Partial stabilization of stochastic hybrid neural networks driven by Levy noise

被引:3
|
作者
Liu, Dezhi [1 ,3 ]
Wang, Zecheng [2 ]
Zhang, Zizhen [2 ]
Liu, Jia [1 ]
机构
[1] Anhui Univ Finance & Econ, Sch Stat & Appl Math, Bengbu, Anhui, Peoples R China
[2] Anhui Univ Finance & Econ, Sch Management Sci & Engn, Bengbu, Anhui, Peoples R China
[3] Shandong Univ, Sch Control Sci & Engn, Jinan, Shandong, Peoples R China
关键词
Neural networks; stabilization; destabilization; Markov chain; Levy process; almost surely exponential stable; DIFFERENTIAL-EQUATIONS; STABILITY; SYSTEMS; DELAY;
D O I
10.1080/21642583.2020.1788470
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The paper tackles the stabilization of stochastic hybrid neural networks. Different from the existing work which the Brownian motion was used to stabilize neural networks for the stochastic stability, the stabilization is generalized by Levy noise in this paper. To address the unobservable items in the hybrid neural networks, we introduce the Markov chain into the hybrid neural networks and divide the state space of Markov chain into two subsets for observable and unobservable items. Then, the sufficient conditions of stabilization and destabilization for the stochastic neural networks are obtained.
引用
收藏
页码:413 / 421
页数:9
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