Research on Classification Algorithm of Multi-band Laser Spectrum based on Data Mining

ZHAO Li-li

doi:10.13756/j.gtxyj.2020.06.004

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Research on Classification Algorithm of Multi-band Laser Spectrum based on Data Mining

- Research on Classification Algorithm of Multi-band Laser Spectrum based on Data Mining
- Study on Optical communication Issue 6, Pages: 12-16(2020)
- 作者机构：
  
  长春理工大学光电信息学院
- 作者简介：
- 基金信息：
- DOI：10.13756/j.gtxyj.2020.06.004
  CLC： TP311.13
- Published：2020，
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[1]赵丽莉.基于数据挖掘的多波段激光数据分类算法研究[J].光通信研究,2020(06):12-16.

ZHAO Li-li. Research on Classification Algorithm of Multi-band Laser Spectrum based on Data Mining. [J]. (6):12-16(2020)
[1]赵丽莉.基于数据挖掘的多波段激光数据分类算法研究[J].光通信研究,2020(06):12-16. DOI： 10.13756/j.gtxyj.2020.06.004.

ZHAO Li-li. Research on Classification Algorithm of Multi-band Laser Spectrum based on Data Mining. [J]. (6):12-16(2020) DOI： 10.13756/j.gtxyj.2020.06.004.

摘要

针对多波段激光干涉图像中的频谱数据混叠问题

文章提出了一种基于数据挖掘技术的自相关提取分类算法。首先

采用自相关计算模式完成数据属性分类;然后

通过属性隶属度函数作为判据设计了基于特征数据挖掘的自回归平均模型;最后

采用时序衰减权值系数循环迭代的方式完成数据挖掘。实验对混合激光光源进行测试

并与传统均值分类算法和最小二乘法分类算法作了对比。仿真结果表明

该算法的分类平均误差为0.110

优于另外两种算法（0.191和0.167）

并且在相同数据总量的条件下该算法的耗时最少。由此可见

该算法对多波段激光干涉图像中混叠数据的有效分离具有很好的改善作用

在混叠数据分类应用中具有一定的实用价值。

Abstract

Aiming at the problem of spectral data aliasing in multi-band laser interference images

this paper proposes an autocorrelation extraction classification algorithm based on associated feature information. First

the auto-correlation calculation mode is used to complete the data attribute classification. Then

an autoregressive average model based on feature data mining is designed by using attribute membership function as a criterion. Finally

the data mining is completed by cyclic iteration of time-decay weight coefficients. In the experiment

a mixed laser light source is tested. The algorithm is compared with traditional mean classification algorithm and least square method classification algorithm. Experimental results show that the classification error of the algorithm is 0.110

which is better than the other two algorithms（0.191 and 0.167）. And under the condition of the same total amount of data

the proposed algorithm takes the least time. It can be seen that this algorithm has a very good effect on the effective separation of aliasing data in multi-band laser interference images. It has certain practical value in the application of aliasing data classification.

关键词

多波段光谱数据特征提取数据挖掘自相关计算模型

Keywords

multi-band spectral datafeature extractiondata miningautocorrelation calculation model

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The Ministry of Education Key Laboratory of Electromagnetic Wave Information Science, Department of Communication Science and Engineering, Fudan University

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Deparment of Electronic Engineering,Naval Aeronautical and Astronautical University

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