Multi-parametric classification of automaton Markov models based on the sequences they generate

This article is devoted to multi-parametric classification of automaton Markov models (AMMs) on the base of output sequences with the use of discriminant analysis. The AMMs under consideration are specified by means of stochastic matrices belonging to subclasses defined a priori. A set of claasification features is introduced to distinguish AMMs specified by matrices from different subclasses. The features are related to the frequency characteristics of sequences generated by AMMs. A method is suggested for determining the minimal length of the sequence need to calculate the features with a required accuracy.