Interval predictor model: Difference between revisions

Revision as of 06:23, 28 January 2020

Revision as of 18:14, 19 June 2020

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where the training data examples are <math> y^{(i)}</math> and <math> x^{(i)}</math>, and the Interval Predictor Model bounds <math> \underline{y}_p(x)</math> and <math>\overline{y}_p(x) </math> are parameterised by the parameter vector <math> p </math>.

The reliability of such an IPM is obtained by noting that for a convex IPM the number of support constraints is less than the dimensionality of the [[trainable parametersparameter]]s, and hence the scenario approach can be applied.

Lacerda (2017) demonstrated that this approach can be extended to situations where the training data is interval valued rather than point valued <ref name="LacerdaCrespo2017">{{cite book|last1=Lacerda|first1=Marcio J.|title=2017 American Control Conference (ACC)|last2=Crespo|first2=Luis G.|chapter=Interval predictor models for data with measurement uncertainty|year=2017|pages=1487–1492|doi=10.23919/ACC.2017.7963163|isbn=978-1-5090-5992-8|hdl=2060/20170005690}}</ref>.