Publication Details

Category Text Publication
Reference Category Journals
DOI 10.1002/etc.5620130508
Title (Primary) Back-propagation neural networks-recognition vs. prediction capability
Author Schüürmann, G.; Müller, E.
Journal Environmental Toxicology and Chemistry
Year 1994
Department OEC; COE
Volume 13
Issue 5
Page From 743
Page To 747
Language englisch
Keywords Neural network; Back-propagation; Prediction error; Leave-re-out procedure; Biodegradation
Abstract 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.
Persistent UFZ Identifier
Schüürmann, G., Müller, E. (1994):
Back-propagation neural networks-recognition vs. prediction capability
Environ. Toxicol. Chem. 13 (5), 743 - 747