El Badaoui, H. and Abdallaoui, A. and Chabaa, S. (2017) Optimization numerical the neural architectures by performance indicator with LM learning algorithms. Journal of Materials and Environmental Science, 8 (1). pp. 169-179.

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Abstract

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 a time 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 Mean Squared 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 are discussed and compared to the MLR traditional method. Consequently, MLP method presents a very powerful ability 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 torque is the model the most efficient for predict the moisture in the region Chefchaouen.

Item Type: Article
Subjects: Environmental Science
Divisions: SCIENTIFIC PRODUCTION > Environmental Science
Depositing User: Administrateur Eprints Administrateur Eprints
Last Modified: 31 Jan 2020 15:47
URI: http://eprints.umi.ac.ma/id/eprint/3475

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