Exponential synchronization of discrete-time mixed delay neural networks with actuator constraints and stochastic missing data

This paper investigates the problem of exponential synchronization of discrete-time neural networks with mixed time delays, actuator saturation and failures. Meanwhile, the unreliable communication links are considered between the neural networks, and such unreliable links are modeled as stochastic...

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Bibliographic Details
Published in:Neurocomputing (Amsterdam) 2016-09, Vol.207, p.700-707
Main Authors: Li, Jian-Ning, Bao, Wen-Dong, Li, Shi-Bao, Wen, Cheng-Lin, Li, Lin-Sheng
Format: Article
Language:English
Subjects:
Actuator constraints
Missing data
Mixed delay
Neural networks
Synchronization
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Summary:This paper investigates the problem of exponential synchronization of discrete-time neural networks with mixed time delays, actuator saturation and failures. Meanwhile, the unreliable communication links are considered between the neural networks, and such unreliable links are modeled as stochastic missing data satisfying Bernoulli distributions. In order to show the relationships between actuator constraints, unreliable communication link and mixed delay neural networks, by using Lyapunov functional approach, a missing data probability dependent exponential synchronization criterion is given. Then, based on such criterion, a reliable controller is designed to ensure that the neural networks are exponentially synchronized in the mean square. Finally, a numerical example is provided to illustrate the effectiveness of the proposed approach.
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2016.05.056