Attractor and Stochastic Boundedness for Stochastic Infinite Delay Neural Networks with Markovian Switching
Attractor and Stochastic Boundedness for Stochastic Infinite Delay Neural Networks with Markovian Switching
复制标题
DOI：
10.1007/s11063-013-9314-9
复制
发表时间：
2014-10
影响因子：
3.1
通讯作者：
Dingshi Li;Chao Ma
中科院分区：
文献类型：
--
作者：
Dingshi Li;Chao Ma
首先，我们建立了具有马尔可夫开关的随机无穷时滞微分方程的随机LaSalle定理，并由此得到吸引性的一些判据。然后，利用李雅普诺夫方法和建立的LaSalle型定理，得到了马尔可夫切换随机无穷时滞神经网络吸引子和随机有界的充分条件.最后，通过一个例子说明了所得结果的有效性。
First, we establish the stochastic LaSalle theorem for stochastic infinite delay differential equations with Markovian switching, from which some criterias on attraction are obtained. Then, by employing Lyapunov method and LaSalle-type theorem established above, we obtain some sufficient conditions ensuring the attractor and stochastic boundedness for stochastic infinite delay neural networks with Markovian switching. Finally, an example is also discussed to illustrate the efficiency of the obtained results.