Recursive algorithms using a bivariate weighted L1 criterion and alternating optimization are proposed for sparse parameter identification of multivariate stochastic systems, with proofs of set and parameter convergence under non-stationary conditions.
Sparse Bayesian deep learning for dynamic system identification
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Recursive Sparse Parameter Identification of Multivariate ARMAX Systems with Non-stationary Observations and Colored Noise
Recursive algorithms using a bivariate weighted L1 criterion and alternating optimization are proposed for sparse parameter identification of multivariate stochastic systems, with proofs of set and parameter convergence under non-stationary conditions.