Journal Article10.1016/S1352-2310(97)00447-0
Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences
M.W. Gardner,Stephen Dorling +1 more
3.2K
TL;DR: This paper presents a general introduction and discussion of recent applications of the multilayer perceptron, one type of artificial neural network, in the atmospheric sciences.
read more
About: This article is published in Atmospheric Environment. The article was published on 01 Aug 1998. The article focuses on the topics: Multilayer perceptron & Artificial neural network.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Estimating sex and age from a face: a forensic approach using machine learning based on photo-anthropometric indexes of the Brazilian population
Lucas Faria Porto,Laíse Nascimento Correia Lima,Ademir Franco,Donald M. Pianto,Carlos Eduardo Palhares Machado,Flavio de Barros Vidal +5 more
TL;DR: This manuscript presents an approach using photo-anthropometric indexes, generated from frontal faces cephalometric landmarks of the Brazilian population, to create an artificial neural network classifier that allows the estimation of anthropological information, in this specific case age and sex.
25
Machine learning based bias correction for numerical chemical transport models
TL;DR: An approach based on machine learning is applied to predict model bias in the CTM and it is then combined with the C TM for formulating a hybrid forecast system, the first time that machine learning methods are used in this way.
25
Machine leaning aided study of sintered density in Cu-Al alloy
Zhenghua Deng,Haiqing Yin,Xue Jiang,Cong Zhang,Kai-qi Zhang,Tong Zhang,Bin Xu,Qingjun Zheng,Xuanhui Qu +8 more
TL;DR: In this article, the multilayer perceptron model (MLP) outperformed other four regression and neutral network models with high coefficient of correlation and low error for predicting the sintered density of powder metallurgy materials.
25
New Neural Network Methods for Forecasting Regional Employment: An Analysis of German Labour Markets
TL;DR: In this paper, a set of neural network (NN) models is developed to compute short-term forecasts of regional employment patterns in Germany, with a 2-year forecasting range.
Prediction of shear capacity of steel channel sections using machine learning algorithms
Madhushan Dissanayake,Hoang Chuong Ngo Nguyen,Keerthan Poologanathan,Gatheeshgar Perampalam,I.R. Upasiri,Heshachanaa Rajanayagam,Thadshajini Suntharalingam +6 more
TL;DR: In this paper , support vector regression (SVR), multi-layer perceptron (MLP), gradient boosting regressor (GBR), and extreme gradient boosting (XGB) were used to predict the shear resistance of steel channel sections.
25
References
Multilayer feedforward networks are universal approximators
TL;DR: It is rigorously established that standard multilayer feedforward networks with as few as one hidden layer using arbitrary squashing functions are capable of approximating any Borel measurable function from one finite dimensional space to another to any desired degree of accuracy, provided sufficiently many hidden units are available.
23.1K
•Book
Classification and regression trees
Leo Breiman
- 01 Jan 1983
TL;DR: The methodology used to construct tree structured rules is the focus of a monograph as mentioned in this paper, covering the use of trees as a data analysis method, and in a more mathematical framework, proving some of their fundamental properties.
22.7K
•Book
Neural networks for pattern recognition
Christopher M. Bishop
- 01 Jan 1995
TL;DR: This is the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition, and is designed as a text, with over 100 exercises, to benefit anyone involved in the fields of neural computation and pattern recognition.
Learning internal representations by error propagation
David E. Rumelhart,Geoffrey E. Hinton,Ronald J. Williams +2 more
- 01 Jan 1988
TL;DR: This chapter contains sections titled: The Problem, The Generalized Delta Rule, Simulation Results, Some Further Generalizations, Conclusion.