ID 原文 译文
47166 在分析混沌族群系统的演化方法以及混沌族群系统的统一同步问题的基础上，构建三维空间混沌系统的转动模型。 Based on theanalysis on the evolution method and the unification-synchronization problem of the chaotic group system, a rotationmodel of 3D chaotic system was established.
47167 以 Newton-Leipnik 系统为研究对象，利用 3 路不同的通信信号，对混沌族群的演化、混沌族群系统的统一同步进行了仿真和验证。 With the Newton-Leipnik system as study object, three different types ofcommunication signals to simulate and validate the evolution of chaotic group, and the unification and synchronization ofthe chaotic group system was utilized.
47168 实验结果证明了该方法的有效性，应用前景良好。 Experimental results show that the method presented in this study is effective andhas a good prospect for the application.
47169 相比传统数据路由的数据采集技术，无线移动节点技术逐步成为近年来无线传感网中数据采集的另一种新技术。 Comparing to the traditional data collecting method with data route, the technology of wireless mobile nodes hasgradually became a new technique in the wireless sensor network.
47170 由于其中对静态节点遍历次序的求解本身是一个 NP 难问题，提出了一种更为通用的基于多移动节点的多目标数据采集策略， As the solution to the visiting order of the static nodes wasan intrinsic NP-hard problem, a more general multi-objective data colleting strategies based on multi-mobile nodes was pro-posed.
47171 将此问题建模为一种时变多旅行商问题模型。 The proposed data collecting technique was abstracted as a model of time variable multiple traveling salesman prob-lem.
47172 考虑到其属于 NP 难的离散优化问题模型，设计了一种针对问题特点的混合遗传算法来求解多个移动节点的规划路径， Belonging to a discrete optimal problem, the proposed model was solved by with a proposed hybrid genetic algorithm todetermine the paths of the multi-mobile nodes.
47173 并对设计的算法给出了收敛性证明。 The convergence analysis of the proposed algorithm was given.
47174 通过对公开数据集的测试证实，所提基于多移动节点采集数据的时变旅行商问题模型和设计的求解算法确实能有效地提高数据采集的效率和实时性。 With the ex-periment of open dataset, the proposed model based on the time variable multiple traveling salesman problem and the pro-posed hybrid genetic algorithm certify a certain improvement to the efficiency and real-time ability.
47175 数据中不确定性的存在使对其聚类分析时要充分考虑不确定性的影响。 The effect of the uncertainties needs to be taken full advantage during uncertain data clustering.