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作 者:亢春艳[1]
机构地区:[1]天津广播电视大学教学资源管理与建设处,天津300191
出 处:《控制工程》2014年第6期969-972,共4页Control Engineering of China
基 金:天津市教育科学"十二五"规划课题"远程教育环境下基于内容感知的移动学习模式研究"(课题批准号CEYP6001)阶段性研究成果之一
摘 要:在移动自组织网络中,需要通过移动学习控制,全面监控移动计算设备和网络上的各种应用进程和学习进程,提高网络通信性能和学习交互能力。传统的移动自组织网络学习控制算法采用基于半监督学习的移动学习控制算法,半监督学习方法对网络自组织特征的检测率较高,但对数据的内容感知性能不好。提出一种基于内容感知的同频率震荡移动学习控制算法。构建移动自组织网络模型,结合协同量子群算法,模仿粒子的学习行为,设计同频震荡移动学习控制策略,通过创建种群基因库实现了子群间的信息共享,实现对学习内容的最优适应度感知,实现控制算法的改进。仿真结果表明,该控制算法能通过学习内容的感知,结合协同搜索策略与粒子学习行为,实现了学习过程的同频率震荡控制,达到收敛速度与收敛精度间的平衡区域平衡,具有更良好的搜索性能,具有较好的移动学习控制能力和寻优能力。In a mobile ad hoc network, need to mobile learning control, for comprehensive monitoring of mobile computing devices and networks of various application in the learning process, improve network communication performance and interaction of learning ability. The traditional mobile ad hoc networks learning control algorithm used semi supervised learning control algorithm based on mobile learn- ing, semi supervised learning method for detection of network self organizing feature rate is higher, but the content of perceptual per- formance data is not good. A same frequency oscillation Mobile Learning control algorithm based on content aware is proposed. Mobile ad hoc network model is constructed, combined with cooperative quantum group learning algorithm, the behavior of particles is simula- ted, the same frequency oscillation control strategy for mobile learning is designed, sharing by creating a population of gene library im- plements the information between the subgroups, achieve optimal learning content to the fitness awareness. The control algorithm is im- proved. The simulation results show that the control algorithm can 1earn the content of perception, combined with cooperative search strategy and particle learning behavior, to achieve the balance of regional balance convergence, convergence precision has better per- formance, it has good mobile learning control ability and searching ability.
分 类 号:TP393[自动化与计算机技术—计算机应用技术]
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