摘要
本文将反向传播人工神经网络（ＢＰ－ＡＮＮ）用于ＩＣＰ－ＡＥＳ中重叠光谱干扰的校正。利用模拟的Ｃｅ４１３．３８０ｎｍ和Ｐｒ４１３．３６１ｎｍ光谱对神经网络的训练方式、输入值范围、噪声影响等作了较详细的讨论。
A back propagation artificial neural network (BP ANN) has been applied to correcting spectral overlap interference in inductively coupled plasma atomic emission spectrometry(ICP AES).Some network parameters including the range of input values and training sequence for training patterns presented to the network were discussed using simulated Ce 413 380nm and Pr 413 380nm line profiles.Results show that the noise in simulated mixture spectra will slow down the network convergence and has more influence on network prediction.
出处
《光谱学与光谱分析》
SCIE
EI
CAS
CSCD
北大核心
1997年第5期77-81,共5页
Spectroscopy and Spectral Analysis
基金
国家自然科学基金
吉林省科技发展项目
关键词
人工神经网络
光谱干扰校正
ICP
AES
Inductively coupled plasma atomic emission spectrometry, Artificial neural network, Spectral interference correction
