基于机器学习的匹配概率与测量误差统计特征建模研究

Shuanzhu Li, Runfeng He, Baozhu Pan
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引用次数: 0

摘要

针对当前匹配概率和测量误差统计特征建模方法存在的问题,提出了基于机器学习的匹配概率和测量误差统计特征建模方法。根据总序列匹配概率和系统匹配次数的要求,计算序列匹配概率。分析了采集和匹配过程中的测量误差,得到了可测量的干扰参数。根据分析结果,对匹配测量误差的平均值进行了标准化,并建立了匹配概率和测量误差统计特征的性别模型。实验结果表明,该方法的匹配概率和测量误差统计模型具有较高的精度,在实际应用中具有良好的应用效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research on Statistical Characteristics Modeling of Matching Probability and Measurement Error Based on Machine Learning
In view of the problems of the current modeling methods for the statistical characteristics of matching probability and measurement error, the modeling method of matching probability and measurement error statistical characteristics based on machine learning is proposed. According to the requirements of total sequence matching probability and system matching times, the sequence matching probability is calculated. The measurement error is analyzed in the process of acquisition and matching, and the measurable interference parameters are obtained. According to the analysis results, the mean value of matching measurement error is standardized, and the matching probability and measurement error statistical characteristics are established sex model. The experimental results show that the matching probability and measurement error statistical model of this method has high accuracy, and has good application effect in practical application.
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