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- W2681038810 abstract "Abstract This paper is concerned with the set membership weighted recursive least squares (SM-WRLS) algorithm which can be used for estimating the parameters of linear system or signal models in which the error sequence is pointwise energy bounded. This algorithm works with bounding hyperellipsoidal regions to describe the solution sets. A new strategy is developed which can be applied to virtually any version of the SM-WRLS algorithm to improve the computational efficiency. A significant reduction in computational complexity can be achieved by employing a sub- optimal test for information content in an incoming equation. The proposed check is argued to be a useful determiner of the ability of incoming data to shrink the ellipsoid. The performance of this strategy is compared to that of SM-WRLS using simulation studies." @default.
- W2681038810 created "2017-06-30" @default.
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- W2681038810 date "1992-07-01" @default.
- W2681038810 modified "2023-09-26" @default.
- W2681038810 title "Sm-Wrls Algorithms with an Efficient Test for Innovation" @default.
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- W2681038810 doi "https://doi.org/10.1016/s1474-6670(17)50644-8" @default.
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