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.
A novel grain boundary(GB) model characterized with different angles and positions in the nanowire was set up.By means of device simulator,the effects of grain boundary angle and location on the electrical performance of ZnO nanowire FET(Nanowire Field-Effect Transistor) with a wrap-around gate configuration,were explored.With the increase of the grain boundary angle,the electrical performance degrades gradually.When a grain boundary with a smaller angle,such as 5° GB,is located close to the source or drain electrode,the grain boundary is partially depleted by an electric field peak,which leads to the decrease of electron concentration and the degradation of transistor characteristics.When the 90° GB is located at the center of the nanowire,the action of the electric field is balanced out,so the electrical performance of transistor is better than that of the 90° GB located at the other positions.
