东篱科研大数据发现系统（DRDS）

位置：成果数据库 > 期刊 > 期刊详情页

MIMO-RFID系统反向识别距离研究

ISSN号：1000-7105
期刊名称：电子测量与仪器学报
时间：2012
页码：305-311
分类：TP393[自动化与计算机技术—计算机应用技术;自动化与计算机技术—计算机科学与技术] O224[理学—运筹学与控制论;理学—数学]
作者机构：[1]Province-Ministry Joint Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability, Hebei University of Technology, Tianjin 300130, China, [2]College of Electrical and Information Engineering, Hunan University, Changsha 410082, China
相关基金：Project supported by the National Natural Science Foundation for Distinguished Young Scholars of China （Grant No. 50925727） and the National Natural Science Foundation of China （Grant No. 60876022）.
相关项目：复杂电网络分析、综合与诊断

关键词：网络拓扑结构, 优化问题, 识别, 粒子群优化算法, 网络转换, 量子行为, 同步方法, 网络同步, complex networks, topology identification, optimization, particle swarm

中文摘要：

<正>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.

英文摘要：

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.

同期刊论文项目