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Tensor-Based Recursive Least-Squares Adaptive Algorithms with Low-Complexity and High Robustness Features
| Content Provider | MDPI |
|---|---|
| Author | Ionu, ț-Dorinel Fîciu Stanciu, Cristian-Lucian Elisei-Iliescu, Camelia Anghel, Cristian |
| Copyright Year | 2022 |
| Description | The recently proposed tensor-based recursive least-squares dichotomous coordinate descent algorithm, namely RLS-DCD-T, was designed for the identification of multilinear forms. In this context, a high-dimensional system identification problem can be efficiently addressed (gaining in terms of both performance and complexity), based on tensor decomposition and modeling. In this paper, following the framework of the RLS-DCD-T, we propose a regularized version of this algorithm, where the regularization terms are incorporated within the cost functions. Furthermore, the optimal regularization parameters are derived, aiming to attenuate the effects of the system noise. Simulation results support the performance features of the proposed algorithm, especially in terms of its robustness in noisy environments. |
| Starting Page | 237 |
| e-ISSN | 20799292 |
| DOI | 10.3390/electronics11020237 |
| Journal | Electronics |
| Issue Number | 2 |
| Volume Number | 11 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-01-12 |
| Access Restriction | Open |
| Subject Keyword | Electronics Industrial Engineering Adaptive Filters Recursive Least-squares (rls) Algorithm Regularization System Identification Tensor Decomposition |
| Content Type | Text |
| Resource Type | Article |
