Convergence of Numerical Solutions to Stochastic Delay Neural Networks with Poisson Jump and Markovian Switching

Abstract:

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In general stochastic delay neural networks with Poisson jump and Markovian switching do not have explicit solutions. Appropriate numerical approximations, such as the Euler scheme, are therefore a vital tool in exploring their properties. Unfortunately, the numerical methods for stochastic delay neural networks with Poisson jump and Markovian switching (SDNNwPJMSs) have never been studied. In this paper we proved that the Euler approximate solutions will converge to the exact solutions for SDNNwPJMSs under local Lipschitz condition. This result is more general than what they deal with the Markovian switching term or the jump term.

Info:

Periodical:

Advanced Materials Research (Volumes 219-220)

Edited by:

Helen Zhang, Gang Shen and David Jin

Pages:

1153-1157

DOI:

10.4028/www.scientific.net/AMR.219-220.1153

Citation:

H. G. Yue and Q. M. Zhang, "Convergence of Numerical Solutions to Stochastic Delay Neural Networks with Poisson Jump and Markovian Switching", Advanced Materials Research, Vols. 219-220, pp. 1153-1157, 2011

Online since:

March 2011

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$35.00

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