Shahid Beheshti University Journal of Industrial Management Perspective 2251-9874 4 1 2014 05 22 Finding the ANN Suitable Structure using Taguchi Experimental Design Method Finding the ANN Suitable Structure using Taguchi Experimental Design Method 121 142 87295 FA Ali Morovati Sharifabadi Assistant Professor, Yazd University. Rasool Khancheh Mehr MA Student, Yazd University. Journal Article 2019 09 27 The delay in the supply of gasoil has wide Consequences of political, social and economic. Therefore, the gasoil demand forecasting is very important. The use of artificial neural networks in forecasting has many applications. Appropriate design of ANN parameters enhances performance and accuracy of the neuralnetwork model<span lang="FA" dir="RTL">. </span>Trial and error method is used to setting the ANN parameters in the most of studies. Trial and error method is not a reliable solution to achieve the best ANN structure. In the present study, the optimum structure of ANN to forecasting the demand of gasoil in the province of Hormozgan determined by Taguchi experimental design method. Analysis of variance (ANOVA) of the ANN parameters indicates that contribution of the number of neurons in first hidden layer to the changes in the network mean square error (MSE) is about 41% and contribution of the learning algorithm is about 27%.   Also, the results of this study show that artificial neural networks are designed using Taguchi experimental design method have better performance than other Networks. The delay in the supply of gasoil has wide Consequences of political, social and economic. Therefore, the gasoil demand forecasting is very important. The use of artificial neural networks in forecasting has many applications. Appropriate design of ANN parameters enhances performance and accuracy of the neuralnetwork model<span lang="FA" dir="RTL">. </span>Trial and error method is used to setting the ANN parameters in the most of studies. Trial and error method is not a reliable solution to achieve the best ANN structure. In the present study, the optimum structure of ANN to forecasting the demand of gasoil in the province of Hormozgan determined by Taguchi experimental design method. Analysis of variance (ANOVA) of the ANN parameters indicates that contribution of the number of neurons in first hidden layer to the changes in the network mean square error (MSE) is about 41% and contribution of the learning algorithm is about 27%.   Also, the results of this study show that artificial neural networks are designed using Taguchi experimental design method have better performance than other Networks. https://jimp.sbu.ac.ir/article_87295_48f54a481b231dbbbe270cf119e527ab.pdf