کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6539967 1421105 2017 7 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Prediction of carbon dioxide concentration in weaned piglet buildings by wavelet neural network models
ترجمه فارسی عنوان
پیش بینی غلظت دی اکسید کربن در ساختمان های خوک شخم با مدل شبکه عصبی موجک
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نرم افزارهای علوم کامپیوتر
چکیده انگلیسی
Because CO2 is a highly autoregressive variable, the best results are obtained with the AM. The AM yield an RMSE of 26.330 ppm and a Pearson's r of 0.995. The EM, with an RMSE of 154.361 ppm and a Pearson's r of 0.895, reveal the importance of indoor and outdoor temperatures and, consequently, of ventilation rate, for CO2 concentration inside the building. In addition, our results show the effects of animal activity on CO2 concentration, which are delayed by 40-50 min. Based on these results, the CO2 concentrations in the animal zone of weaner buildings can be accurately predicted by WNN models. Therefore, WNN modeling could be widely used to predict and understand the dynamics of environmental variables inside livestock buildings.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computers and Electronics in Agriculture - Volume 143, December 2017, Pages 201-207
نویسندگان
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