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

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...

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Veröffentlicht in:Physica A 2020-05, Vol.545, p.123782, Article 123782
Hauptverfasser: Tan, Jianguo, Tan, Yahua, Guo, Yongfeng, Feng, Jianfeng
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Sprache:eng
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Zusammenfassung: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. •The Euler method and backward Euler method are used to the model.•The almost sure exponential stability of numerical solutions is investigated.•Our main research tool is the discrete semimartingale convergence theorem.•Our main result is illustrated by an example in this paper.
ISSN:0378-4371
1873-2119
DOI:10.1016/j.physa.2019.123782