32.
Zhao H (2002) Global stability of bidirectional associative memory neural networks with distributed delays. Phys Lett A 297:182–190CrossRefMATHMathSciNet
33.
Zhou Q (2009) Global exponential stability of BAM neural networks with distributed delays and impulses. Nonlinear Anal Real World Appl 10:144–153CrossRefMATHMathSciNet
34.
Zhou Q, Wan L (2009) Impulsive effects on stability of Cohen–Grossberg-type bidirectional associative memory neural networks with delays. Nonlinear Anal Real World Appl 10:25–31MathSciNet
Metadaten
Titel
Dynamic analysis of discrete-time BAM neural networks with stochastic perturbations and impulses
verfasst von
R. Raja
U. Karthik Raja
R. Samidurai
A. Leelamani
Publikationsdatum
01.02.2014
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 1/2014
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-013-0199-8

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