一种采用CCPSO-SVM的煤与瓦斯突出预测方法

摘要

In order to forecast effectively coal-and-gas outburst in coal-mine, a new method for coal-and-gas outburst forecast based on CCPSO (complete chaotic particle swarm optimization) and SVM (support vector machine) was presented. With multi-fractal dimension spectrum of gas emission amount dynamic time series in the front of work-face in coal-mine being feature index, the forecasting model was constructed by using SVM. The parameters vector of the proposed model was selected and optimized by CCPSO and the criteria of CERM (classification error rate minimization) and TSSM (test sample set minimization). The experimental results show that the proposed method is effective and provides a new approach for forecasting coal-and-gas outburst in coal-mine.%为了有效地对矿井煤与瓦斯突出进行预测,提出了一种基于完全混沌粒子群优化(CCPSO)与支持向量机(SVM)的矿井煤与瓦斯突出预测方法.该方法将矿井工作面前方煤体瓦斯涌出量动态变化时间序列的多重分维谱作为特征指标,应用支持向量(SVM)构建预测模型,模型的参数向量由改进的完全混沌粒子群优化算法和测试集样本集分类错误率最小准则选择和优化.实验结果证明,该方法是有效的,它为煤与瓦斯突出预测提供了一种新途径.