在可变和不明确的观测间隔下监测过程的关系可分离模型

Skalozub Vladyslav, Horiachkin Vadim, Murashov Oleg
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引用次数: 0

摘要

本文致力于开发组合模型、方法和工具,旨在解决当前以时间序列为代表的不同变量或模糊观测区间(CHRPNI)的监测过程数据建模和分析问题。本文提出了一种新的关系可分模型(RSM)和组合分位数算法,以提高CHRPNI过程建模和分析的准确性和效率。关系模型是在原始数据序列的基础上得到的一、二阶模糊关系系统来定义的。在该组合算法中,将SPM计算结果和模糊关系模型进行了推广,实现了各分量权重因子的最优选择。通过数值模拟的研究,证明了在PNEU情况下引入组合过程模型的合理性和有效性。以糖尿病患者康复监测过程的数据分析为例,表明了保证指标分析结果及其短期预测准确性的一定可能性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Relational-separable models of monitoring processes at variable and unclear observation intervals
The article is devoted to the development of combined models, methods and tools designed to solve the current problems of modeling and analysis of monitoring process data, which are repre-sented by time series and differ in variable or fuzzy observation intervals (CHRPNI). In the article, a new relational separable model (RSM) and a combined quantile algorithm are proposed to in-crease the accuracy and efficiency of modeling and analysis of the processes of CHRPNI. The rela-tional model is defined by a system of fuzzy relational relations of the first and second order ob-tained on the basis of the original sequence of data. In the combined algorithm, the results of calcu-lations obtained by SPM and models of fuzzy relational relationships were generalized with the op-timal selection of weighting factors for individual components. As a result of the conducted research by means of numerical modeling, it was established that the introduction of combined process models in the case of PNEU is rational and effective. Exam-ples of data analysis of monitoring processes of rehabilitation of diabetic patients showed certain possibilities of ensuring the accuracy of the results of the analysis of indicators and their short-term forecasting.
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