K-step ahead prediction in fuzzy decision space-application to prognosis

Abstract

The authors demonstrate the ability and the accuracy of a modified extended Kalman filter used as a k-step-ahead predictor to perform a predicted membership function's point in a fuzzy decision space based on fuzzy pattern recognition principles, instead of a predicted state in the feature space. Results obtained with this prediction procedure are presented. A scheme including both fuzzy decision and prediction procedures is proposed for prognosis.<>