高斯过程混合模型在含噪输入预测策略下的煤矿瓦斯浓度柔性预测

Soft Prediction of Coal-mine Gas Concentration Through the Mixture of Gaussian Processes Under the Noisy Input Prediction Strategy

高斯过程回归是机器学习中解决非线性回归的一种典型回归方法。然而,单一的高斯过程难以拟合非平稳、多模态的时序数据。另外,在实际应用中需要预测的输入数据会受到噪声的干扰。为了克服这些问题,本文提出了含噪输入预测策略下的高斯过程混合回归预测方法(niMGP),并针对煤矿瓦斯浓度数据进行了参数学习和柔性预测。与其他传统回归方法相比,这种柔性预测方法是在测试输入数据具有噪声干扰的情况下进行预测,使其结果更为鲁棒和准确。本文首先通过模拟实验验证了在具有固定信噪比的测试输入数据上,高斯过程混合模型在含噪输入预测策略下的回归结果在稳定性上优于其传统预测策略下的回归结果。本文进一步选取松藻煤矿中打通一矿的333944号传感器获取的实际瓦斯浓度数据片段,对其进行了适当的数据增强之后,通过实际数据的实验进一步表明,高斯过程混合模型采用含噪输入预测策略在数据回归分析的预测上相比传统预测策略具有更好的稳定性。实际中还可以通过调节测试输入数据中噪声分布的方差来调节预测的灵敏度,达到分级预警的效果。

Gaussian process regression is a typical nonlinear regression method in machine learning.However,it is rather difficult for a single Gaussian process to fit non-stationary and multi-modal series data.In addition,test input data may be interfered by noise.In order to overcome these problems,this paper proposes a regression method of mixture of Gaussian processes with the noisy input prediction strategy(niMGP),which is further implemented to perform a kind of soft prediction on coal mine gas concentration data.In comparison with traditional regression methods,this method adopts a noise into the test input data so that the prediction results become more robust and accurate.It is firstly demonstrated by simulation experiments that on a synthetic noisy test data with a fixed signal-to-noise ratio,the regression results of mixture of Gaussian processes with the noisy input prediction strategy is better than those with traditional prediction strategy in terms of stability.It is further demonstrated on the actual gas concentration dataset from the fragments recorded by No.333944 sensor in Songzao Coal Mine with appropriate data enhancements that the noisy input prediction strategy is more stable than traditional prediction strategy.In practice,the prediction sensitivity can also be adjusted by adjusting the distribution variance of noise added to the test input data to achieve the effect of hierarchical warning.