Neural networks - methodology and applications

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Online-SVR for short-term traffic flow prediction under typical and atypical traffic conditions (2009, background): “NNet are nonlinear devices that are trained in a supervised manner to adjust their weights to minimize an objective function (Dreyfus, 2005).”
Estimation of lignocellulosic biomass pyrolysis product yields using artificial neural networks (2021, methodology): “This selection was carried out by performing two kinds of tests (hereby called powerset and sequential), as described in [60].”
Optimal power flow: a bibliographic survey II (2012, methodology): “ANN energy function weights – [44] learning-based data processing algorithm mainly used for classification and pattern recognition; training sets for optimization problems not required; can handle non-linearities and integrality; suitable for on-line OPF problems as high-speed implementations are po”
The Perils of Ignoring Data Suitability - The Suitability of Data used to Train Neural Networks Deserves More Attention (2011, background): “More recent neural network texts such as (Dreyfus, 2005), (Bishop, 2006) and (Tang et al., 2007) show a similar omission.”
Computationally efficient FLANN-based intelligent stock price prediction system (2009, methodology): “The return from trading based on this model is shown to be better than buy-and-hold strategy and the random walk model [7]-[8].”