Exponential Stability of Numerical Solutions to a Stochastic Delay Neural Networks

Qi Min Zhang

doi:10.4028/www.scientific.net/AMR.219-220.1035

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 219-220Exponential Stability of Numerical Solutions to a...

Exponential Stability of Numerical Solutions to a Stochastic Delay Neural Networks

Abstract:

The main purpose of this paper is to develop a numerical Euler scheme and show the convergence of the numerical approximation solution to the true solution for stochastic delay neural networks. The definition of exponential mean square stability of numerical method is introduced. It is proved that the Euler scheme is exponentially stable in mean square sense. An example is given for illustration our result.

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Periodical:

Advanced Materials Research (Volumes 219-220)

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1035-1039

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.219-220.1035

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Online since:

March 2011

Authors:

Qi Min Zhang

Keywords:

Euler Approximation, Numerical Solution, Stochastic Delay Neural Networks

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References

[1] G. Chen, J. Zhou, Z. Liu, Global synchronization of coupled delayed neural networks and applications to chaotic CNN model, Int. J. Bifur. Chaos 14(2004) 2229-2240.

DOI: 10.1142/s0218127404010655

Google Scholar

[2] M. Gilli, Strange attractors in delayed cellular neural networks, IEEE Trans. Circuits Syst. I 40 (1993) 849-853.

DOI: 10.1109/81.251826

Google Scholar

[3] H. Lu, Chaotic attractors in delayed neural networks,Phys. Lett. A 298 (2002) 109-116.

Google Scholar

[4] X. Liao, X. Mao, Exponential stability and instability of stochastic neural networks, Stochast. Anal. Appl. 14 (1996)165-185.

Google Scholar

[5] S. Blythea, X. Mao, X. Liao, Stability of stochastic delay neural networks, J. Franklin Inst. 338 (2001) 481-495.

DOI: 10.1016/s0016-0032(01)00016-3

Google Scholar

[6] Li Ronghua, Meng Hongbing, Chang Qin. Exponential Stability of Numerical Solutions to SDDEs with Markovian Switching[J]. Applied Mathematics and Computation. 2006,174(2):1302-1313.

DOI: 10.1016/j.amc.2005.05.037

Google Scholar

[7] X. Mao, S. Sabanis, Numerical solutions of stochastic differential delay equtions under local Lipschitz condition [J],J. Computat. Appl. Math. 151 (2003) 215-227.

DOI: 10.1016/s0377-0427(02)00750-1

Google Scholar

[8] Steve Blythe, Xuerong Mao, Xiaoxin Liao. Stability of stochastic delay neural networks[J]. Journal of the Franklin Institute 338 (2001) 481-495.

DOI: 10.1016/s0016-0032(01)00016-3

Google Scholar