Abstract
We consider a statistical decision problem as a tool for solving control problems with a camera in the loop. The first stage is features extraction from images. Its role is to process images in order to extract features relevant for the control problem. Then, they are fed as inputs to the Bayesian decision problem. At the second stage a loss function, which is a sum of squared deviations of decisions from true decisions is considered. Finally, an approximation of the optimal decision rule is proposed, using a learning sequence of decisions, which – together with feature extracting algorithms – form the control system. The proposed approach is illustrated by a system that is dedicated to control natural gas burners.
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Rafajłowicz, E., Pawlak-Kruczek, H., Rafajłowicz, W. (2014). Statistical Classifier with Ordered Decisions as an Image Based Controller with Application to Gas Burners. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2014. Lecture Notes in Computer Science(), vol 8467. Springer, Cham. https://doi.org/10.1007/978-3-319-07173-2_50
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DOI: https://doi.org/10.1007/978-3-319-07173-2_50
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