基于激光特征数据分类的数学建模仿真分析
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摘要
为了提高激光大数据的准确分类性能,构建激光特征数据分类数学模型,提出一种基于向量量化分解和自相关特征提取的激光特征数据分类数学建模方法。构建激光特征数据的相空间分布结构模型,分析激光特征数据的量化结构特征,采用关联规则挖掘方法进行激光特征数据的属性类别挖掘,对挖掘的属性类别特征量采用向量量化分解方法进行结构重排,构建激光特征数据分类的检验统计量和判决准则,采用自相关特征提取方法进行数据分类识别,实现激光特征数据分类数学模型优化建模。仿真结果表明,采用该方法进行激光特征数据分类的准确性较高,误分率较小。
In order to improve the accuracy of large laser data classification,a mathematical model of laser feature data classification is constructed. A mathematical modeling method of laser feature data classification based on vector quantization decomposition and auto-correlation feature extraction is proposed. The phase space distribution structure model of laser feature data is constructed and the quantitative structure features of laser feature data are analyzed. Association rule mining method is used to mine the attributes of laser feature data. Vector quantization decomposition method is used to rearrange the features of the attributes. The inspection statistics and decision criteria of laser feature data classification are constructed. The data classification and recognition are carried out by using auto-correlation feature extraction method. The simulation results show that the method has high accuracy and low misclassification rate.
In order to improve the accuracy of large laser data classification,a mathematical model of laser feature data classification is constructed. A mathematical modeling method of laser feature data classification based on vector quantization decomposition and auto-correlation feature extraction is proposed. The phase space distribution structure model of laser feature data is constructed and the quantitative structure features of laser feature data are analyzed. Association rule mining method is used to mine the attributes of laser feature data. Vector quantization decomposition method is used to rearrange the features of the attributes. The inspection statistics and decision criteria of laser feature data classification are constructed. The data classification and recognition are carried out by using auto-correlation feature extraction method. The simulation results show that the method has high accuracy and low misclassification rate.
引文
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[4] GUO H,LIU H,WU C,et al.Logistic discrimination based on G-mean and F-measure for imbalanced problem[J].Journal of Intelligent and Fuzzy Systems,2016,31(3):1155-1166.
[5] XU Y,TONG S,LI Y.Prescribed performance fuzzy adaptive fault-tolerant control of non-linear systems with actuator faults[J].IET Control Theory and Applications,2014,8(6):420-431.
[6]侯森,罗兴国,宋克.基于信息源聚类的最大熵加权信任分析算法[J].电子学报,2015,43(5):993-999.
[7]张永,李卓然,刘小丹.基于主动学习SMOTE的非均衡数据分类[J].计算机应用与软件,2012,29(3):91-93.
[8]袁泉,郭江帆.新型含噪数据流集成分类的算法[J].计算机应用,2018,38(6):1591-1595.
[9]姜栋瀚,林海涛.云计算环境下的资源分配关键技术研究综述[J].中国电子科学研究院学报,2018,13(3):308-314.
[10]陈志旺,黄兴旺,陈志兴,等.区间多目标优化非支配排序云模型算法[J].计算机工程与应用,2017,53(22):143-149.
[11]王强,张合新,孟飞,等.基于结构相似度评价方法的窗口效应[J].激光技术,2016,40(6):902-906.
[12] MOHAMMADZADE H,HATZINAKOS D. Iterative closest normal point for 3D face recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2013,35(2):381-397.
[13]曾志,王晋,杜震洪,等.一种云格环境下可计算大型电气数据库的云存储资源与服务高效调配机制[J].浙江大学学报(理学版),2014,41(3):353-357.
[14]朱彦松,窦桂琴.一种基于多维度信任的WSN安全数据融合方法[J].武汉大学学报(理学版),2013,59(2):193-197.
[15]白曜铭,蒋建中,孙有铭,等.采用FFT方法的抗阶数过估计信道盲辨识算法[J].信号处理,2014,30(1):65-71.
[16]罗红英,屈英,俞元洪.具有正负系数的二阶中立型时滞Emden-Fowler方程的振动准则[J].应用数学学报,2017,40(5):667-675.
[17]郭华平,董亚东,毛海涛,等.一种基于逻辑判别式的稀有类分类方法[J].小型微型计算机系统,2016,37(1):140-145.