کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
294631 511489 2011 5 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Time-series gas prediction model using LS-SVR within a Bayesian framework
موضوعات مرتبط
مهندسی و علوم پایه علوم زمین و سیارات زمین شناسی اقتصادی
پیش نمایش صفحه اول مقاله
Time-series gas prediction model using LS-SVR within a Bayesian framework
چکیده انگلیسی

The traditional least squares support vector regression (LS-SVR) model, using cross validation to determine the regularization parameter and kernel parameter, is time-consuming. We propose a Bayesian evidence framework to infer the LS-SVR model parameters. Three levels Bayesian inferences are used to determine the model parameters, regularization hyper-parameters and tune the nuclear parameters by model comparison. On this basis, we established Bayesian LS-SVR time-series gas forecasting models and provide steps for the algorithm. The gas outburst data of a Hebi 10th mine working face is used to validate the model. The optimal embedding dimension and delay time of the time series were obtained by the smallest differential entropy method. Finally, within a MATLAB7.1 environment, we used actual coal gas data to compare the traditional LS-SVR and the Bayesian LS-SVR with LS-SVMlab1.5 Toolbox simulation. The results show that the Bayesian framework of an LS-SVR significantly improves the speed and accuracy of the forecast.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Mining Science and Technology (China) - Volume 21, Issue 1, January 2011, Pages 153–157
نویسندگان
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