Volume 13, Issue 5 No AccessArtificial neural networks, back propagation, and the Kelley-Bryson gradient procedureStuart E. DreyfusStuart E. Dreyfus University of California, Berkeley, Berkeley, California 94720Search for more papers by this authorPublished Online:23 May 2012https://doi.org/10.2514/3.25422SectionsPDFPDF Plus ToolsAdd to favoritesDownload citationTrack citations ShareShare onFacebookTwitterLinked InRedditEmail About Previous article Next article FiguresReferencesRelatedDetailsSee PDF for referencesCited byMachine learning to predict effective reaction rates in 3D porous media from pore structural features31 March 2022 | Scientific Reports, Vol. 12, No. 1Federated learning-based short-term building energy consumption prediction method for solving the data silos problem10 December 2021 | Building Simulation, Vol. 15, No. 6Fundamentals and Applications of Artificial Neural Network Modelling of Continuous Bifidobacteria Monoculture at a Low Flow Rate6 May 2022 | Data, Vol. 7, No. 5A hybrid resampling algorithms SMOTE and ENN based deep learning models for identification of Marburg virus inhibitorsFuture 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