Abstract
Block coordinate descent is an optimization technique that is used for estimating multi-input single-output (MISO) continuous-time models, as well as single-input single output (SISO) models in additive form. Despite its widespread use in various optimization contexts, the statistical properties of block coordinate descent in continuous-time system identification have not been covered in the literature. The aim of this paper is to formally analyze the bias properties of the block coordinate descent approach for the identification of MISO and additive SISO systems. We characterize the asymptotic bias at each iteration, and provide sufficient conditions for the consistency of the estimator for each identification setting. The theoretical results are supported by simulation examples.
Original language | English |
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Article number | 10500885 |
Pages (from-to) | 388-393 |
Number of pages | 6 |
Journal | IEEE Control Systems Letters |
Volume | 8 |
DOIs | |
Publication status | Published - 16 Apr 2024 |
Keywords
- Additive models
- Additives
- Block coordinate descent
- Continuous-time system identification
- Convergence
- MISO communication
- MISO models
- Polynomials
- Statistical analysis
- System identification
- Vectors
- block coordinate descent
- additive models