Optimization-based topology identification of complex networks

In many cases, the topological structures of a complex network are unknown or uncertain, and it is of significance to identify the exact topological structure. An optimization-based method of identifying the topological structure of a complex network is proposed in this paper. Identification of the exact network topological structure is converted into a minimal optimization problem by using the estimated network. Then, an improved quantum-behaved particle swarm optimization algorithm is used to solve the optimization problem. Compared with the previous adaptive synchronization-based method, the proposed method is simple and effective and is particularly valid to identify the topological structure of synchronization complex networks. In some cases where the states of a complex network are only partially observable, the exact topological structure of a network can also be identified by using the proposed method. Finally, numerical simulations are provided to show the effectiveness of the proposed method.     

关联复杂网络建模及辨识研究

运用异质耦合拆分方法和驱动-响应模型,提出关联复杂网络节点参数和拓扑结构的辨识方法.首先,研究异质关联复杂网络建模方法,进而依据网络耦合性质不同,拆分构造了两类异质关联复杂网络.然后运用驱动-响应模型、LaSalle不变原理和Gram矩阵,设计节点系统参数和拓扑参数的自适应辨识观测器.所提的观测器能在线获取网络的节点参数、不同耦合性质的拓扑参数.最后,通过数值仿真验证所提方法的有效性.