Skip to Main Content
A multivariate nonparametric pattern recogiton system is described for the surveillance of a high-flux isotope reactor. Two nonparametric methods are worked out: one using the Bayes rule with the Rosenblatt-Parzen estimator for the probability law, and one using the k-nearest neighbor rule. Performances are evaluated by comparing the probability of misclassification between the two chosen clases: the first corresponds to a nonaction of the reactor operator on its power and the second to an action of the pilot. Processing is perfonned on the power sigal of the reactor which is an observation corrupted by noise. The system has been tested on several experiences and implemented to work in real time on the reactor. The aim Is to conceive a computer-aided decsion system for the reactor's pilot.