Comparison of Artificial Neural Network and Regression Models for Prediction of Body Weight in Raini Cashmere Goat
محورهای موضوعی : Camelم. خورشیدی-جلالی 1 , م.ر. محمدآبادی 2 , ع. اسمعیلی‌زاده 3 , ا. برازنده 4 , ُ.ا. بابنکو 5
1 - Department of Animal Science, Faculty of Agriculture, Shahid Bahonar University of Kerman, Kerman, Iran
2 - Department of Animal Science, Faculty of Agriculture, Shahid Bahonar University of Kerman, Kerman, Iran
3 - Department of Animal Science, Faculty of Agriculture, Shahid Bahonar University of Kerman, Kerman, Iran
4 - Department of Animal Science, Faculty of Agriculture, University of Jiroft, Jiroft, Iran
5 - Department of Animal Science, Bila Tserkva National Agrarian University, Bila Tserkva, Ukraine
کلید واژه: Artificial Neural Networks, body measurements, linear models, Raini goat,
چکیده مقاله :
The artificial neural networks (ANN) are the learning algorithms and mathematical models, which mimic the information processing ability of human brain and can be used to non linear and complex data. The aim of this study was to compare artificial neural network and regression models for prediction of body weight in Raini Cashmere goat. The data of 1389 goats for body weight, height at withers (HAW), body length (BL) and chest girth (CG) were used. Different regression models with all fixed factors were calculated for the most possible states and with different degrees and two artificial neural networks with different hidden layers, learning functions and transform functions were used. Finally, Multilayer perceptron model with one hidden layer along with neurons was selected and used. Correlation between body weight and its measurements showed that it is possible to use body measurements for prediction of body weight though prediction of body weight can be improved when more measurements are used. Based on R2 andmean square error (MSE) parameters, the best fitted regression equation for prediction of body weight using body measurements was selected. While all three measurements had a significant effect in the model (P<0.0001), height at wither had the highest correlation coefficient (0.65), hence may have the greatest effect on prediction. Comparing two models indicated that both models can predict body weight well and near to actual body weight, but the capability of artificial neural network model is higher (R2=0.86 for ANN and 0.76 for multiple regression analysis (MRA)) and closer to actual body weight. However, if more related measurements are recorded, ANN can give the desirable results. Therefore, it is possible to apply artificial neural networks, instead of customary procedures for prediction of actual body weight using body measurements.
شبکههای عصبی مصنوعی الگوریتمهای آموزشی و مدلهای ریاضی هستند که توانایی تقلید از مغز انسان در پردازش اطلاعات را دارند و میتوانند دادههای پیچیده و غیر خطی را مورد استفاده قرار دهند. هدف این پژوهش مقایسه شبکه عصبی مصنوعی و مدلهای رگرسیونی برای پیش‌بینی وزن بدن در بز کرکی راینی بود. دادههای 1389 بز برای وزن بدن، ارتفاع جدوگاه، طول بدن و قفسه سینه مورد استفاده قرار گرفت. مدلهای رگرسیونی مختلف با تمام فاکتورهای ثابت برای بیشتر حالتهای ممکن و با درجههای مختلف محاسبه شدند و دو شبکه عصبی مصنوعی با لایههای مخفی متفاوت، توابع آموزش و توابع انتقال گوناگون استفاده شدند. در نهایت، مدل پرسپترون چند لایه با یک لایه مخفی به همراه نرونها انتخاب و استفاده شد. همبستگی بین وزن بدن و اندازه‌گیریهایش نشان داد که میتوان از اندازههای بدن برای پیش‌بینی وزن بدن استفاده کرد و هرچه اندازههای بیشتری استفاده شوند پیش‌بینی دقیقتری انجام خواهد شد. براساس پارامترهای R2و MSE، بهترین معادله رگرسیون فیت شده برای پیش‌بینی وزن بدن با استفاده از اندازه‌گیریهای ابعاد بدن انتخاب شد. در حالیکه هر سه اندازه در مدل اثر معنی‌داری داشتند (0001/0P<)، ارتفاع جدوگاه بالاترین ضریب را داشت (65/0)، بنابراین میتواند بیشترین اثر را در پیش‌بینی داشته باشد. مقایسه دو مدل نشان داد که هر دو مدل میتوانند به خوبی وزن بدن را، نزدیک به وزن واقعی آن پیش‌بینی کنند، اما توانایی شبکه عصبی مصنوعی بالاتر است (R2 برای شبکه عصبی مصنوعی 86/0 و برای مدلهای رگرسیونی 76/0) و به ورن واقعی بدن نزدیکتر میباشد. با این وجود، اگر اندازههای مرتبط بیشتری رکورد‌برداری شوند میتوان نتایج مطلوبتری را با شبکه عصبی مصنوعی به دست آورد. بنابراین، از شبکه عصبی مصنوعی میتوان به جای روشهای سنتی مرسوم برای پیش‌بینی وزن واقعی بدن با استفاده از اندازههای بدن استفاده کرد.
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