Combining artificial neural networks and experimental design to prediction of kinetic rate constants

DOI10.1007/S10910-013-0170-7zbMath1264.92057OpenAlexW2072899313MaRDI QIDQ2375867

J. L. González-Hernández, Sonsoles Encinar, M. Mar Canedo

Publication date: 25 June 2013

Published in: Journal of Mathematical Chemistry (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10910-013-0170-7

zbMATH Keywords

artificial neural networksexperimental designs

Mathematics Subject Classification ID

Probabilistic models, generic numerical methods in probability and statistics (65C20) Learning and adaptive systems in artificial intelligence (68T05) Classical flows, reactions, etc. in chemistry (92E20)

Related Items (3)

ANN-MATOPT hybrid algorithm: determination of kinetic and non-kinetic parameters in different reaction mechanisms ⋮ Computational and experimental approaches for investigating membranes diffusion behavior in model diesel fuel ⋮ A robust hybrid algorithm (neural networks-AGDC) applied to non-isothermal kinetics of consecutive chemical reactions

Uses Software

• chemCal
• Unnamed Item

Cites Work

• KINMODEL (AGDC): a multipurpose computational method for kinetic treatment

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