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机器学习和深度学习算法的比较分析,用于使用NIRS评估农产品质量.

Jiwen Ren1, Yuming Xiong1, Xinyu Chen2

  • 1School of Mechatronics and Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China.

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概括

与浅层学习 (SL) 相比,深度学习 (DL) 模型显著提高了近红外光谱 (NIRS) 分析准确性. 一个新的格拉米安角差场和卷积神经网络 (G-CACNN) 模型证明了NIRS应用的卓越稳定性和耐噪性能.

关键词:
格拉米安的角度差距场.卷积神经网络是一种卷积神经网络.协调注意力,协调注意力.接近红外光谱学近红外光谱学强大的模型 强大的模型

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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:

  • 近红外光谱 (NIRS) 分析的成功取决于精确的校准模型.
  • 浅层学习 (SL) 算法与光谱数据复杂性和噪声作斗争,限制了NIRS应用程序.
  • 深度学习 (DL) 提供了从有限的光谱样本中改进特征提取的潜力.

研究的目的:

  • 评估NIRS校准模型的稳定性和有效性.
  • 为了比较SL,共识学习 (CL) 和DL方法的性能.
  • 提出和验证一个新的G-CACNN模型用于NIRS歧视性分析.

主要方法:

  • 利用小麦核和Yali梨数据集的歧视性分析.
  • 与DL和CL模型进行了部分最小平方差分分析 (PLS-DA) 的比较.
  • 开发了格拉米安角差场和协调注意力卷积神经网络 (G-CACNN) 模型.

主要成果:

  • DL和CL模型对光谱预处理的敏感性低于SL.
  • 提出的G-CACNN模型在分辨任务中实现了高精度 (98.48%和99.39%).
  • 与其他车型相比,G-CACNN表现出优越的坚固性和抗噪能力.

结论:

  • 深度学习显著提高了NIRS分析的准确性和稳定性.
  • G-CACNN模型为NIRS应用提供了一种强大且耐噪的方法.
  • 这项研究推动了NIRS校准模型的开发,使其具有更广泛的适用性.