Almost sure exponential stability of hybrid stochastic delayed Cohen-Grossberg neural networks

作者：Yu, Peilin; Cheng, Pei^*; Deng, Feiqi

来源：ASIAN JOURNAL OF CONTROL, 2022, 24(4): 1904-1915.

DOI：10.1002/asjc.2591

• almost sure exponential stability
• Brownian motion
• Cohen-Grossberg neural network
• linear matrix inequality
• Markov chain

Benefit from the significant work of Song and Mao (2018), this paper focuses on the almost sure exponential stability of hybrid stochastic delayed Cohen-Grossberg neural network (SDCGNN) with the nonlinear disturbance. By virtue of stationary distribution, Lyapunov stability theory and LMI tool, we will propose that if the corresponding delay-free stochastic neural network is almost surely exponentially stable, then there exists a positive number tau* such that the SDCGNN is also almost surely exponentially stable so long as tau < tau*. Finally, an example is given to verify the effectiveness of our results, and an implicit lower bound for tau* is provided.