دانلود مقاله ISI انگلیسی شماره 46650
عنوان فارسی مقاله

مقایسه شبکه عصبی مصنوعی و مدل رگرسیون خطی چندگانه برای پیش بینی مقاومت اتصال بهینه از جنگل تحت درمان با گرما

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
46650 2014 8 صفحه PDF سفارش دهید محاسبه نشده
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
Comparison of artificial neural network and multiple linear regression models to predict optimum bonding strength of heat treated woods
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : International Journal of Adhesion and Adhesives, Volume 55, December 2014, Pages 29–36

کلمات کلیدی
رگرسیون خطی چندگانه - شبکه عصبی - بهینه سازی - مدل پیش بینی
پیش نمایش مقاله
پیش نمایش مقاله مقایسه شبکه عصبی مصنوعی و مدل رگرسیون خطی چندگانه برای پیش بینی مقاومت اتصال بهینه از جنگل تحت درمان با گرما

چکیده انگلیسی

In this study, an artificial neural network (ANN) model was developed for predicting an optimum bonding strength of heat treated woods. The MATLAB Neural Network Toolbox was used for the training and optimization of the ANN model. The ANN model having the best prediction performance was detected by trying various networks. Then, the ANN results were compared with the results of multiple linear regression (MLR) model. It was shown that the ANN model produced more successful results compared to MLR model in all cases. The mean absolute percentage errors (MAPE) were found as 1.49% and 3.06% in the prediction of bonding strength values for training and testing data sets, respectively. Determination coefficient (R2) values for training and testing data sets in the prediction of bonding strength by ANN were 0.997 and 0.986, respectively. The results also indicated that the designed model is a useful, reliable and quite effective tool for optimizing the effects of heat treatment conditions on bonding strength of wood. Thanks to using optimum bonding strength values obtained by the model, the increase of the bonding quality of wood products can be provided and the costs for experimental material and energy can be reduced.

خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.