• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>International Conference on Microelectronics >Performance analysis of kernel adaptive filters based on RLS algorithm
【24h】

Performance analysis of kernel adaptive filters based on RLS algorithm

机译：基于RLS算法的内核自适应滤波器性能分析

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The design of adaptive nonlinear filters has sparked a great interest in the machine learning community. The present paper aims to present some recent developments in nonlinear adaptive filtering. We present an in-depth analysis of the performance and complexity of a class of kernel filters based on the recursive least-squares algorithm. A key feature that underlies kernel algorithms is that they map the data in a high-dimensional feature space where linear filtering is performed. The arithmetic operations are carried out in the initial space via evaluation of inner products between pairs of input patterns called kernels. We evaluated the SNR improvement and the convergence speed of kernel-based recursive least-squares filters on two types of applications: time series prediction and cardiac artifacts extraction from magnetoencephalographic data.
机译：自适应非线性滤波器的设计引起了机器学习界的极大兴趣。本文旨在介绍非线性自适应滤波的一些最新进展。我们对基于递归最小二乘算法的一类内核过滤器的性能和复杂性进行了深入分析。内核算法基础的一个关键功能是它们将数据映射到执行线性过滤的高维特征空间中。算术运算是在初始空间中通过评估成对的输入模式（称为内核）之间的内积来进行的。我们在两种类型的应用程序上评估了基于核的递归最小二乘滤波器的SNR改善和收敛速度：时间序列预测和从脑磁图数据中提取心脏伪影。

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