Development of the learning classifier of rail lines states with multivariate informative features

A considerable number of failures in the system of interval control of train traffic are associated with the impact of disturbing actions in a wide range of change in the only informative feature characterizing the rail lines states. In the paper, it is proposed to determine the state of the control object by the principles of the pattern recognition with multivariate informative features. It is suggested to use as the features the voltages and currents at the input and output of a quadripole, and as a polynomial of the decisive function — the Hermit’s orthogonal polynomial, which allows increasing the depth of recognition and ensuring the relative invariance of disturbing actions by amplifying the order and dimension. In recognition of the rail lines states the relative error of calculating class boundaries by decisive functions is used as a quality criterion.
Serviceability of the proposed method is demonstrated by the results of rail lines states recognition with a "trained" decisive function.