基于PCA和案例推理的煤与瓦斯突出动态预测

为了实现对煤与瓦斯突出快速、准确和动态预测，提出了一种基于主成分分析(PCA)和案例推理(CBR)的煤与瓦斯突出预测方法。考虑煤与瓦斯突出多种影响因素，利用案例推理技术对煤与瓦斯突出危险性进行预测。同时采用一种基于PCA的案例描述特征权值确定方法，以提高案例检索效率以及煤与瓦斯突出预测准确率。利用实测数据对所提方法进行验证，实例验证结果表明，所提方法预测结果的准确性和稳定性更高，预测平均误差和最大误差分别仅为0.154%和0.77%，远小于模糊神经网络方法和专家给定权值的案例推理方法。

Dynamic prediction of coal and gas outburst based on PCA and case-based reasoning

In order to realize the accurate,speed and dynamic prediction of coal and gas outburst,a prediction meth?od based on principal component analysis(PCA)and case-based reasoning(CBR)was proposed. Considering multiple influencing factors of coal and gas outburst,the hazard prediction is done with CBR technology. At the same time,a method based on PCA is used in weights allocation for case retrieval and matching to improve the retrieval efficien?cy and prediction precision. The proposed method was validated using practical measured data. The simulation ex?ample shows that the proposed method provides more accurate and robust prediction results and the average predic?tion error and maximum prediction error are 0.154%and 0.77%,respectively. The prediction errors are much less than that obtained from the fuzzy neural network method and the CBR method using weights given by experts.