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Low-Complexity Recursive Least-Squares Adaptive Algorithm Based on Tensorial Forms
| Content Provider | MDPI |
|---|---|
| Author | Ionu, ț-Dorinel Fîciu Stanciu, Cristian-Lucian Anghel, Cristian Elisei-Iliescu, Camelia |
| Copyright Year | 2021 |
| Description | 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. |
| Starting Page | 8656 |
| e-ISSN | 20763417 |
| DOI | 10.3390/app11188656 |
| Journal | Applied Sciences |
| Issue Number | 18 |
| Volume Number | 11 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2021-09-17 |
| Access Restriction | Open |
| Subject Keyword | Applied Sciences Industrial Engineering Adaptive Filters Dichotomous Coordinate Descent (dcd) Recursive Least-squares (rls) System Identification Tensor Decomposition |
| Content Type | Text |
| Resource Type | Article |
