Department of Mathematics, Research Center for Complex Systems and Network Sciences, Southeast University, Nanjing, China.
IEEE Trans Neural Netw Learn Syst. 2017 Nov;28(11):2638-2647. doi: 10.1109/TNNLS.2016.2598730.
In this paper, the synchronization problem of master-slave chaotic neural networks with remote sensors, quantization process, and communication time delays is investigated. The information communication channel between the master chaotic neural network and slave chaotic neural network consists of several remote sensors, with each sensor able to access only partial knowledge of output information of the master neural network. At each sampling instants, each sensor updates its own measurement and only one sensor is scheduled to transmit its latest information to the controller's side in order to update the control inputs for the slave neural network. Thus, such communication process and control strategy are much more energy-saving comparing with the traditional point-to-point scheme. Sufficient conditions for output feedback control gain matrix, allowable length of sampling intervals, and upper bound of network-induced delays are derived to ensure the quantized synchronization of master-slave chaotic neural networks. Lastly, Chua's circuit system and 4-D Hopfield neural network are simulated to validate the effectiveness of the main results.In this paper, the synchronization problem of master-slave chaotic neural networks with remote sensors, quantization process, and communication time delays is investigated. The information communication channel between the master chaotic neural network and slave chaotic neural network consists of several remote sensors, with each sensor able to access only partial knowledge of output information of the master neural network. At each sampling instants, each sensor updates its own measurement and only one sensor is scheduled to transmit its latest information to the controller's side in order to update the control inputs for the slave neural network. Thus, such communication process and control strategy are much more energy-saving comparing with the traditional point-to-point scheme. Sufficient conditions for output feedback control gain matrix, allowable length of sampling intervals, and upper bound of network-induced delays are derived to ensure the quantized synchronization of master-slave chaotic neural networks. Lastly, Chua's circuit system and 4-D Hopfield neural network are simulated to validate the effectiveness of the main results.
本文研究了具有远程传感器、量化过程和通信时滞的主从混沌神经网络的同步问题。主混沌神经网络和从混沌神经网络之间的信息通信通道由几个远程传感器组成，每个传感器只能访问主神经网络输出信息的部分知识。在每个采样时刻，每个传感器更新自己的测量值，并且只有一个传感器被安排向控制器一侧传输其最新信息，以便更新从神经网络的控制输入。因此，与传统的点对点方案相比，这种通信过程和控制策略更加节能。推导出了输出反馈控制增益矩阵、允许的采样间隔长度和网络诱导延迟的上界的充分条件，以确保主从混沌神经网络的量化同步。最后，通过 Chua 电路系统和 4-D Hopfield 神经网络的仿真验证了主要结果的有效性。
