DEVELOPMENT OF NEURAL NETWORKS FOR FORECASTING OF CHEMICAL SUBSTANCES’ MIGRATION IN SOIL AND ALGORITHMS OF THEIR TRAINING

Abstract

A review of the existing models and methods for forecasting chemical substances' migration in soil is contained in the paper. The paper shows that the most effective decision for solving ecological tasks in this field is an application of artificial neural networks using training «with a tutor» on the basis of an inverse error propagation algorithm. Corresponding structures of neural networks for solution of the given problem have been developed in the paper.A new method for artificial neural network training based on the modification of an inverse error propagation algorithm while using an additional signal is proposed in the paper. The given method allows to achieve 100% convergence in the forecasting problems pertaining to chemical substances' migration in soil.