A Bayesian Framework for Large-Scale Identification of Nonlinear Hybrid Systems. Issue 5 (2021)

• Record Type: Journal Article
• Authors: Madary, Ahmad; Momeni, Hamid Reza; Abate, Alessandro; Larsen, Kim G.
• Is Part Of: IFAC-PapersOnLine. Volume 54:Issue 5(2021)
• Page Start: 259
• Page End: 264
• Publication Date: 2021
• Subjects: Nonlinear hybrid systems -- Switched nonlinear ARX models -- Bayesian inference -- System identification -- Occam's Razor principle -- Large data sets; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2021.08.508
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: In this paper, a two-level Bayesian framework is proposed for the identification of nonlinear hybrid systems from large data sets by embedding it in a four-stage procedure. At the first stage, feature vector selection techniques are used to generate a reduced-size set from the given training data set. The resulting data set then is used to identify the hybrid system using a Bayesian method, where the objective is to assign each data point to a corresponding sub-mode of the hybrid model. At the third stage, this data assignment is used to train a Bayesian classifier to separate the original data set and determine the corresponding sub-mode for all the original data points. Finally, once every data point is assigned to a sub-mode, a Bayesian estimator is used to estimate a regressor for each sub-system independently. The proposed method tested on three case studies.