Abstract The advantage of data-sensitive deep learning methods used in spectral calibration is not obvious when the amount of available data is insufficient. To solve this problem, this paper proposes a new method that combines one-dimensional convolution neural network (1-dim CNN) with negative correlation learning (NCL). First, we create several identical one-dimensional convolutional neural networks as subnetworks of the NCL system. Second, we add the error function of each subnetwork to a negative correlation penalty term that is related to the correlation between the networks and then use this composite error function to back-propagate these networks for parameter adjustment. Finally, after the model has converged, we take the average of the results of all subnetworks as the result of the whole model. We compare CNN_NCL with PLS,creating diversity partial least squares (CDPLS) and a single 1-dim CNN on the pharmaceutical tablet dataset and diesel fuels dataset. The experimental results show that CNN_NCL performs better than PLS and CDPLS when the number of samples is sufficient. Additionally, CNN_NCL can always be more effective than a single CNN regardless of the data scale. Therefore, in the context of the era of big data, CNN_NCL is a fairly efficient model for spectral calibration.

中文翻译：

---

一维卷积神经网络与负相关学习相结合的光谱定标

摘要 当可用数据量不足时，数据敏感的深度学习方法用于光谱校准的优势并不明显。为了解决这个问题，本文提出了一种将一维卷积神经网络（1-dim CNN）与负相关学习（NCL）相结合的新方法。首先，我们创建了几个相同的一维卷积神经网络作为 NCL 系统的子网络。其次，我们将每个子网络的误差函数添加到与网络之间的相关性相关的负相关惩罚项中，然后使用该复合误差函数反向传播这些网络以进行参数调整。最后，模型收敛后，我们取所有子网结果的平均值作为整个模型的结果。我们将 CNN_NCL 与 PLS 进行比较，在药片数据集和柴油燃料数据集上创建多样性偏最小二乘法 (CDPLS) 和单个 1-dim CNN。实验结果表明，当样本数量足够时，CNN_NCL 的性能优于 PLS 和 CDPLS。此外，无论数据规模如何，CNN_NCL 始终比单个 CNN 更有效。因此，在大数据时代背景下，CNN_NCL 是一个相当高效的光谱校准模型。