Stability of Compressed Recursive Least Squares with Forgetting Factor Algorithm
IFAC-PapersOnLine, ISSN: 2405-8963, Vol: 56, Issue: 2, Page: 10240-10245
2023
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Article Description
To identify the unknown sparse time-varying parameters of the stochastic dynamic system, we integrate compressive sensing theory with the traditional recursive least squares with forgetting factor (FFLS) algorithm, and propose a compressed adaptive filtering algorithm. Our algorithm is designed to first compress the original high-dimensional sparse regression vector by using the sensing matrix, and then apply the FFLS algorithm to estimate the compressed parameters. Subsequently, the original high-dimensional sparse parameters can be well recovered by a reconstruction technique. We introduce an excitation condition on the compressed stochastic regressors, under which the stability of the proposed algorithm (i.e., the upper bound of the estimation error) is established without assuming independence, stationarity or ergodicity of the system signals. The effectiveness of our theoretical results is demonstrated by a numerical example, which also shows that our proposed algorithm has better performance than both the compressed least mean squares algorithm and the uncompressed FFLS algorithm for tracking high-dimensional sparse parameters.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S2405896323012880; http://dx.doi.org/10.1016/j.ifacol.2023.10.905; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85184962164&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S2405896323012880; https://dx.doi.org/10.1016/j.ifacol.2023.10.905
Elsevier BV
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