首页|光谱预处理小波基函数的选择对结合FIBS技术和机器学习的铝合金识别精确度影响研究

光谱预处理小波基函数的选择对结合FIBS技术和机器学习的铝合金识别精确度影响研究

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随着经济不断发展，工业建筑领域产生了大量的废弃铝合金材料，对废弃铝合金材料分类回收可以提升废弃资源的利用效率，缓解能源紧张。选取了工业领域常用的五种型号的铝合金，开展了等离子体丝诱导击穿光谱(FIBS)光谱预处理小波变换基函数对铝合金分类识别精度的影响的研究。分别采用bior2。2、bior2。4和bior2。6正交小波基函数对铝合金的FIBS光谱进行预处理，结合线性判别分析(LDA)、网格搜索优化的支持向量机(GSSVM)和反向传播神经网络(BPNN)实现了铝合金型号的快速分类识别。结果表明bior2。2、bior2。4和bior2。6正交小波基函数结合LDA-GSSVM实现铝合金型号的平均识别准确率为90％、100％和76。67％，结合LDA-BPNN实现铝合金型号的平均识别准确率为96。67％、100％和90％，因此选择合适的正交小波基函数对FIBS光谱预处理，对于提高铝合金型号识别准确率有较大作用。
The effect of spectral preprocessing wavelet transform basis functions selection on the classification accuracy of aluminum alloys combining FIBS and machine learning
With the continuous development of the economy,a large amount of waste aluminum alloy material has been generated in the industrial construction sector.The classification and recycling of waste aluminum alloy materials can enhance the utilization efficiency of waste resources and alleviate energy tension.This paper selects five types of a-luminum alloys commonly used in the industrial field to investigate the influence of filament-induced breakdown spec-troscopy(FIBS)spectral preprocessing wavelet transform basis functions on the classification accuracy of aluminum al-loys.The orthogonal wavelet basis functions bior2.2,bior2.4,and bior2.6 are respectively used for preprocessing the FIBS spectrum of aluminum alloys,and the rapid classification identification of aluminum alloy types is achieved by combining with linear discriminant analysis(LDA),grid search optimized support vector machine(GSSVM)and back propagation neural network(BPNN).The results show that the average recognition accuracy rates of aluminum alloy types achieved by orthogonal wavelet basis functions bior2.2,bior2.4,and bior2.6 combining with LDA-GSSVM are 90％,100％,and 76.67％,combining with LDA-BPNN are96.67％,100％,and90％,respectively.Therefore,choosing appropriate orthogonal wavelet basis functions for FIBS spectral preprocessing methods and classification algorithm plays a significant role in improving the recognition accuracy of aluminum alloy types.

filament-induced breakdown spectroscopy(FIBS)orthogonal wavelet basis functionlinear dis-criminant analysis(LDA)classification recognitionaluminum alloy materials

于海龙、高宇瑾、谢云双、杨硕、汤宇轩、高勋、林景全

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长春理工大学物理学院,长春 130022

长春理工大学中山研究院,广东中山 528400

FIBS 正交小波基函数 线性判别分析 分类识别 铝合金材料

吉林省自然科学基金大学生创新创业训练计划

20220101035JC2002165

2024

10.14016/j.cnki.jgzz.2024.06.088

激光杂志
重庆市光学机械研究所

激光杂志

CSTPCD北大核心
影响因子：0.74
ISSN：0253-2743
年,卷(期)：2024.45(6)
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