Coal and gas outbursts prediction based on combination of hybrid feature extraction DWT+FICA–LDA and optimized QPSO-DELM classifier.

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. [ABSTRACT FROM AUTHOR]