<XML><RECORDS><RECORD><REFERENCE_TYPE>0</REFERENCE_TYPE><REFNUM>7406</REFNUM><AUTHORS><AUTHOR>Kocijan,J.</AUTHOR><AUTHOR>Girard,A.</AUTHOR><AUTHOR>Blanko,B.</AUTHOR><AUTHOR>Murray-Smith,R.</AUTHOR></AUTHORS><YEAR>2004</YEAR><TITLE>Dynamic Systems Identification with Gaussian Processes</TITLE><PLACE_PUBLISHED>Mathematical and Computer Modelling of Dynamical Systems </PLACE_PUBLISHED><PUBLISHER>N/A</PUBLISHER><LABEL>Kocijan:2004:7406</LABEL><KEYWORDS><KEYWORD>System identification</KEYWORD></KEYWORDS<ABSTRACT>This paper describes the identification of nonlinear dynamic systems with a Gaussian process prior model. This model is an example of the use of a probabilistic, non-parametric modelling approach. Gaussian processes are flexible models capable of modelling complex nonlinear systems. Also, an attractive feature of this model is that the variance associated with the model response is readily obtained; and it can be used to highlight areas of the input space where prediction quality is poor, due to the lack of data or complexity (high variance). We illustrate the Gaussian process modelling technique on a simulated example of a nonlinear system. </ABSTRACT></RECORD></RECORDS></XML>