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Bayesian nonparametric identification of piecewise affine ARX systems
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
2015 (English)Conference paper, Published paper (Refereed)
Abstract [en]

We introduce a Bayesian nonparametric approach to identification of piecewise affine ARX systems. The clustering properties of the Dirichlet process are used to construct a prior over the partition of the regressor space as well as the parameters of each local model. This enables us to probabilistically reason about and to identify the number of modes, the partition of the regressor space, and the linear dynamics of each local model from data. By appropriate choices of base measure and likelihood function, we give explicit expressions for how to perform both inference and prediction. Simulations and experiments on real data from a pick and place machine are used to illustrate the capabilities of the new approach.

Place, publisher, year, edition, pages
2015. Vol. 48, no 28, p. 709-714
Keywords [en]
Bayesian, Non-parametric identification, Dirichlet processes, Piecewise linear, Autoregressive models
National Category
Signal Processing
Research subject
Electrical Engineering with specialization in Signal Processing
Identifiers
URN: urn:nbn:se:uu:diva-306931DOI: 10.1016/j.ifacol.2015.12.213OAI: oai:DiVA.org:uu-306931DiVA, id: diva2:1044749
Conference
17th IFAC Symposium on System Identification SYSID 2015 – Beijing, China, 19–21 October 2015
Funder
Swedish Research Council, 637-2014-466Swedish Research Council, 621-2013-5524Available from: 2016-11-06 Created: 2016-11-06 Last updated: 2016-11-06

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Publisher's full texthttp://www.sciencedirect.com/science/article/pii/S2405896315028372

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