一种新的遗传神经网络组合方法在近红外光谱分析中的应用

近红外光谱分析技术中波长点的选择和建模方法的选取对建立预测分析模型至关重要.在传统相关系数法的基础上,提出了一种基于遗传算法的相关系数阈值优化方法.该方法以决定系数最大为优化目标,寻找最佳阈值.校正时用径向基神经网络来建立定标模型,选取中心时采用正交最小平方法.采用新方法预测了汽油中的碳酸二甲酯含量,把预测结果与偏最小二乘法的实验结果进行了对比.结果表明,新方法的预测精度更高,决定系数可达到0.9993.

Application of a New Genetic Neural Network Combination Algorithm in Near Infrared Spectroscopy

The selection of wavelength points and modeling methods is very important for the establishment of predictive analysis model in near infrared spectroscopy. On the basis of the traditional correlation coefficient method, a correlation coefficient threshold optimization method based on the genetic algorithm is proposed. In the method, the determination coefficient is maximized so as to find the optimal threshold. A radial basis neural network is used to establish a calibration model and an orthogonal least square method is used to select the center for modeling. The new method is used to predict the dimethy carbonate content in gasoline and its result is compared with the experimental result of the partial least square method. The result shows that the new method has a better prediction accuracy. Its determination coefficient is up to 0.9993.