Tuning the hyperparameters of the filter-based regularization method for impulse response estimation. Issue 1 (July 2017)

• Record Type: Journal Article
• Authors: Marconato, Anna; Schoukens, Maarten
• Is Part Of: IFAC-PapersOnLine. Volume 50:Issue 1(2017)
• Page Start: 12841
• Page End: 12846
• Publication Date: 2017-07
• Subjects: Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2017.08.1934
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: This paper discusses the use of a filter-based method for regularized impulse response modeling for linear time-invariant systems. The proposed method is a reformulation of the Bayesian, kernel based impulse response modeling approaches. The filter interpretation of the regularization cost function allows one to develop an intuitive framework to model a wide range of systems with different properties in a flexible way. Two hyperparameter selection techniques, based on Cross Validation and on Marginal Likelihood Maximization are presented. The proposed methods are tested on Monte Carlo simulation examples and on a real robotics problem. The results are compared with the ones obtained with the kernel-based methods based on the DC and TC kernels.