Skip to Main content Skip to Navigation
Conference papers

Continuous-time system identification using binary measurements

Abstract : This paper investigates the identification of Continuous-Time models based on binary observations. Currently, no identification algorithm has been proposed in this field. The reason is twofold: first the time-domain differentiation operator of a such model structure prevents the use of existing identification methods based on binary observations, second the simple knowledge of binary observations on the output prevents the use of existing Continuous-Time identification methods. In this paper a two steps identification algorithm is derived. It is based on the combined use of a specific input signal and Support Vector Machines for the reconstruction of a high resolution output signal. This high resolution output signal is then used for the estimation of a Continuous-Time model. An extension for the identification of a MISO system is also proposed. Some simulation results are given in order to illustrate the validity of the proposed method.
Document type :
Conference papers
Complete list of metadata
Contributor : Mathieu Pouliquen Connect in order to contact the contributor
Submitted on : Wednesday, June 27, 2018 - 9:41:33 PM
Last modification on : Saturday, June 25, 2022 - 9:51:58 AM



Mathieu Pouliquen, Abdelhak Goudjil, Olivier Gehan, Eric Pigeon. Continuous-time system identification using binary measurements. 2016 IEEE 55th Conference on Decision and Control (CDC), Dec 2016, Las Vegas, United States. pp.3787-3792, ⟨10.1109/CDC.2016.7798840⟩. ⟨hal-01825010⟩



Record views