Application of Linear Regression and Artificial NeuralNetwork for Broiler Chicken Growth Performance Prediction
محورهای موضوعی : Camel
1 - Department of Animal Science, College of Abouraihan, University of Tehran, Tehran, Iran
کلید واژه: Artificial Neural Network, growth performance, broiler chicken, back propagation algorithm, Linear regression,
چکیده مقاله :
This study was conducted to investigate the prediction of growth performance using linear regression and artificial neural network (ANN) in broiler chicken. Artificial neural networks (ANNs) are powerful tools for modeling systems in a wide range of applications. The ANN model with a back propagation algorithm successfully learned the relationship between the inputs of metabolizable energy (kcal/kg) and crude protein (g/kg) and outputs of feed intake, weight gain and feed conversion ratio variables. High R2 and T values for the ANN model in comparison to linear regression revealed that the artificial neural network (ANN) is an efficient method for growth performance prediction in the starter period for broiler chickens. This study also focused on expanding the experiment with more levels of inputs to predict outputs the using best ANN model.
این مطالعه به منظور پیش‌بینی عملکرد رشد با استفاده از مدل خطی و شبکه عصبی مصنوعی در جوجه‌های گوشتی انجام شد. شبکه عصبی مصنوعی ابزار قدرتمندی برای سیستم مدلینگ در دامنه وسیعی از کاربردها است. مدل شبکه عصبی مصنوعی با الگوریتم پس انتشار به طور موفقیت آمیزی ارتباط بین ورودی‌ها (انرژی قابل سوخت و ساز (کیلوکالری/کیلوگرم) و پروتئین خام (گرم/کیلوگرم) و خروجی‌ها (مصرف خوراک، افزایش وزن و ضریب تبدیل خوراک) را آموزش داد. ارزش R2و T بالا برای مدل شبکه عصبی مصنوعی در مقایسه با مدل خطی نشان داد که شبکه عصبی مصنوعی یک روش مؤثر برای پیش‌بینی عملکرد رشد در دوره آغازین برای جوجه‌های گوشتی است. همچنین، گسترش آزمایش با سطوح بیشتری از ورودی‌ها برای پیش‌بینی عملکرد با استفاده از بهترین مدل شبکه عصبی مصنوعی انجام شد.
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