合理遗忘选择训练样本的煤矿瓦斯涌出量预测

为了提高煤矿瓦斯涌出量的预测精度，针对煤矿瓦斯涌出量的训练样本选择问题，提出一种基于合理遗忘训练样本的煤矿瓦斯涌出量预测模型。首先通过引入遗忘因子既考虑了历史数据的影响，又突出了新数据的作用，然后最小二乘支持向量机建立煤矿瓦斯涌出量预测模型，最后进行了仿真分析。结果表明，该模型提高了煤矿瓦斯涌出量的建模效率，获得了更加理想的煤矿瓦斯涌出量预测结果。

Coal gas emission prediction selective forgetting training samples

In order to improve the prediction accuracy of coal gas emission, a novel prediction model of coal gas emission is proposed based on selective forgotten training samples to solve problem of training sample selection. Firstly, the forgetting factor is introduced to weaken the old training samples and highlight the role of new data simultaneously, and training samples are updated reasonably, and then the least squares support vector machine is used to establish the prediction model of gas emission, and finally, the simulation analysis is carried out to test the performance of model. The results show that the proposed model improves the modeling efficiency of coal gas emission and can obtain good coal gas emission prediction results.