Sparse parameter identification of stochastic dynamical systems

Sparsity of a parameter vector in stochastic dynamic systems and precise reconstruction of its zero and nonzero elements appear in many areas including systems and control[1-4],signal processing[5,6],statistics[7,8],and machine learning[9,10]since it provides a way to discover a parsimonious model that leads to more reliable and robust prediction.Classical system identification theory has been a well-developed field[11,12].It usually characterizes the identification error between the estimates and the unknown parameters using different criteria such as randomness of noises,frequency domain sample data。