Comparison of Artificial Neural Network and Multiple Regression Analysis for Prediction of Fat Tail Weight of Sheep
محورهای موضوعی : Camelم.ع. نوروزیان 1 , م. وکیلی علویجه 2
1 - Department of Animal Science, College of Abouraihan, University of Tehran, Tehran, Iran
2 - Department of Mathematics, Faculty of Mathematical Science, Shahid Beheshti University, Tehran, Iran
کلید واژه: Artificial Neural Network, Sheep, multiple linear regression, fat tail,
چکیده مقاله :
A comparative study of artificial neural network (ANN) and multiple regression is made to predict the fat tail weight of Balouchi sheep from birth, weaning and finishing weights. A multilayer feed forward network with back propagation of error learning mechanism was used to predict the sheep body weight. The data (69 records) were randomly divided into two subsets. The first subset is the training set comprising of 75 percent data (52 records) to build the neural network model and test data set comprising of 25 percent (17 records), which is not used during the training and is used to evaluate performance of different models. The mean relative error was significantly (P<0.01) lower for ANN than the MLR model. The coefficient of determination (R2) values computed for the body measurements were generally higher (0.93) using ANN model than themultiple linear regression (MLR) model (0.81). The ANN model improved the mean squared error (MSE) of the MLR model by 59% and R2 by 15% that the ANN represents a valuable tool for predicting of lamb fat tail weight from birth, weaning and finishing weights.
در این مطالعه ارتباط بین وزنهای تولد، از شیرگیری و پایان پروار با وزن دنبه 69 رأس گوسفند بلوچی توسط روشهای شبکه عصبی مصنوعی و رگرسیون چندگانه بررسی شد. هر دو روش با دقت بالایی وزن دنبه را پیشبینی کردند. هر چند که میانگین خطا به صورت معنیداری در روش شبکه عصبی مصنوعی کمتر از رگرسیون چندگانه بود. ضریب تعیین برآورد شده در روش شبکه عصبی مصنوعی (93/0) بالاتر از رگرسیون چندگانه (81/0) به دست آمد. استفاده از شبکه عصبی مصنوعی میانگین خطای استاندارد را 59 و ضریب تعیین را 15 درصد بهبود داد. به نظر میرسد که بتوان با استفاده از شبکه عصبی مصنوعی وزن دنبه را از صفات وزن بدن پیشبینی کرد.
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