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基于改进粒子群算法的小波钢液光谱的预处理

Pretreatment of Wavelet Steel Liquid Spectra Based on Improved Particle Swarm Optimization
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摘要 在激光诱导击穿技术(LIBS)分析钢液元素成分时,对钢液产生的光谱谱线有较高的要求,谱线是由高能激光束照射钢液表面产生的等离子体发射而得,处理这些谱线数据是钢液光谱分析的重要步骤,因为钢液谱线的产生过程中伴随着大量谱线重叠、谱线间自吸收以及畸形等问题,对研究结果的有一定影响。为了对光谱信号进行去噪分析,采用小波阈值的方法能够简单有效地达到去噪的目的,同时在基于小波阈值去噪的算法中加入改进粒子群算法,改进惯性权重参数,设置较为合理的惯性权重是避免陷入局部最优解的关键,对钢液产生的谱线数据进行去噪,能得到较为理想的谱线信息。 In order to denoise the spectral signal,the wavelet threshold method can be used to achieve the purpose of denoising,the improved particle swarm optimization algorithm is added to the algorithm based on wavelet threshold denoising,and the inertia weight can be improved by setting the reasonable inertia weight to avoid the key of the local optimal solution.The spectral data of the molten steel can be denoised to the more ideal spectral information.
出处 《工业控制计算机》 2017年第12期78-79,82,共3页 Industrial Control Computer
基金 国家自然科学基金(61271402)
关键词 小波分析 粒子群算法 阈值去噪 光谱谱线 wavelet analysis particle swarm pptimization threshold denoising spectral lines
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