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A Targeted Forgetting Factor for Recursive Least Squares | IEEE Conference Publication | IEEE Xplore

A Targeted Forgetting Factor for Recursive Least Squares


Abstract:

Recursive least squares (RLS) is widely used in signal processing, identification, and control, but is plagued by the inability to adjust quickly to changes in the unknow...Show More

Abstract:

Recursive least squares (RLS) is widely used in signal processing, identification, and control, but is plagued by the inability to adjust quickly to changes in the unknown parameters. RLS with standard forgetting factor overcomes this problem but causes divergence due to the lack of persistency. Variable and directional forgetting factors have been proposed for overcoming this deficiency. The present paper proposes a targeted forgetting factor that looks directly at recent data in order to determine which directions possess new information. Targeted forgetting applies a forgetting factor directly to these directions, thereby providing a simple and effective technique for avoiding covariance divergence. Numerical examples compare targeted forgetting to standard and directional forgetting.
Date of Conference: 17-19 December 2018
Date Added to IEEE Xplore: 20 January 2019
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Conference Location: Miami, FL, USA

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