A Simulation-induced Regularization Method for System Identification⁎This work was supported by the Thousand Youth Talents Plan funded by the central government of China, the general project funded by NSFC under contract No. 61773329, the Shenzhen research projects funded by the Shenzhen Science and Technology Innovation Council under contract No. Ji-20170189, the President's grant under contract No. PF. 01.000249 and the Start-up grant under contract No. 2014.0003.23 funded by CUHKSZ. Issue 7 (2021) - Chen, Tianshi; Ju, Yue; Zhang, Junpeng; Mu, Biqiang
Abstract: In the past decade, regularization methods for system identification have attracted a great deal of attention in the system identification community. For regularization method with regularization in quadratic form, there are different ways to design the regularization, e.g., through designing a positive semidefinite kernel or a filter. In this paper, we propose a new regularization method, where the regularization is in essence induced by simulating a carefully designed linear system driven by a white Gaussian noise and this regularization method is thus called the simulation-induced regularization method (SIRM). In contrast with the kernel or filter based regularization methods, SIRM has the advantages that it is free of the explicit expression of the regularization and moreover, has a linear computational complexity.