摘要
体育运动数据特征提取的过程模糊性强、关联性弱,与其他数据不同,体育运动数据多指标之间呈现线性关联过程,但这一特点未被当前的经典融合算法利用,使融合后覆盖率低。文中设计一种基于线性拟合的体育运动数据特征融合方法。首先构建特征提取模型,再通过监测体系,得到数据特征提取的更新规则,统计提取的数据特征并去噪,最后用回归分析和线性拟合方法对其量化,完成体育运动数据特征融合。结果表明:所提方法的覆盖率较高,可靠性较强,在不同等级的高斯噪声下,信噪比均较高,提取效果好,且能耗低,拟合度高。
The process of feature extraction of sports data has strong fuzziness and weak correlation.Different from other data,the multiple indicators of sports data show a linear correlation process,but this feature is not used by the current classical fusion algorithm,making the fusion coverage low.A method of sports data feature fusion based on linear fitting is designed.Firstly,a feature extraction model is built,and then the updated rules of data feature extraction are adopted through the monitoring system.Nextly,the extracted data features are counted and denoised.Finally,regression analysis and linear fitting methods are used to quantify the features thus completing the sports data feature fusion.The results show that the proposed method has high coverage and high reliability.Under different levels of Gaussian noise,the SNR is high,the extraction effect is good,the energy consumption is low,and the fitting degree is high.
作者
曹扬
CAO Yang(Yangling Vocational&Technical College,Xianyang 712100,Shaanxi Province,China)
出处
《信息技术》
2024年第6期118-123,129,共7页
Information Technology
基金
陕西省自然科学基金(2020AH-268)。
关键词
体育运动
模糊性
生理指标数据
特征提取
线性拟合
sports
fuzziness
physiological index data
feature extraction
linear fitting
