Linear System Identification via EM with Latent Disturbances and Lagrangian Relaxation
Abstract¶
In the application of the Expectation Maximization algorithm to identification of dynamical systems, internal states are typically chosen as latent variables, for simplicity. In this work, we propose a different choice of latent variables, namely, system disturbances. Such a formulation elegantly handles the problematic case of singular state space models, and is shown, under certain circumstances, to improve the fidelity of bounds on the likelihood, leading to convergence in fewer iterations. To access these benefits we develop a Lagrangian relaxation of the nonconvex optimization problems that arise in the latent disturbances formulation, and proceed via semidefinite programming.
Links¶
Knowledge Base
External
Tags¶
Similar Papers¶
1
77%
2
76%
3
76%
4
76%
Low-Complexity Learning of Linear Quadratic Regulators from Noisy Data
Low-Complexity Data-Driven LQR
5
75%