- Title
- On the Uncertainty Modelling for Linear Continuous-Time Systems Utilising Sampled Data and Gaussian Mixture Models
- Creator
- Orellana, Rafael; Coronel, Maria; Carvajal, Rodrigo; Delgado, Ramon A.; Escarate, Pedro; Agüero, Juan C.
- Relation
- 19th IFAC Symposium on System Identification (SYSID). Proceedings of 19th IFAC Symposium on System Identification (SYSID), Volume 54 (Padova, Italy 13-16 July, 2021) p. 589-594
- Publisher Link
- http://dx.doi.org/10.1016/j.ifacol.2021.08.424
- Publisher
- Elsevier
- Resource Type
- conference paper
- Date
- 2021
- Description
- In this paper a Maximum Likelihood estimation algorithm for model error modelling in a continuous-time system is developed utilising sampled data and a Stochastic Embedding approach. Orthonormal basis functions are used to model both the continuous-time nominal model and the error-model. The stochastic properties of the error-model distribution are defined by using a Gaussian mixture model. For the estimation of the nominal model and the error-model distribution we develop a technique based on the Expectation-Maximization algorithm using sampled data from independent experiments. The benefits of our proposal are illustrated via numerical simulations.
- Subject
- continuous-time model; discrete-time model; Gaussian mixture model; maximum likelihood; stochastic embedding
- Identifier
- http://hdl.handle.net/1959.13/1450742
- Identifier
- uon:44020
- Identifier
- ISSN:2405-8963
- Language
- eng
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