Adaptive exponential synchronization in mean square for Markovian jumping neutral-type coupled neural networks with time-varying delays by pinning control
Adaptive exponential synchronization in mean square for Markovian jumping neutral-type coupled neural networks with time-varying delays by pinning control
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DOI：
10.1016/j.neucom.2015.08.034
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发表时间：
2016-01
期刊：
影响因子：
6
通讯作者：
Anding Dai;Wuneng Zhou;Yuhua Xu;Cuie Xiao
中科院分区：
文献类型：
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作者：
Anding Dai;Wuneng Zhou;Yuhua Xu;Cuie Xiao
研究了具有马尔可夫切换参数的中立型耦合神经网络的自适应指数同步问题。开关参数被建模为一个连续的时间，有限状态马尔可夫链。基于李雅普诺夫稳定性理论、随机分析和矩阵理论，给出了均方指数同步的充分条件。在部分节点上添加自适应控制器，自适应律依赖于马尔可夫链和误差状态。两个数值例子说明了理论结果的有效性。通过比较同步控制代价和同步时间的平均值，验证了当网络拓扑结构按马尔可夫链进行切换时，控制不同节点可能比控制固定节点更有效地实现同步。
In this paper, the adaptive exponential synchronization problem of neutral-type coupled neural networks with Markovian switching parameters is investigated. The switching parameters are modeled as a continuous time, finite state Markov chain. Based on Lyapunov stability theory, stochastic analysis and matrix theory, some sufficient conditions for exponential synchronization in mean square are derived. The adaptive controllers are added to part of nodes, and the adaptive laws are depend on Markov chain and error states. Two numerical examples are exhibited to illustrate the validity of the theoretical results. Through the comparison of average value of synchronization control cost and synchronization time, we verify that control different nodes may be more effectively to achieve synchronization than control fixed nodes when the network topology is switching by a Markov chain.