[ Perceptron Multicouches et réseau à Fonction de Base Radiale pour la prédiction du taux d'humidité ]
Volume 5, Issue 1, July 2014, Pages 55–67
Hicham EL BADAOUI1, Abdelaziz Abdallaoui2, and Samira CHABAA3
1 Department of Chemistry, Analytical Chemistry and Environment Team, Moulay Ismail University, Faculty of Science, BP. 11201, Zitoune Meknes, Morocco
2 Department of Chemistry, Analytical Chemistry and Environment Team, Moulay Ismail University, Faculty of Science, BP. 11201, Zitoune Meknes, Morocco
3 Industrial engineering Department, Ibn Zohr University, National School of Applied Sciences, BP. 1136, Agadir, Morocco
Original language: French
Copyright © 2014 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
This work presents the development of an artificial neural network model based on the Multi-layer Perceptron (MLP) and Radial Basis Function (RBF) for predicting the moisture in the zone of Chefchaouen (Morocco). Our objective is to treat a chronological series of measured data for network response evaluation. For this reason, we used a basic learning neural model for the years 2008 and 2013, the latter consists of a number of meteorological parameters such as humidity, temperature of the air, dew point temperature, air pressure, visibility, cloud cover, wind speed and rain. To determine the network architecture to be used, we varied the number of hidden number of neurons in hidden layers, transfer functions and pairs of transfer functions and learning algorithms. Models performances have been evaluated and developed through the study of the Mean Squared Error (MSE) and correlation coefficient (R). We demonstrated in this study for the prediction of moisture, the best performing model is the one used as transfer functions, the Tansig function in the hidden layer and the Purelin function in the output layer, while using a learning algorithm LM, PMC type of configuration.
Author Keywords: Moisture, Artificial neural networks, PMC, RBF, Prediction, Learning algorithms.
Volume 5, Issue 1, July 2014, Pages 55–67
Hicham EL BADAOUI1, Abdelaziz Abdallaoui2, and Samira CHABAA3
1 Department of Chemistry, Analytical Chemistry and Environment Team, Moulay Ismail University, Faculty of Science, BP. 11201, Zitoune Meknes, Morocco
2 Department of Chemistry, Analytical Chemistry and Environment Team, Moulay Ismail University, Faculty of Science, BP. 11201, Zitoune Meknes, Morocco
3 Industrial engineering Department, Ibn Zohr University, National School of Applied Sciences, BP. 1136, Agadir, Morocco
Original language: French
Copyright © 2014 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
This work presents the development of an artificial neural network model based on the Multi-layer Perceptron (MLP) and Radial Basis Function (RBF) for predicting the moisture in the zone of Chefchaouen (Morocco). Our objective is to treat a chronological series of measured data for network response evaluation. For this reason, we used a basic learning neural model for the years 2008 and 2013, the latter consists of a number of meteorological parameters such as humidity, temperature of the air, dew point temperature, air pressure, visibility, cloud cover, wind speed and rain. To determine the network architecture to be used, we varied the number of hidden number of neurons in hidden layers, transfer functions and pairs of transfer functions and learning algorithms. Models performances have been evaluated and developed through the study of the Mean Squared Error (MSE) and correlation coefficient (R). We demonstrated in this study for the prediction of moisture, the best performing model is the one used as transfer functions, the Tansig function in the hidden layer and the Purelin function in the output layer, while using a learning algorithm LM, PMC type of configuration.
Author Keywords: Moisture, Artificial neural networks, PMC, RBF, Prediction, Learning algorithms.
Abstract: (french)
Ce travail présente le développement des modèles statistiques neuronaux de type Perceptron Multicouches (PMC) et réseau à Fonction de Base Radiale (RBF) pour la prédiction du taux d'humidité de la zone de Chefchaouen (Maroc). Notre objectif est de traiter une série chronologique des données mesurées entre les années 2008 et 2013 pour évaluer la réponse du réseau. Cette série est constituée d'un certain nombre de paramètres météorologiques telles que le taux d'humidité, la température de l'air, la température de rosée, la pression atmosphérique, la visibilité, la nébulosité, la vitesse du vent et la précipitation. Pour déterminer l'architecture du réseau à utiliser, nous avons varié le nombre des couches cachées, le nombre de neurones dans une couche cachée, les fonctions de transfert et les couples de fonctions de transfert ainsi que les algorithmes d'apprentissage. Les performances des modèles ainsi développés ont été évaluées grâce à l'étude de l'erreur quadratique moyenne (MSE) et le coefficient de corrélation (R). Nous avons montré par cette étude que pour la prédiction du taux d'humidité, le modèle le plus performant est celui qui utilise comme fonctions de transfert, la fonction Tansig dans la couche cachée et la fonction Purelin dans la couche de sortie, tout en utilisant un algorithme d'apprentissage LM, de type PMC de configuration.
Author Keywords: Taux d'humidité, Réseau de neurones artificiels, MLP, RBF, Prédiction, Algorithmes d'apprentissage.
How to Cite this Article
Hicham EL BADAOUI, Abdelaziz Abdallaoui, and Samira CHABAA, “Multilayer Perceptron and Radial Basis Function network to predict the moisture,” International Journal of Innovation and Scientific Research, vol. 5, no. 1, pp. 55–67, July 2014.
