Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4754898 | New Biotechnology | 2017 | 26 Pages |
Abstract
A methodology based on artificial neural networks (ANNs) and differential evolution (DE), specifically a neuro-evolutionary approach, was applied to model germination rates, dry biomass and root/stem length and proving the robustness of the experimental data. The errors associated with all four variables are small and the correlation coefficients higher than 0.98, which indicate that the selected models can efficiently predict the experimental data.
Related Topics
Physical Sciences and Engineering
Chemical Engineering
Bioengineering
Authors
Dana LuminiÈa Sobariu, Daniela Ionela Tudorache Fertu, Mariana Diaconu, Lucian Vasile Pavel, Raluca-Maria Hlihor, Elena Niculina DrÄgoi, Silvia Curteanu, Markus Lenz, Philippe François-Xavier Corvini, Maria (Professor),