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In this chapter we show that the LMS algorithm can be speeded up without changing any of its adaptive characteristics. The parallel LMS adaptive filtering algorithm and its modifications are presented. High speed is achieved by increasing the parallelism in the LMS adaptive algorithm through a proper modification of the LMS adaptive algorithm. An iterative procedures for efficient computation of the lower triangular inverse matrix and the input signal covariance matrix are presented.

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High Speed Least Mean Square Adaptive Filtering

  • 章节

  • 发表日期：

    2026年

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  • Rimantas Pupeikis,
  • Kazys Kazlauskas

摘要

In this chapter we show that the LMS algorithm can be speeded up without changing any of its adaptive characteristics. The parallel LMS adaptive filtering algorithm and its modifications are presented. High speed is achieved by increasing the parallelism in the LMS adaptive algorithm through a proper modification of the LMS adaptive algorithm. An iterative procedures for efficient computation of the lower triangular inverse matrix and the input signal covariance matrix are presented.