温度限制串联相关网络用于有机环境污染物紫外光谱的识别

本文将温度限制串联相关网络用于有机环境污染物紫外光谱的识别。紫外光谱的库检索比红外光谱检索更困难 ,因为紫外光谱的重叠更为严重。此外 ,光谱测量的漂移和噪声也会影响紫外光谱库检索的正确率。因此 ,采用具有模糊性质的神经网络是一个很好的选择。温度限制串联相关网络 (TCCCN)是一种与通常所用BP网络不同结构的网络模型 ,它采用串联相关的神经元连续方式 ,且引入温度参数 ,因而可以减少网络的过度训练和加快训练速度。本工作采用TCCCN进行紫外光谱的库检索 ,对有关参数进行了优化 ,并对光谱测量噪声的影响做了研究。结果表明 ,采用TCCCN方法明显优于在谱库检索中常用的相关系数法。

Library Search of UV Spectra of Organic Environmenttal Pollutants by Temperature-constrained Cascade-Correlation Networks

A temperature-constrained cascade-correlation network (TCCCN) was used to identify ultraviolet (UV) spectra of organic enviromental pollutants. Library search for UV spectra is more difficult than that for infrared (IR) spectra, because the UV spectra overlap more severely than IR spectra. Besides, drift and noise in the measurement will have significant effect on UV library spectra search. Therefore, neural networks with fuzzy output should be a better alternative for the library search. The TCCCN is different from the commonly used BP networks in architecture. The processing units in the TCCCN are connected in a cascade mode, and a temperature constraint is introduced. Therefore, the TCCCN can reduce overtraining and fast training speed. TCCCN was used for library search of UV spectra in the present work and the effects of network parameters and noise were investigated. Results showed that better results were obtained with the TCCCN than with conventional correlation method.