...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Discrete and continuous dynamical systems >BIG DATA DYNAMIC COMPRESSIVE SENSING SYSTEM ARCHITECTURE AND OPTIMIZATION ALGORITHM FOR INTERNET OF THINGS
【24h】

BIG DATA DYNAMIC COMPRESSIVE SENSING SYSTEM ARCHITECTURE AND OPTIMIZATION ALGORITHM FOR INTERNET OF THINGS

机译：物联网的大数据动态压缩传感系统架构与优化算法

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

In order to reduce the amount of data collected in the Internet of things, to improve the processing speed of big data. To reduce the collected data from Internet of Things by compressed sensing sampling method is proposed. To overcome high computational complexity of compressed sensing algorithms, the search terms of the gradient projection sparse reconstruction algorithm (GPSR-BB) are improved by using multi-objective optimization particle swarm optimization algorithm; it can effectively improve the reconstruction accuracy of the algorithm. Application results show that the proposed multi-objective particle swarm optimization-Genetic algorithm (MOPSOGA) is than traditional GPSR-BB algorithm iterations decreased 51.6%. The success rate of reconstruction is higher than that of the traditional algorithm of 0.15; it's with a better reconstruction performance.
机译：为了减少在物联网中收集的数据量，提高大数据的处理速度。为了减少物联网的数据收集，提出了一种压缩感知采样方法。为了克服压缩感知算法的高计算复杂度，采用多目标优化粒子群算法对梯度投影稀疏重建算法（GPSR-BB）的搜索项进行了改进。可以有效提高算法的重构精度。应用结果表明，提出的多目标粒子群优化遗传算法（MOPSOGA）比传统的GPSR-BB算法迭代减少了51.6％。重建成功率高于传统算法0.15。具有更好的重建性能。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号