کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6883451 | 1444173 | 2018 | 21 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Crop yield forecasting using fuzzy logic and regression model
ترجمه فارسی عنوان
پیش بینی عملکرد محصول با استفاده از منطق فازی و مدل رگرسیون
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کلمات کلیدی
میانگین مربع خطا، سری زمانی فازی میانگین خطای نرخ پیش بینی، پیش بینی، عملکرد گندم، داده های سری زمانی،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
شبکه های کامپیوتری و ارتباطات
چکیده انگلیسی
Time-Series data has been of great importance to the research field of prediction models. Over the past two decades, multitudinous fuzzy time series blueprints have been put forth for agricultural yield production. However, most of these predictions were based on 7th interval partitioning. A surprising insight was that nobody gave a sound reason to justify the choice of that particular interval. So, this paper focuses on predicting data values on a large spectrum of fuzzy logic computations based on second and third-degree relationships. This paper showcases work on 4 different types of the fuzzy interval, where each interval is tested with 4 degrees of regression equations. Each of these 16 cases is performed for the fuzzy logic relationship (FLR) 2 and 3 separately. Apart from this, the robustness of algorithm is a testament to an incredible solution for the time series model. In addition to this, A Regression analysis model has been enforced to accomplish the efficient defuzzification operation. To elucidate the process of forecasting, the historical data of wheat yield of University of Agriculture and Technology has been used.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computers & Electrical Engineering - Volume 67, April 2018, Pages 383-403
Journal: Computers & Electrical Engineering - Volume 67, April 2018, Pages 383-403
نویسندگان
Bindu Garg, Shubham Aggarwal, Jatin Sokhal,