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《Environmental Chemistry》 2000-01
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Wang Shiming Shi Hanchang Qian Yi(Department of Environmental Science and Engineering, State Key Laboratory of Environmental Simulation and Pollution Control,Tsinghua University, Beijing, 100084)  
Based on the substituted functional groups and their positions as the chemical structure descriptors, and the substrates' maximum specific biodegradation rates as the ability of their biodegran-dability. the aromatic compounds' degradabilities were studied using the authors constructed artificial nerual networks. During the experiment, the aromatic compounds' degradabilities were artificially classified as hardly biodegradable, hard biodegradable, biodegradable, and easy biodegradable according their maximum specific biodegradation rates, 10% substances were randomly subtracted as the prediction sets, and the rest were treated as the training sets. The correct prediction rates were 80% , 80%, 40%, and 80% respectively in the total four tests. These results showed that the artificial neural networks has the good ability to predict the biodegradability of aromatic substances.
【CateGory Index】: O621.1
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