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Plenary Lecture

Incorporating Knowledge about Model Structure in Identification of Gaussian-Process Models

Professor Juš Kocijan
Jozef Stefan Institute
Ljubljana, Slovenia
&
University of Nova Gorica
Nova Gorica, Slovenia
E-mail: jus.kocijan@ijs.si

Abstract: Dynamic system identification with Gaussian-process prior model is a probabilistic, nonparametric modelling method for identification. Gaussian-process models provide, besides the prediction, also the information about prediction uncertainty based on the availability or uncertainty of the data used for the modelling. An advantage of this kind of model is a small number of training parameters, a facilitated structure determination and the possibility to include various sorts of prior knowledge into the model. One of possibilities is to include block-structure knowledge like Hammerstein model structure. The identification procedure of Gaussian-process model with Hammerstein model structure will be presented and illustrated with an example.

Brief Biography of the Speaker: Juš Kocijan received the doctorate in electrical engineering from the Faculty of Electrical Engineering, University of Ljubljana, Slovenia. He is currently a senior researcher at the Department of Systems and Control, Jozef Stefan Institute and Professor of Electrical Engineering at the School of Engineering and Management, University of Nova Gorica, Slovenia. His other experience includes: running a number of international and domestic research projects, serving as editor and on editorial boards of research journals, serving as a member of IFAC Technical committee on Computational Intelligence in Control. His main research interests are: applied nonlinear control and multiple model and probabilistic approaches to modelling and control. He is a Senior member of IEEE, Control Systems Society, a member of SLOSIM – Slovenian Society for Simulation and Modelling and Automatic control society of Slovenia.

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