Exponential Synchronization for Markovian Stochastic Coupled Neural Networks of Neutral-Type via Adaptive Feedback Control
Exponential Synchronization for Markovian Stochastic Coupled Neural Networks of Neutral-Type via Adaptive Feedback Control
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DOI：
10.1109/tnnls.2016.2546962
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发表时间：
2017-07
影响因子：
10.4
通讯作者：
Huabin Chen;P. Shi;C. Lim
中科院分区：
文献类型：
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作者：
Huabin Chen;P. Shi;C. Lim
本文研究了具有时变时滞和随机耦合强度的中立型$ {N}$全同马尔可夫随机耦合神经网络的自适应指数同步问题.首先建立了中立型变时滞随机马尔可夫系统均方指数稳定的广义李雅普诺夫定理。假设系统中的时变时滞是一个有界可测函数。然后，在自适应反馈控制器下，给出了保证底层系统均方指数同步的充分条件，这些条件由$\mathcal {M}$ -矩阵和代数不等式给出。在相同的条件下，还得到了系统的几乎必然指数同步。最后给出了一个数值算例，验证了理论结果的有效性和潜力.
In this paper, we investigate the adaptive exponential synchronization in both the mean square and the almost sure senses for an array of $ {N}$ identical Markovian stochastic coupled neural networks of neutral-type with time-varying delay and random coupling strength. The generalized Lyapunov theorem of the exponential stability in the mean square for the neutral stochastic Markov system with the time-varying delay is first established. The time-varying delay in the system is assumed to be a bounded measurable function. Then, sufficient conditions to guarantee the exponential synchronization in the mean square for the underlying system are developed under an adaptive feedback controller, which are given in terms of the $\mathcal {M}$ -matrix and the algebraic inequalities. Under the same conditions, the almost sure exponential synchronization is also presented. A numerical example is given to show the effectiveness and potential of the proposed theoretical results.