Due to the severity and great harm of coal and gas outbursts accidents, outbursts prediction becomes very necessary; the paper presents a hybrid prediction model of feature extraction and pattern classification for coal and gas outbursts. First, discrete wavelet transform (DWT) is utilized as a preprocessing technique to decompose subseries and extract the features with different frequencies and the optimal feature components are retained; second, in order to eliminate the redundancy between the features and uncorrelation between features and outbursts, we use the fast independent component analysis (FICA) to obtain each independent component, obtaining the global information in the feature; then, the obtained features are input into linear discriminant analysis (LDA), under the guidance of class labels, then the local information in features is obtained; finally, the projected features are input into the deep extreme learning machine (DELM) classifier based on the optimal parameters by quantum particle swarm optimization (QPSO) for training and classification. The experimental results on the dataset of coal and gas outbursts show that compared with other models in the current prediction of coal and gas outbursts, this method has significant effect on various indicators.

由于煤与瓦斯突出事故的严重性和危害性很大，突出预测变得十分必要；本文提出了一种煤和瓦斯突出特征提取和模式分类的混合预测模型。首先，利用离散小波变换（DWT）作为预处理技术，分解子序列，提取不同频率的特征，保留最优特征分量；其次，为了消除特征之间的冗余以及特征与爆发之间的不相关性，我们使用快速独立成分分析（FICA）来获取每个独立成分，获得特征中的全局信息；然后，将得到的特征输入到线性判别分析（LDA）中，在类标签的指导下，然后得到features中的局部信息；最后，通过量子粒子群优化（QPSO）将投影特征输入到基于最优参数的深度极限学习机（DELM）分类器中进行训练和分类。在煤与瓦斯突出数据集上的实验结果表明，与目前煤与瓦斯突出预测中的其他模型相比，该方法对各项指标的效果显着。