Plenary Lecture

Probabilistic Kernel Method for System Identification

Professor Juš Kocijan
Jozef Stefan Institute
Ljubljana, Slovenia
University of Nova Gorica
Nova Gorica, Slovenia


Abstract: The Gaussian-process model is an example of a probabilistic, kernel-regression model that can be used for the identification of nonlinear dynamic systems. It possesses several interesting features like model predictions contain the measure of confidence; the model has a small number of optimisation parameters and different possibilities of including prior knowledge. The Gaussian-process approach to modelling alleviates any model bias by not focusing on a single dynamics model, but by using a probabilistic dynamics model, a distribution over all plausible dynamics models that could have generated the observed experience. The framework for the identification of dynamic systems with Gaussian- process models will be presented and illustrated with a case study.

Brief Biography of the Speaker: Juš Kocijan received his 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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