Finite-time synchronization control for uncertain Markov jump neural networks with input constraints
Finite-time synchronization control for uncertain Markov jump neural networks with input constraints
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DOI：
10.1007/s11071-014-1412-3
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发表时间：
2014-04
影响因子：
5.6
通讯作者：
Hao Shen;Ju H. Park;Zhengguang Wu
中科院分区：
文献类型：
--
作者：
Hao Shen;Ju H. Park;Zhengguang Wu
研究了控制输入幅值受限的不确定Markov跳变神经网络的有限时间同步控制问题。所考虑的参数不确定性被假定为属于一个固定的凸多面体。利用参数相关的李雅普诺夫泛函和一种简单的矩阵解耦方法，给出了保证所考虑的网络在有限时间区间内随机同步的充分条件.所需的模式无关的控制器参数可以通过求解凸优化问题来计算。最后，两个混沌神经网络的应用证明了我们所提出的方法的有效性。
This paper is concerned with the problem of finite-time synchronization control for uncertain Markov jump neural networks in the presence of constraints on the control input amplitude. The parameter uncertainties under consideration are assumed to belong to a fixed convex polytope. By using a parameter-dependent Lyapunov functional and a simple matrix decoupling method, a sufficient condition is proposed to ensure that the considered networks are stochastically synchronized over a finite-time interval. The desired mode-independent controller parameters can be computed via solving a convex optimization problem. Finally, two chaos neural networks are employed to demonstrate the effectiveness of our proposed approach.