Abstract

The problem of neural network forecasting of processes with changing laws of their behavior and imperfection of time-series samples is considered on the example of analysis of urban traffic flows. The goal is to improve the accuracy of such forecasting. To achieve this goal, we analyze the applicability of self-learning recurrent neural networks with controlled elements and the spiral structure of layers. Based on the development and application of these neural networks, the new methods and the system implementing them are proposed. These methods, in contrast to known solutions, allow continuous training of neural networks and forecasting of processes. There is no need to interrupt training to perform forecasting. For forecasting, it is possible to continuously take into account the properties of the observed processes. In addition, improved controlling of associative recall of information from the memory of recurrent neural networks is provided to improve the accuracy of forecasting. The results of traffic flow forecasting are presented. The results are compared with estimates obtained using other methods. It is shown that the proposed methods have advantages in accuracy compared to the known solutions. The developed methods are recommended for use in advanced robotic and other intelligent systems.

中文翻译：

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递归神经网络的层状螺旋结构预测城市交通流量

摘要

在城市交通流量分析的例子中，考虑了具有行为变化规律和时间序列样本不完善的过程的神经网络预测问题。目的是提高这种预测的准确性。为了实现这一目标，我们分析了具有自控元素和层螺旋结构的自学习循环神经网络的适用性。在这些神经网络的发展和应用的基础上，提出了新的方法和实现它们的系统。与已知的解决方案相反，这些方法允许连续训练神经网络和预测过程。无需中断训练即可进行预测。为了进行预测，可以连续考虑所观察过程的属性。此外，提供了从递归神经网络的内存中关联召回信息的改进控制，以提高预测的准确性。介绍了交通流量预测的结果。将结果与使用其他方法获得的估计值进行比较。结果表明，与已知解决方案相比，所提出的方法在准确性上具有优势。建议将开发的方法用于高级机器人和其他智能系统。