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Method for improving RLS algorithms

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Abstract

The recursive least-square (RLS) algorithm has been extensively used in adaptive identification, prediction, filtering, and many other fields. This paper proposes adding a second-difference term to the standard recurrent formula to create a novel method for improving tracing capabilities. Test results show that this can greatly improve the convergence capability of RLS algorithms.

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Authors and Affiliations

  1. College of Automation, Harbin Engineering University, Harbin, 150001, China

    Tian-shu Li, Kai Tian & Wen-xiu Li

Authors
  1. Tian-shu Li
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  2. Kai Tian
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  3. Wen-xiu Li
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Additional information

LI Tian-Shu was born in 1965. He earned his doctorate from Harbin Engineering University. His current research interests include time series predictions, support vector machines, chaos theory, etc.

TIAN Kai was born in 1972. He is an Associate Professor at Harbin Engineering University. His current research interests include power systems, time series predictions, etc.

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Li, Ts., Tian, K. & Li, Wx. Method for improving RLS algorithms. J. Marine. Sci. Appl. 6, 68–70 (2007). https://doi.org/10.1007/s11804-007-5077-x

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  • DOI: https://doi.org/10.1007/s11804-007-5077-x

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