کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
294901 511500 2009 5 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
On-line least squares support vector machine algorithm in gas prediction
موضوعات مرتبط
مهندسی و علوم پایه علوم زمین و سیارات زمین شناسی اقتصادی
پیش نمایش صفحه اول مقاله
On-line least squares support vector machine algorithm in gas prediction
چکیده انگلیسی

Traditional coal mine safety prediction methods are off-line and do not have dynamic prediction functions. The Support Vector Machine (SVM) is a new machine learning algorithm that has excellent properties. The least squares support vector machine (LS-SVM) algorithm is an improved algorithm of SVM. But the common LS-SVM algorithm, used directly in safety predictions, has some problems. We have first studied gas prediction problems and the basic theory of LS-SVM. Given these problems, we have investigated the affect of the time factor about safety prediction and present an on-line prediction algorithm, based on LS-SVM. Finally, given our observed data, we used the on-line algorithm to predict gas emissions and used other related algorithm to compare its performance. The simulation results have verified the validity of the new algorithm.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Mining Science and Technology (China) - Volume 19, Issue 2, March 2009, Pages 194-198