Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences

By M. Gardner, S. Dorling. 1998

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Citations in the corpus

Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019 (2019, methodology): “Through DMLP models, problems such as regression and classification can be solved by modeling the input data [46].”
Enforcing Analytic Constraints in Neural Networks Emulating Physical Systems. (2019, methodology): “In the reference case of an “unconstrained network” (UCnet), we optimize a multilayer perceptron [18,19] using MSE as its loss function L.”
Quantile regression neural networks: Implementation in R and application to precipitation downscaling (2011, methodology): “Given the complexity and potential nonlinearity of environmental processes, flexible statistical models, for example artificial neural networks (ANNs) (Gardner and Dorling, 1998; Hsieh and Tang, 1998), have increasingly been used for probabilistic prediction tasks.”
A Survey on ensemble learning under the era of deep learning (2021, methodology): “2006) which was composed of restricted Boltzmann machines (RBMs) (Hinton and Salakhutdinov 2006; Hinton 2012) and multilayer perceptron (MLP) (Gardner and Dorling 1998), resulting in a series of base deep learners.”
CNNpred: CNN-based stock market prediction using a diverse set of variables (2019, background): “LeCun and his colleagues introduced convolutional neural networks in 1995 (LeCun et al., 1995; Gardner & Dorling, 1998).”
Univariate modelling of monthly maximum temperature time series over northeast India: neural network versus Yule–Walker equation based approach (2011, background): “Several authors have proposed artificial neural networks (ANNs) for time series decomposition and prediction (Khan et al., 2005) and its applicability in atmospheric modeling has been thoroughly discussed in Hsieh and Tang (1998) and Gardner and Dorling (1998).”
Applying Neural-Network-Based Machine Learning to Additive Manufacturing: Current Applications, Challenges, and Future Perspectives (2019, background): “Table 1 [39–55] summarizes the application of NNs into AM (in fact, NN is referred to here as MLP, since all the datasets are the tabular type), and lists the input values of the processing parameters and the output values of the property/performance.”
A survey of the recent architectures of deep convolutional neural networks (2019, background): “In this regard, different hardware accelerators are needed for reducing both execution time and power consumption (Geng et al. 2019).”