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مقایسه عملکرد شبکه‌های عصبی پرسپترون چندلایه و توابع با پایه شعاعی در برآورد ستانده انرژی مرغ گوشتی | ||
| مهندسی بیوسیستم ایران | ||
| مقاله 13، دوره 47، شماره 2، شهریور 1395، صفحه 319-328 اصل مقاله (540.93 K) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/ijbse.2016.58781 | ||
| نویسندگان | ||
| سما عمید* ؛ ترحم مصری گندشمین؛ غلامحسین شاهقلی | ||
| دانشگاه محقق اردبیلی | ||
| چکیده | ||
| مدیریت انرژی یکی از اصلی‌ترین راه‌های بهینه‌سازی مصرف منابع انرژی است. پیش‌بینی عملکرد محصولات بر اساس ورودی‌های انرژی می‌تواند به کشاورزان و سیاست‌گذاران به منظور برآورد سطح تولید کمک کند. داده‌های مورد نیاز برای مطالعه به طور تصادفی از 70 مزرعه مرغ‌گوشتی در شمال‌غرب ایران جمع‌آوری گردید. انرژی‌های ورودی شامل نیروی انسانی، ماشین‌آلات، سوخت، خوراک و الکتریسیته و انرژی‌های خروجی تولید شده به عنوان متغیرهای خروجی در نظر گرفته شد. شبکه‌های عصبی پرسپترون چندلایه (MLP) و تابع با پایه شعاعی (RBF) به منظور پیش‌بینی انرژی‌های خروجی تولید مرغ‌گوشتی مورد استفاده قرار گرفت. با توجه به نتایج مقایسه به‌دست آمده از شاخص‌های ضریب تبیین (R2)، جذر میانگین مربعات خطا (RMSE) و میانگین قدرمطلق خطا (MAE) عملکرد مدل شبکه عصبی RBF بهتر از شبکه عصبی MLP برآورد گردید. در ارزیابی تأثیرپذیری خروجی از نهاده‌های ورودی، در هر دو مدل سوخت فسیلی بالاترین حساسیت را در بین نهاده‌های تولیدی از خود نشان داد. | ||
| کلیدواژه‌ها | ||
| پیش‌بینی؛ حساسیت؛ مدیریت انرژی؛ منابع انرژی | ||
| عنوان مقاله [English] | ||
| Comparison of MLP and RBF neural networks performance for estimation of broiler output energy | ||
| نویسندگان [English] | ||
| Sama Amid؛ Tarahom Mesri Gundoshmian؛ Gholamhossein Shahgoli | ||
| چکیده [English] | ||
| Energy management is one of the main ways of the efficient use of energy resources. The prediction of crop yields based on energy inputs can help farmers and policymakers to estimate the level of production. Required data for study were randomly collected from 70 broiler farms in North West of Iran. The input energies were included human labour, machinery, fuel, feed and electricity and the output produced energies were considered as output variables. The multi-layer perceptron (MLP) and the radial basis function (RBF) neural networks were applied for prediction of output energies of broiler production. According to the comparison results obtained from the indices of the coefficient of determination (R2), root mean square error (RMSE) and the mean absolute error (MAE) performance of the ANN-RBF model better than ANN-MLP model was estimated. In evaluating the effects of inputs on outputs of production, the production of fossil fuel showed the highest sensitivity among the production inputs in both models. | ||
| کلیدواژه‌ها [English] | ||
| Energy management, Energy resources, prediction, Sensitivity | ||
| مراجع | ||
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