The study for optimization of chromatographic condition by means of artificial neural networks.
In this paper, the optimization of gas chromatographic experimental parameters is investigated using a three layer feed-forward neural network with the back-propagating. The design, development, and testing of the neural network are described in detail. The chosen structure is 4-6-2 system with a learning rate eta of 0.6 and a momentum constant mu of 0.4. The results of several simulations are very satisfactory. Network results are compared with the results obtained by the orthogonal method.
Guo
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Department of Chemistry, Hangzhou University, Hangzhou, 310028, People's Republic of China.
Zhu
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Brodowsky
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