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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.

In recent years, a method of neural network event forecasting has been developed, based on the use of a pair of recurrent neural networks with controlled elements. This method allows you to make predictions without interrupting training. However, for its full use, a reasonable software implementation is necessary. This study considers the problem of searching for a software architecture that implements the method of neural network forecasting with continuous learning. Offers an improved prediction method that significantly reduces the required amount of memory. A procedure for accelerated calculation of the weights of neural network synapses has been developed. To assess the effectiveness of the proposed architectural solutions, a comparative analysis of various variants of software implementations was conducted. In systems developed with the proposed innovations, the requirements for memory and computing resources are much lower than in software implementations of the prototype method. For example, the amount of memory required has decreased by an average of 15 times, and system initialization has taken 16 times less time. At the same time, the strategy of maximum memory saving in such systems proved to be unproductive compared to the combined approach. Based on the obtained comparison results, recommendations are given for the use and choice of architectures depending on the specific tasks facing the end user, and the hardware and software environment in which the forecasting systems are supposed to operate.