Computational system identification of continuous-time nonlinear systems using approximate Bayesian computation. Issue 15 (17th November 2016)

• Record Type: Journal Article
• Authors: Krishnanathan, Kirubhakaran; Anderson, Sean R.; Billings, Stephen A.; Kadirkamanathan, Visakan
• Is Part Of: International journal of systems science. Volume 47:Issue 15(2016)
• Page Start: 3537
• Page End: 3544
• Publication Date: 2016-11-17
• Subjects: Models -- NARMAX -- continuous-time systems -- system identification and signal processing -- Bayesian estimation -- computational system identification -- nonlinear -- approximate Bayesian computation; System analysis -- Periodicals; 003.3
• DOI: 10.1080/00207721.2015.1090643
• Languages: English
• ISSNs: 0020-7721

Abstract

ABSTRACT: In this paper, we derive a system identification framework for continuous-time nonlinear systems, for the first time using a simulation-focused computational Bayesian approach. Simulation approaches to nonlinear system identification have been shown to outperform regression methods under certain conditions, such as non-persistently exciting inputs and fast-sampling. We use the approximate Bayesian computation (ABC) algorithm to perform simulation-based inference of model parameters. The framework has the following main advantages: (1) parameter distributions are intrinsically generated, giving the user a clear description of uncertainty, (2) the simulation approach avoids the difficult problem of estimating signal derivatives as is common with other continuous-time methods, and (3) as noted above, the simulation approach improves identification under conditions of non-persistently exciting inputs and fast-sampling. Term selection is performed by judging parameter significance using parameter distributions that are intrinsically generated as part of the ABC procedure. The results from a numerical example demonstrate that the method performs well in noisy scenarios, especially in comparison to competing techniques that rely on signal derivative estimation.