Method for classifying untyped objects based on cascade neural network filter and finite deterministic automaton

The article describes a method for the phased finding of the multidimensional object class in the case when the set of classes is not known in advance. The developed method first solves the problem of selecting classes from an untyped heterogeneous set of objects, and then classifies an arbitrary new object into the determined classes. Classes are found on the basis of the author's algorithm of cascade neural filtering, and the objects classification is performed using the author's model based on a finite automaton.