Abstract The paper considers missing data management task in smart systems. The main strategies of missing data management in handling missing data are analyzed. A prediction method for probable recovery of partially missing or completely lost data based on the improvement of an ensemble of two GRNNs by the additional use of extended-input SGTM neural-like structure is proposed. The latter is used to increase the accuracy of the procedure of weighted summation with the displacement of the outputs of both GRNN networks in comparison with existing methods. The flowchart of the ensemble is given. The training algorithm is presented, as well as the detailed procedure of its use. The improved ensemble prediction method has been tested for the task of completing the gaps in the real dataset reflecting air condition monitoring. The optimal parameters of the component operation of the improved ensemble have been determined experimentally. The efficiency of its performance has been evaluated by its experimental comparison with methods of the same class based on the MAPE and RMSE accuracy indicators. The minimum value of an application error of the developed method for solving the task in comparison with existing ones for the specified accuracy indicators is established. The time delays in applying the methods examined have been determined experimentally. The limitations of the improved method and the prospects for further research are described.

中文翻译：

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一种使用改进的 GRNN-SGTM 集成方法进行缺失数据管理的方法

摘要 本文考虑了智能系统中的缺失数据管理任务。分析了处理缺失数据时缺失数据管理的主要策略。提出了一种基于通过额外使用扩展输入 SGTM 类神经结构改进两个 GRNN 集成的部分丢失或完全丢失数据的可能恢复预测方法。与现有方法相比，后者用于通过两个 GRNN 网络的输出位移来提高加权求和过程的准确性。给出了集成的流程图。介绍了训练算法，以及其使用的详细过程。改进的集合预测方法已经过测试，以完成反映空调监测的真实数据集中的空白。改进后的集成组件操作的最佳参数已通过实验确定。基于 MAPE 和 RMSE 精度指标，通过与同类方法的实验比较来评估其性能的效率。建立所开发的解决任务方法的应用误差与指定精度指标的现有方法相比的最小值。应用所检查方法的时间延迟已通过实验确定。描述了改进方法的局限性和进一步研究的前景。基于 MAPE 和 RMSE 精度指标，通过与同类方法的实验比较来评估其性能的效率。建立所开发的解决任务方法的应用误差与指定精度指标的现有方法相比的最小值。应用所检查方法的时间延迟已通过实验确定。描述了改进方法的局限性和进一步研究的前景。基于 MAPE 和 RMSE 精度指标，通过与同类方法的实验比较来评估其性能的效率。建立所开发的解决任务方法的应用误差与指定精度指标的现有方法相比的最小值。应用所检查方法的时间延迟已通过实验确定。描述了改进方法的局限性和进一步研究的前景。