模式识别用于压电晶体传感器阵列识别可燃物质

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％）。

Detection of Combustible Material with Piezoelectric Crystal Sensor Array Using Pattern-Recognition Techniques

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.