应用化学计量学技术研究同时定量分析重叠光谱

在八种化学计量学除噪技术比较研究的基础上,研制了小波包变换Elman回归神经网络方法(WPERNN)用于研究重叠光谱的同时定量测定。结合小波包变换和Elman回归神经网络改进除噪质量及回归能力。通过最佳化,选择了小波函数、小波包分解水平和Elman回归神经网络(ERNN)的结构及参数。两个程序PWPERNN和PERNN被设计执行WPERNN和ERNN方法计算。七种化学计量学方法用于比较研究。实验结果显示WPERNN方法是成功的且优于其他六种方法。

Studies on Simultaneous Quantitative Determination of Overlapping Spectra Using Chemometric Techniques

Based on comparative study of eight chemometric denoising methods,a wavelet packet transform Elman recurrent neural network(WPERNN)method was developed to study simultaneous quantitative determination of overlapping spectra.The quality of noise removal and ability of regression were improved by combining wavelet packet transform with Elman recurrent neural network.Through optimization,the wavelet function,the wavelet packet decomposition levels as well as the structure and parameters of Elman recurrent neural network were selected.Two programs,PWPERNN and PERNN,were designed to perform WPERNN and ERNN calculation.Seven kinds of chemometric methods were applied in the present study for comparison.Experimental results showed that the WPERNN method was successful and better than the other 6 methods.