Computational approach based on use of multilayer neural networks in classification of rail line states

Abstract

The article considers the issue of classification of the states of rail lines using multilayer neural networks. It is shown that with significant external disturbing influences, the most promising direction is the retraining of classification models using neural networks. The representativeness of the sample is proposed to be realized by applying the scaling of informative features. It is proposed to minimize classification errors by gradient optimization methods. In the work during classification of rail lines states by neural networks, at the first stage, artificial classes of states are formed. At the second stage, the classification of states by the trained neural network is considered, classification errors are identified. On the basis of these errors the network is retrained until acceptable results are obtained. The article presents neuron learning algorithms: Hebb’s algorithm, error backpropagation method (for training a multilayer neural network), Konohen’s, Hopfield’s, Hamming’s algorithm and network. In the presented revision of the classification model, the correction is carried out according to the Widrow-Hoff algorithm.