期刊
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
卷 33, 期 9, 页码 4491-4501出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2021.3057490
关键词
Synchronization; Biological neural networks; Neurons; Symmetric matrices; Topology; Tools; Stochastic processes; Impulse; pinning control; stochastic neural networks; synchronization; uniformly stable function
类别
资金
- National Science Foundation of China [61973177, 61833005]
- National Key Research and Development Project of China [2020YFA0714301]
- Natural Science Foundation of Guangdong Province of China [2015A030310425, 2019A1515011031]
- Natural Science Foundation of Henan Province of China [212102210142]
This article investigates the synchronization of stochastic delayed neural networks under pinning impulsive control, proposing new mean square decay results and analysis methods, discussing system behavior in different scenarios, and extending the flexibility of impulsive gains.
This article investigates the synchronization of stochastic delayed neural networks under pinning impulsive control, where a small fraction of nodes are selected as the pinned nodes at each impulsive moment. By proposing a uniformly stable function as a new tool, some novel mean square decay results are presented to analyze the error system obtained from the leader and the considered neural networks. For the divergent error system without impulsive effects, the impulsive gains of pinning impulsive controller can admit destabilizing impulse and the number of destabilizing impulse may be infinite. However, if the error system without impulsive effects is convergent, to achieve the synchronization of the stochastic neural networks, the growth exponent of the product of impulsive gains can not exceed some positive constant. It is shown that the obtained results increase the flexibility of the impulsive gains compared with the existing results. Finally, a numerical example is given to illustrate the practicality of synchronization criteria.
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