School of Electrical and Automation Engineering, Tianjin University, 300072, Tianjin, People's Republic of China.
Chaos. 2011 Mar;21(1):013109. doi: 10.1063/1.3539815.
The changes of parameters and topology in a complex network often lead to unexpected accidents in complex systems, such as diseases in neural systems and unexpected current in circuit system, so the methods of adjusting the abnormal network back to its normal conditions are necessary to avoid these problems. However, it is not easy to detect the structures and information of each network, even if we can find a network which has the same function as the abnormal network, it is still hard to use it as a reference to adjust the abnormal network because a lot of network information is unknown. In this paper, we design a "bridging network" as an information bridge between a normal network and an abnormal network to estimate and control the abnormal network. Through the "bridging network" and some adaptive laws, the abnormal parameters and connections in abnormal network can be adjusted to the same conditions as those of the normal network which is chosen as a reference model. Finally, the "bridging network" and the abnormal network achieve synchronization with the normal network. Besides, the detailed inner information in normal network and abnormal network can be accurately estimated by this "bridging network." Finally, the nodes in the abnormal network will behave normally after the correction. In this paper, we use Hindmarsh-Rose model as an example to describe our method.
复杂网络中的参数和拓扑结构的变化往往会导致复杂系统中的意外事故，例如神经系统中的疾病和电路系统中的意外电流，因此需要调整异常网络使其恢复正常状态的方法来避免这些问题。然而，检测每个网络的结构和信息并不容易，即使我们能够找到一个具有与异常网络相同功能的网络，也很难将其用作调整异常网络的参考，因为许多网络信息是未知的。在本文中，我们设计了一个“桥接网络”作为正常网络和异常网络之间的信息桥梁，以估计和控制异常网络。通过“桥接网络”和一些自适应律，可以将异常网络中的异常参数和连接调整到与所选参考模型的正常网络相同的条件。最后，“桥接网络”和异常网络与正常网络实现同步。此外，通过这个“桥接网络”可以准确估计正常网络和异常网络中的详细内部信息。最后，异常网络中的节点在纠正后会正常运行。本文以 Hindmarsh-Rose 模型为例来说明我们的方法。
