Neural network event forecasting for robots with continuous training

Abstract

Introduction: High hopes for a significant expansion of human capabilities in various fields of activity are pinned on the creation and use of highly intelligent robots. To achieve this level of robot intelligence, it is necessary to successfully solve the problems of predicting the external environment and the state of the robots themselves. Solutions based on recurrent neural networks with controlled elements are promising neural network forecasting systems. Purpose: Search for appropriate neural network structures for predicting events. Development of approaches to controlling the associative call of information from a neural network memory. Methods: Computer simulation of recurrent neural networks with controlled elements and various structures of layers. Results: An improved method of neural network event forecasting with continuous robot training has been developed. This method allows you to predict events on either long or short samples of time series. In order to improve the forecasting accuracy, new rules have been proposed for controlling the associative call of information from the neural network memory. A software system has been developed which implements the proposed method and supports the emulation of neural networks with various layer structures. The possibilities of recurrent neural networks with linear or spiral layer structures are analyzed using the example of urban traffic flow forecasting. The gain of the proposed method in comparison with the ARIMA model for the MAPE indicator is from 4.1 to 7.4%. Among the studied neural network structures, the spiral structures have shown the highest accuracy, and linear structures have shown the lowest accuracy. Practical relevance: The results of the study can be used to improve the accuracy of event forecasting for intelligent robots.