Study of Training Quality of Multilayer Artificial Neural Networks with Variable Signal Conductivity in Scheduling Problems

Abstract

The paper studies approaches to railway scheduling problems using artificial neural networks (ANNs). The authors analyze traditional learning algorithms and difficulties for their application. The description of ANN’s behavior is provided in the form of a phase portrait. New approaches and techniques are proposed for quality improvement of training of multilayer ANNs with variable signal conductivity.