Probabilistic advisory systems for data-intensive applications

Abstract

Real-world, multidimensional, dynamic, non-linear processes typically exhibit many distinct modes of operation. Mixtures of dynamic models improve greatly on traditional one-component linear models in this context. Improved prediction then points the way to effective adaptive control design. This paper presents the experience gained under the EU Project, ProDaCTool, in designing and implementing advisory systems, based on dynamic mixtures, in diverse domains: urban traffic regulation, therapy recommendations in nuclear medicine, and operator support for metal-strip rolling mills. Efficient, recursive estimation of the dynamic mixtures from archive data is accomplished using the quasi-Bayes (QB) algorithm, implemented with dedicated software developed within ProDaCTool. The advisory systems are designed using the probabilistic control design technique presented in the previous paper. Highly encouraging prediction and performance enhancements are reported for the applications considered.