A new diffusion variable spatial regularized LMS algorithm
Signal Processing, ISSN: 0165-1684, Vol: 188, Page: 108207
2021
- 9Citations
- 6Captures
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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
This paper develops a new diffusion (Diff) least mean squares (LMS) algorithm for the identification of a network of systems that have distinct parameters at each node. The mean and mean squares behavior of the Diff-LMS algorithm in the so called multitask environment is studied in order to obtain an explicit expression of the estimation bias and variance in terms of the spatial regularization (SR) parameter. An optimal SR formula for the Diff LMS algorithm is then derived via minimizing the estimation error. An approximation is made to the formula such that a new practical Diff variable SR LMS (Diff-VSR-LMS) algorithm is obtained. This paper also provides a framework for the design of other LMS-like algorithms that incorporate diffusion technology to solve multitask problems. The theoretical analysis is evaluated via computer simulations and the performance of the proposed algorithm is compared with conventional Diff LMS algorithms under the multitask environment.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S0165168421002450; http://dx.doi.org/10.1016/j.sigpro.2021.108207; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85108976193&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S0165168421002450; https://dx.doi.org/10.1016/j.sigpro.2021.108207
Elsevier BV
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