科技视界
科技視界
과기시계
Science&Technology Vision
2014年
7期
287-288
,共2页
瓦斯涌出量预测%遗传算法%最小二乘支持向量机%神经网络
瓦斯湧齣量預測%遺傳算法%最小二乘支持嚮量機%神經網絡
와사용출량예측%유전산법%최소이승지지향량궤%신경망락
Prediction of gas emission%Genetic algorithm%Least squares support vector machine%Neural network
瓦斯涌出量的准确预测对于通风系统的设计、瓦斯防治、安全管理有着重要意义，可以有效减轻采煤工作面的危险程度，同时提高煤矿业在燃料市场中的竞争能力。本文简述了瓦斯涌出量预测的价值和曾出现的各种方法，提出了基于遗传算法(Genetic Algorithms, GA)和最小二乘支持向量机(least square support vector machine, LS-SVM)的短期瓦斯涌出量预测方法，以某煤矿回采工作面瓦斯涌出量与影响因素为例，建立了GA-LSSVM预测模型，根据上述煤矿的数据进行实例验证，结果表明文中的方法显著优于神经网络的预测结果。
瓦斯湧齣量的準確預測對于通風繫統的設計、瓦斯防治、安全管理有著重要意義，可以有效減輕採煤工作麵的危險程度，同時提高煤礦業在燃料市場中的競爭能力。本文簡述瞭瓦斯湧齣量預測的價值和曾齣現的各種方法，提齣瞭基于遺傳算法(Genetic Algorithms, GA)和最小二乘支持嚮量機(least square support vector machine, LS-SVM)的短期瓦斯湧齣量預測方法，以某煤礦迴採工作麵瓦斯湧齣量與影響因素為例，建立瞭GA-LSSVM預測模型，根據上述煤礦的數據進行實例驗證，結果錶明文中的方法顯著優于神經網絡的預測結果。
와사용출량적준학예측대우통풍계통적설계、와사방치、안전관리유착중요의의，가이유효감경채매공작면적위험정도，동시제고매광업재연료시장중적경쟁능력。본문간술료와사용출량예측적개치화증출현적각충방법，제출료기우유전산법(Genetic Algorithms, GA)화최소이승지지향량궤(least square support vector machine, LS-SVM)적단기와사용출량예측방법，이모매광회채공작면와사용출량여영향인소위례，건립료GA-LSSVM예측모형，근거상술매광적수거진행실례험증，결과표명문중적방법현저우우신경망락적예측결과。
Accurate prediction of gas emission has important significance to the design of the ventilation system, gas control and safety management, and it can reduce the danger degree of coal mining work face effectively, At the same time ,it also improves the market competition ability of the coal in the fuel. This paper expounds the value of gas emission prediction and a variety of methods having appeared, and proposed a short-term gas emission prediction method based on Genetic algorithm (GA) and least squares support vector machine (LS-SVM).Taken gas emission quantity and influenced factors in a coal mine working face for an example, the GA-LS-SVM forecasting model is established. According to the data of coal mine, the results show that the method of this paper was superior to that of the neural network prediction results.
