The artificial intelligence method application for grain production productivity indicators predicting

Abstract

The purpose of the article is to calculate the grain production productivity indicators prediction based on the operation mechanism of a linear cellular automatic machine. The concepts of «memory depth», «long-term memory» are given, a description of the artificial intelligence method, its approbation and interpretation of the results obtained are presented. It is shown that the grain production productivity predicting develops along cyclic trajectories; their characteristics stability is significantly higher than the stability of the periodicity of separately selected process points. The article presents a demonstration of the linear cellular automaton method operation based on the time series of grain crop yields in the Ishim district of the Tyumen region.