Mixed model identification for linear time‐invariant systems with mixed noises in frequency domain

Abstract

In this paper, we present a new method for frequency domain identification of discrete linear time-invariant systems. We take consideration of the case where the output noises are mixed or unknown. In order to deal with this problem, a new mixed model structure is used correspondingly. The augmented Lagrangian method (ALM) is combined in selection of poles for the shifted Cauchy kernels to get solutions to the optimal problem. Simulations show the proposed method can get efficient approximation to the original systems. KEYWORDSfrequency domain, mixed noises, orthogonal basis, rational function, shifted Cauchy kernel MSC CLASSIFICATION A1420; 26C15Math Meth Appl Sci. 2019;42:7285-7295.wileyonlinelibrary.com/journal/mma

Sparse representations in reproducing kernels of operator equations

“…POAFD 算法的具体的例子可参见文献 [7,13,14,23]. AFD 和 POAFD 算法在信号与图像处理及 在系统辨识或模型约化 (model reduction) 实际问题中的应用可参见文献 [32][33][34][35][36].…”