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基于量子K近邻分类算法的软件应用层接口数据分析方法

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由于软件应用层接口数据的分布具有星散分布的特点,因此,分析数据判断接口状态时,对应的可靠性难以得到保障.为此,本文提出基于量子K近邻分类算法的软件应用层接口数据分析方法.将接口数据的特征映射到量子比特后,使用量子态编码具体的接口数据.在分析阶段,采用交叉验证的方式确定最佳K值,并选择曼哈顿距离作为衡量数据点之间相似性的基准,设置分类中心为软件应用层接口数据分析的目标状态参数,根据量子化软件应用层接口数据与状态分类中心的距离,完成数据分析.测试结果表明,设计方法对于软件应用层接口状态的分析结果与监测值具有较高的拟合度.
A Software Application Layer Interface Data Analysis Method Based on Quantum K-nearest Neighbor Classification Algorithm
Due to the scattered distribution of software application layer interface data,when analyzing data to determine interface status,the corresponding reliability is difficult to guarantee.To this end,a software application layer interface data analysis method based on quantum K-nearest neighbor classification algorithm is proposed.After mapping the characteristics of interface data to quantum bits,encode specific interface data using quantum states;During the analysis phase,cross validation is used to determine the optimal K value,and choose the Manhattan distance as the benchmark to measure the similarity between data points,set the center of classification as the target state parameter for software application layer interface data analysis,complete data analysis based on the distance between the interface data of the quantization software application layer and the state classification center.The test results indicate that,the design method has a high degree of fit between the analysis results of software application layer interface status and monitoring values.

quantum K-nearest neighbor classification algorithmsoftware application layer interface dataquantum bitsK-value

韩亦睿

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伦敦大学学院,英国

量子K近邻分类算法 软件应用层接口数据 量子比特 K值

2024

软件
中国电子学会 天津电子学会

软件

影响因子:1.51
ISSN:1003-6970
年,卷(期):2024.45(7)