摘要
用7个压电晶体组成传感器阵列,每个晶体上分别涂有不同种类的气相色谱固定液,通过测定各种可燃物质燃烧时放出的混合气体来识别所燃物质,在识别中分别应用了人工神经网络法(ANN)和逐步判别分析法(SDA).讨论了解决神经网络开始训练时不收敛或产生麻痹现象的方法,提出了训练数据选取的新方法─—训练集逐步扩展法.实验证明:人工神经网络对被测物质的识别准确率达100%,高于逐步判别分析法(83%).
A gas sensors array with seven piezoelectric crystals each coated with a different partially selective coating material was constructed to identify four kinds of combustible material which generate smoke contammg different components. The signals from the sensors were analyzed with both conventional multivariate analysis, stepwise discriminant analysis (SDA), and artificial neural networks (ANN). The results show that the predictions were even better with ANN models. In our experiment, we have reported a new method for training data selection,' stepwise expanding training set method' to solve the problem that the network can not converge at the beginning of training.
出处
《高等学校化学学报》
SCIE
EI
CAS
CSCD
北大核心
1997年第5期696-700,共5页
Chemical Journal of Chinese Universities
基金
国家自然科学基金
关键词
人工神经网络
压电晶体传感器
可燃气体
识别
Artificial neural networks (ANN), Piezoelectric crystal sensor array, Stepwise discriminant analysis(SDA)