BACKPROPAGATION NEURAL NETWORKS - RECOGNITION VS PREDICTION CAPABILITY

被引：17
|
作者：
SCHUURMANN, G
MULLER, E
机构：
[1] Centre for Environmental Research Leipzig-Halle, Department of Chemical Ecotoxicology, Leipzig, D-04301
来源：
ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY | 1994年 / 13卷 / 05期
关键词：
NEURAL NETWORK; BACKPROPAGATION; PREDICTION ERROR; LEAVE-N-OUT PROCEDURE; BIODEGRADATION;
D O I：
10.1002/etc.5620130508
中图分类号：
X [环境科学、安全科学];
学科分类号：
08 ; 0830 ;
摘要：
Literature data on biodegradation kinetics of organic compounds, together with a descriptor representation, are subjected to a systematic analysis of the performance of back-propagation neural-network models. The results show distinct dependencies on various model parameters, particularly on the number of iteration cycles. The application of leave-n-out procedures leads to general recommendations for a proper evaluation of the recognition and prediction power of this class of nonlinear structure-activity models.
引用
收藏
页码：743 / 747
页数：5
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