Journal
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
Volume 30, Issue 11, Pages 3246-3259Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2018.2890269
Keywords
Synchronization; Delays; Adaptive control; Neural networks; Power system stability; Stability analysis; Delay systems; Cluster synchronization; neutral stochastic coupled neural networks; periodically intermittent pinning adaptive control; stability; time-varying delay
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Funding
- National Natural Science Foundation of China [61773131, 61364005, 11401292, U1509217]
- Australian Research Council [DP170102644]
- Natural Science Foundation of Jiangxi Province of China [20171BAB201007, 20171BCB23001]
- Foundation of Jiangxi Provincial Educations of China [GJJ14155]
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This paper studies the problem of cluster synchronization at exponential rates in both the mean square and almost sure senses for neutral stochastic coupled neural networks with time-varying delay via a periodically intermittent pinning adaptive control strategy. The network topology can be symmetric or asymmetric, with each network node being described by neutral stochastic delayed neural networks. When considering the exponential stabilization in the mean square sense for neutral stochastic delay system, the delay integral inequality approach is used to circumvent the obstacle arising from the coexistence of random disturbance, neutral item, and time-varying delay. The almost surely exponential stabilization is also analyzed with the nonnegative semimartingale convergence theorem. Sufficient criteria on cluster synchronization at exponential rates in both the mean square and almost sure senses of the underlying networks under the designed control scheme are derived. The effectiveness of the obtained theoretical results is illustrated by two examples.
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