利用神经网络模型集成改进近红外光谱校正
Improved Calibration of Near-Infrared Spectra by Using Ensembles of Neural Network Models
摘要：
IR或近红外（NIR）光谱是用于识别化合物或分析材料成分的方法。近红外光谱的校准是指使用光谱作为多变量描述符来预测组分的浓度。为了建立校准模型，最先进的软件主要使用线性回归技术。对于非线性校准问题，基于神经网络的模型已被证明是一个有趣的替代方案。在本文中，我们提出了一种新的扩展传统的神经网络为基础的方法，使用集成的神经网络模型。个体神经网络是通过用自举或交叉验证技术对可用的训练数据进行重新排序而获得的。一个现实的校准例子得到的结果表明，基于集成的方法产生一个显着更准确和强大的校准模型比传统的回归方法。
英文摘要：
IR or near-infrared (NIR) spectroscopy is a method used to identify a compound or to analyze the composition of a material. Calibration of NIR spectra refers to the use of the spectra as multivariate descriptors to predict concentrations of the constituents. To build a calibration model, state-of-the-art software predominantly uses linear regression techniques. For nonlinear calibration problems, neural network-based models have proved to be an interesting alternative. In this paper, we propose a novel extension of the conventional neural network-based approach, the use of an ensemble of neural network models. The individual neural networks are obtained by resampling the available training data with bootstrapping or cross-validation techniques. The results obtained for a realistic calibration example show that the ensemble-based approach produces a significantly more accurate and robust calibration model than conventional regression methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
