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Erschienen in:
2011 | OriginalPaper | Buchkapitel
Key Learnings from Twenty Years of Neural Network Applications in the Chemical Industry
verfasst von : Aaron J. Owens
Erschienen in: Engineering Applications of Neural Networks
Verlag: Springer Berlin Heidelberg
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This talk summarizes several points that have been learned about applying Artificial Neural Networks in the chemical industry. Artificial Neural Networks are one of the major tools of
Empirical Process Modeling
, but not the only one. To properly assess the appropriate model complexity, combine information about both the
Training
and the
Test
data sets. A neural network, or any other empirical model, is better at making predictions than the comparison between modeled and observed data shows. Finally, it is important to exploit synergies with other disciplines and practitioners to stimulate the use of Neural Networks in industry.