基于相关向量机的瓦斯涌出量不确定性预测

Prediction of uncertainties of gas emission quantity based on RVM

为了分析瓦斯涌出量预测结果的不确定性,提出一种基于相关向量机的估计方法:依据稀疏贝叶斯学习模型,计算瓦斯涌出量样本空间的稀疏相关支持向量和相应的超参数,再计算预测结果的均值和方差,从而得出瓦斯涌出量预测结果的概率分布和置信区间。分析结果表明,3组检验样本的平均预测误差为1.74%,其实际值均在置信度为97%的置信区间内,与实际情况相符,这说明采用该方法可以得出瓦斯涌出量预测结果的概率分布,且具有预测精度高、所需支持向量少的优点。

In order to evaluate uncertainties of prediction results of gas emission, an estimation approach based on relevant vector machine was proposed. The sparse relevant support vector and its corresponding hyper parameters were calculated on sample space of gas emission by sparse Bayesian learning model. The mean and variance of prediction results were worked out, so probability distribution and confidence interval of prediction results of gas emission quantity can also be obtained. The analysis results show that the mean prediction error of three group testing samples is 1.74%, and real gas emission quantities are all in confidence interval of 97%. The prediction result is consistent with actual situation, it shows that the proposed approach can be used to get probability distribution of prediction result of gas emission, and has high prediction accuracy and requires less support vectors.