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          Institute: MPI für biologische Kybernetik     Collection: Biologische Kybernetik     Display Documents



ID: 198177.0, MPI für biologische Kybernetik / Biologische Kybernetik
Predictive control with Gaussian process models
Authors:Kocijan, J.; Murray-Smith, R.; Rasmussen, C.E.; Likar, B.
Editors:Zajc, B.; Tkal, M.
Date of Publication (YYYY-MM-DD):2003
Title of Proceedings:Proceedings of IEEE Region 8 Eurocon 2003: Computer as a Tool
Start Page:352
End Page:356
Review Status:not specified
Audience:Not Specified
Intended Educational Use:No
Abstract / Description:This paper describes model-based predictive control based on Gaussian processes.Gaussian process models provide a probabilistic non-parametric modelling approach for black-box identification of non-linear dynamic systems. It offers more insight in variance of obtained model response, as well as fewer parameters to determine than other models. The Gaussian processes can highlight areas of the input space where prediction quality is poor, due to the lack of data or its
complexity, by indicating the higher variance around the predicted mean. This property is used in predictive control, where optimisation of control signal takes the variance information into account. The predictive control principle is demonstrated on a simulated example of nonlinear system.
External Publication Status:published
Document Type:Conference-Paper
Communicated by:Holger Fischer
Affiliations:MPI für biologische Kybernetik/Empirical Inference (Dept. Schölkopf)
Identifiers:LOCALID:2283
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