Almost sure exponential stability of numerical solutions for stochastic delay Hopfield neural networks with jumps

Jianguo Tan, Yahua Tan, Yongfeng Guo and Jianfeng Feng

Physica A: Statistical Mechanics and its Applications, 2020, vol. 545, issue C

Abstract: In this paper, we main investigate the almost sure exponential stability of stochastic delay Hopfield neural networks with jumps on numerical solutions. The methods we used are Euler approach and backward Euler approach. By giving some conditions of theoretical significance, we verify that not only Euler approach but also backward Euler approach is almost sure exponential stability. However, the range of application of Euler approach is smaller than that of backward Euler approach. Moreover, our main research tool is the discrete semimartingale convergence theorem. Lastly, we give an example as illustration.

Keywords: Stochastic delay Hopfield neural networks with jumps; Euler approach; Backward Euler approach; Almost sure exponential stability; Discrete semimartingale convergence theorem (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:545:y:2020:i:c:s0378437119321065

DOI: 10.1016/j.physa.2019.123782

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