Dynamic classification of stochastic processes and systems in discrete time. I. Design of algorithms

The paper is concerned with effective methods for process recognition in discrete time from direct and indirect measurements. Basic equations are obtained for statistics whith permit forming sequential non-randomize decision rules for classification in current time of completely or partially observable multi-dimensional processes and systems. Recognition under uncertainty is investigated.