基于粒子群算法的最小二乘支持向量机在红花提取液近红外定量分析中的应用OA北大核心CSCDCSTPCD
Application of Particle Swarm Optimization Based Least Square Support Vector Machine in Quantitative Analysis of Extraction Solution of SafflowerUsing Near-infrared Spectroscopy
提出一种基于粒子群算法的最小二乘支持向量机(PSO-LS-SVM)方法,用于建立红花提取过程关键质控指标的定量分析模型.近红外光谱数据经波段选择、预处理和主成分分析(降维)后,利用粒子群优化(PSO)算法对最小二乘支持向量机算法中的参数进行优化,然后使用最优参数建立固含量和羟基红花黄色素A(HSYA)浓度的定量校正模型.将校正结果与偏最小二乘法回归(PLSR)和BP神经网络(BP-ANN)比较,并将所建的3个模型用于红花提取过程未知样本的预测.结…查看全部>>
A novel particle swarm optimization(PSO) based least squares support vector machine (LS-SVM) method was investigated for quantitative analysis of extraction solution of safflower using near-infrared (NIR) spectroscopy. The usable spectral region (5400 6500 cm-1) was identified, spectral preprocessing of Norris derivative smoothing was employed, and spectral dimension was also reduced through principal component analysis(PCA). In this paper, the PSO algorit…查看全部>>
金叶;杨凯;吴永江;刘雪松;陈勇
浙江大学药学院,杭州310058浙江大学药学院,杭州310058浙江大学药学院,杭州310058浙江大学药学院,杭州310058浙江大学药学院,杭州310058
近红外光谱粒子群优化最小二乘支持向量机红花提取液
Near-infrared Spectroscopy Particle swarm optimization Least squares support vactor machine Extraction solution of safflower
《分析化学》 2012 (6)
925-931,7
本文系浙江省重大科技计划项目(No.2008C0305)和国家“十一五”科技支撑计划项目(Na.2006BAI06A08)资助
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