Mean square exponential stability of discrete-time Markov switched stochastic neural networks with partially unstable subsystems and mixed delays

2021 ◽  
Vol 580 ◽  
pp. 243-259
Author(s):  
Lina Fan ◽  
Quanxin Zhu
Keyword(s):  
Neural Networks ◽  
Exponential Stability ◽  
Discrete Time ◽  
Stochastic Neural Networks ◽  
Mixed Delays ◽  
Mean Square ◽  
Unstable Subsystems ◽  
Mean Square Exponential Stability

scholarly journals Mean Square Exponential Stability of Stochastic Complex-Valued Neural Networks with Mixed Delays

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-20 ◽  
Author(s):  
Xiaohui Xu ◽  
Jibin Yang ◽  
Yanhai Xu
Keyword(s):  
Neural Networks ◽  
Exponential Stability ◽  
Sufficient Conditions ◽  
Mixed Delays ◽  
Mean Square ◽  
Integral Theorem ◽  
Stochastic Disturbance ◽  
Mean Square Exponential Stability ◽  
The Mean ◽  
Complex Valued

This paper investigates the mean square exponential stability problem of a class of complex-valued neural networks with stochastic disturbance and mixed delays including both time-varying delays and continuously distributed delays. Under different assumption conditions concerning stochastic disturbance term from the existing ones, some sufficient conditions are derived for assuring the mean square exponential stability of the equilibrium point of the system based on the vector Lyapunov function method and Ito^ differential-integral theorem. The obtained results not only generalize the existing ones, but also reduce the conservatism of the previous stability results about complex-valued neural networks with stochastic disturbances. Two numerical examples with simulation results are given to verify the feasibility of the proposed results.

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