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- W3200380712 abstract "Modern solutions for system identification problems employ multilinear forms, which are based on multiple-order tensor decomposition (of rank one). Recently, such a solution was introduced based on the recursive least-squares (RLS) algorithm. Despite their potential for adaptive systems, the classical RLS methods require a prohibitive amount of arithmetic resources and are sometimes prone to numerical stability issues. This paper proposes a new algorithm for multiple-input/single-output (MISO) system identification based on the combination between the exponentially weighted RLS algorithm and the dichotomous descent iterations in order to implement a low-complexity stable solution with performance similar to the classical RLS methods." @default.
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- W3200380712 date "2021-09-17" @default.
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- W3200380712 title "Low-Complexity Recursive Least-Squares Adaptive Algorithm Based on Tensorial Forms" @default.
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- W3200380712 doi "https://doi.org/10.3390/app11188656" @default.
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