文章基本信息

标题：Optimization numerical the neural architectures by performance indicator with LM learning algorithms
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作者：El Badaoui H. ; Abdallaoui A. ; Chabaa S. 等
期刊名称：Journal of Materials and Environmental Science
印刷版ISSN：2028-2508
出版年度：2017
卷号：8
期号：1
页码：169-179
出版社：University of Mohammed Premier Oujda
摘要：The objective of this study is to develop a mathematical model based on the MLP Artificial Neural Networks(ANN) to predict meteorological parameters in general and moisture in Particular. For this purpose, we used atime series of moisture, Measured in the area of Chefchaouen in Morocco, which depends on the air temperature,dew point temperature, atmospheric pressure, visibility, cloud cover, wind speed and precipitation. Furthermore,to choose the best architecture of the MLP neural network, we used several statistical Criteria such as: Root MeanSquared Error, Mean Absolute Percentage Error, Akaike Information Criterion, Bayesian Information Criterion,Mean Absolute Error and correlation coefficient. The obtained results of the MLP artificial neural network arediscussed and compared to the MLR traditional method. Consequently, MLP method presents a very powerfulability to predict relative moisture. We have shown also that the structure of the MLP neural network {7-5-1}using the Levenberg-Marquart algorithm, and hyperbolic tangent functions and purelin as transfer function torqueis the model the most efficient for predict the moisture in the region Chefchaouen.
关键词：Criteria Information; ANN; Prediction; Moisture spleen; MLP; MLR