一种混合变量选择方法,结合了费舍尔的线性分辨器综合人口分析和改进的二进制子搜索算法
Shuobo Chen1, Kang Du1, Baoming Shan1
1College of Automation and Electronic Engineering, Qingdao University of Science & Technology, Qingdao, 266061, P. R. China. f.k.zhang@hotmail.com.
Analytical methods : advancing methods and applications
|February 5, 2024
概括
一种新的混合变量选择方法改进了近红外 (NIR) 光谱技术,用于工业成分测量. 这种方法提高了模型的准确性,并减少了酒,玉米和柴油等产品的预测错误.
科学领域:
- 分析化学 分析化学
- 化学测量 化学测量 化学测量
- 频谱学是一种光谱学.
背景情况:
- 精确的成分测量对于工业过程控制至关重要.
- 近红外 (NIR) 光谱技术提供了一种快速,非破坏性的分析技术.
- 变量选择对于构建可靠的化学测量模型至关重要,尤其是在多对线光谱数据中.
研究的目的:
- 提出一种新的混合变量选择方法,用于基于近红外光谱 (NIR) 的模型构建.
- 提高工业过程中成分测量的准确性和可靠性.
- 为应对光谱数据多线性所带来的挑战.
主要方法:
- 一个双层变量选择策略,结合了费舍尔的线性歧视组合人口分析 (FCPA) 和改进的二进制子搜索算法 (IBCS).
- FCPA用于信息变量间隔的初始粗略定位,处理多对线性.
- IBCS,增强了基于对立的学习 (OBL) 和跳转基因 (JG),用于精确选择关键变量,避免局部最佳.
主要成果:
- 与其他方法相比,拟议的FCPA-IBCS方法在变量选择方面表现优越.
- 使用FCPA-IBCS构建的部分最小平方 (PLS) 模型显示出更高的适配精度.
- 对于预测酒提取物,玉米蛋白/粉和柴油燃料沸点的校准模型,观察到较少的预测错误.
结论:
- 混合FCPA-IBCS变量选择方法在工业应用中的NIR光谱分析中是有效的.
- 这种方法提高了化学测量模型用于成分测量的准确性和稳定性.
- 该方法成功地导航了多对线性,并优化了变量选择,以提高预测性能.
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