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

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 disturba...

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Bibliographic Details
Published in:Complexity (New York, N.Y.) N.Y.), 2019, Vol.2019 (2019), p.1-20
Main Authors: Xu, Xiaohui, Xu, Yanhai, Yang, Jibin
Format: Article
Language:English
Subjects:
Computer simulation
Control systems
Economic models
Liapunov functions
Mean square values
Neural networks
Stability
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Summary: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.
ISSN:1076-2787
1099-0526
DOI:10.1155/2019/3429326