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相关概念视频

Residuals and Least-Squares Property01:11

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The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
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Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
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Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
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系统识别应用程序的强大数据重复使用规范化的递归最小方程算法

Radu-Andrei Otopeleanu1,2, Constantin Paleologu1, Jacob Benesty3

  • 1Department of Telecommunications, National University of Science and Technology POLITEHNICA Bucharest, 060042 Bucharest, Romania.

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概括
此摘要是机器生成的。

这项研究介绍了一种计算效率高的数据重用技术,用于规范化的递归最小方程 (RLS) 算法. 增强的自适应过方法可以在噪音环境等具有挑战性的条件下提高合性和稳定性.

关键词:
适应性过器数据重复使用声响取消递归最小平方算法 (RLS)规范化工作坚固性系统识别

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科学领域:

  • 信号处理
  • 适应性过
  • 系统识别

背景情况:

  • 递归最小方程 (RLS) 算法对自适应过和系统识别有效.
  • RLS提供快速融合,但在噪音条件下可能缺乏稳定性.
  • 收和稳定性往往是相互矛盾的绩效标准.

研究的目的:

  • 为规范化的RLS算法开发一个计算效率高的数据再利用技术.
  • 在杂的环境中增强RLS算法的稳定性.
  • 为了实现融合率和稳定性之间的妥协.

主要方法:

  • 使用数据再利用技术实现规范化的RLS算法.
  • 用同等的单步数据重复使用来提高计算效率.
  • 具有时间依赖的规范化参数的可变规范化算法的参与.
  • 在回声取消应用程序中测试算法.

主要成果:

  • 开发的数据重复使用规范化的RLS算法显示出可靠的性能.
  • 这种方法在融合和稳定性之间提供了良好的妥协.
  • 在具有挑战性的条件下,包括杂的环境中实现有效控制.

结论:

  • 拟议的计算效率高的数据重复使用规范化的 RLS 算法提供了更好的性能.
  • 这些算法适用于自适应过应用,特别是在回声取消中.
  • 这些发现支持了加强RLS系统识别方法的理论优势.