煤田地质与勘探
煤田地質與勘探
매전지질여감탐
COAL GEOLOGY & EXPLORATION
2012年
6期
44-47
,共4页
支持向量机%粒子群优化%煤层底板%突水量等级%预测
支持嚮量機%粒子群優化%煤層底闆%突水量等級%預測
지지향량궤%입자군우화%매층저판%돌수량등급%예측
support vector machine%particle swarm optimization%coal floor%inrushed water volume grade%forecast
为更好地解决支持向量机(SVM)核参数和惩罚因子的取值对煤层底板突水量等级预测精度的影响问题,提出利用全局搜索能力较强的粒子群优化(PSO)算法优化支持向量机参数.选取含水层水压、隔水层厚度、岩溶发育程度、断层规模等作为影响煤层底板突水量等级的因素,利用华北聚煤区煤层底板突水的实测数据进行训练,建立了煤层底板突水量等级预测的粒子群-支持向量机(PSO-SVM)模型,并将其应用于其他样本的预测.应用表明:模型能够较好地解决煤层底板突水量等级预测中存在的小样本、非线性等问题,预测结果与实际情况吻合程度高,具有较强的实用性和有效性.
為更好地解決支持嚮量機(SVM)覈參數和懲罰因子的取值對煤層底闆突水量等級預測精度的影響問題,提齣利用全跼搜索能力較彊的粒子群優化(PSO)算法優化支持嚮量機參數.選取含水層水壓、隔水層厚度、巖溶髮育程度、斷層規模等作為影響煤層底闆突水量等級的因素,利用華北聚煤區煤層底闆突水的實測數據進行訓練,建立瞭煤層底闆突水量等級預測的粒子群-支持嚮量機(PSO-SVM)模型,併將其應用于其他樣本的預測.應用錶明:模型能夠較好地解決煤層底闆突水量等級預測中存在的小樣本、非線性等問題,預測結果與實際情況吻閤程度高,具有較彊的實用性和有效性.
위경호지해결지지향량궤(SVM)핵삼수화징벌인자적취치대매층저판돌수량등급예측정도적영향문제,제출이용전국수색능력교강적입자군우화(PSO)산법우화지지향량궤삼수.선취함수층수압、격수층후도、암용발육정도、단층규모등작위영향매층저판돌수량등급적인소,이용화북취매구매층저판돌수적실측수거진행훈련,건립료매층저판돌수량등급예측적입자군-지지향량궤(PSO-SVM)모형,병장기응용우기타양본적예측.응용표명:모형능구교호지해결매층저판돌수량등급예측중존재적소양본、비선성등문제,예측결과여실제정황문합정도고,구유교강적실용성화유효성.
To solve the problem of penalty factor and kernel parameter of support vector machine (SVM) which will affect the forecast accuracy, the method was put forward to find the better parameter value by using particle swarm optimization (PSO) which can automatically search the parameters for SVM. Four indexes, including water pressure, the thickness of aquifuge, karst development degree, the fault scale, were selected as the factors influ-encing water inrush from coal floor, the actual cases of water inrush from coal floor in Northern China coalfield were taken as training samples, the PSO-SVM model for forecast of water inrush volume grade from coal floor was established and applied to test other cases. The application of the model indicated that the method can solve the small sample, nonlinear problem, and the results obtained is better in accordance with the practice. It is practical and effective in forecasting water inrush volume grade from coal floor.
