Development of Stability Control Mechanisms in Neural Network Forecasting Systems

Abstract

The problem of ensuring the stable functioning of time series forecasting systems based on streaming recurrent neural networks with controlled elements is considered. The mechanisms necessary and sufficient for its maintenance are derived, which involve maintaining the balance of the learning history and modifying the synapse learning rules in order to establish a balance between positive and negative potential. The results of experiments to assess the accuracy of forecasting are presented.