In this paper, radial basis neural network (RB-NN) was proposed for the ide
ntification of volatile organic compounds (VOCs). The measuring system with
four 20 MHz quartz crystal microbalances (QCMs) as sensors was used in the
experiments. The four sensors were modified with SnCl2 and PdCl2 to change
the response characteristics. A flow-through type system was used to measu
re the VOC samples including ethyl alcohol, acetone, chloroform, and de-ion
ized water. Rise-time, peak, and fall-time data from the response character
istic curves were used as information for training the neural networks. It
was found that the RB-NNs could be learned faster and better than the conve
ntional back-propagation neural networks (BP-NNs). The samples were clearly
separated and recognized with the RB-NNs, which could not be done with the
BP-NNs. (C) 2000 Elsevier Science S.A All rights reserved.