CAJ | 학술논문

为建立精确度高且具有自学习能力的开采沉陷预计参数选取模型，采用主成分分析方法，对文献中的数据进行预处理，选择累计方差达到96．79％的6个主成分因子和地表下沉系数为输入和输出变量，以径向基（RB F ）为核函数，建立了基于支持向量机开采沉陷预计参数选取模型。结果表明，支持向量机模型在训练样本较少的情况下，具有较高的预测精度和较强的泛化能力，平均相对误差和均方根误差值的对比证明了支持向量机模型的预测准确性和预测稳定性更好。
위건립정학도고차구유자학습능력적개채침함예계삼수선취모형，채용주성분분석방법，대문헌중적수거진행예처리，선택루계방차체도96．79％적6개주성분인자화지표하침계수위수입화수출변량，이경향기（RB F ）위핵함수，건립료기우지지향량궤개채침함예계삼수선취모형。결과표명，지지향량궤모형재훈련양본교소적정황하，구유교고적예측정도화교강적범화능력，평균상대오차화균방근오차치적대비증명료지지향량궤모형적예측준학성화예측은정성경호。
In order to establish selection model of mining subsidence predicting parameters ,which has self learning ability and with high accuracy .In this paper ,using principal component analysis preprocessing the data in the literature ,we have established the prediction parameters of mining subsidence selection model using support vector machine ,based on radial basis function (RBF) ,by selecting main components factor with cumulative variance reaches 96 .79% of 6 and surface subsidence factor as the input and output variables .Results show under the circumstances of less training samples Support vector machine (SVM ) model ,has high precision and strong generalization ability ,the prediction accuracy and prediction stability is better .which was proved contrasting average relative error and root mean square error .