传感技术学报2016，Vol.29Issue(3)：373-377,5.DOI:10.3969/j.issn.1004-1699.2016.03.012
基于蚁群粒子群混合算法与LS-SVM瓦斯涌出量预测
Prediction of Gas Emission Based on Hybrid Algorithm of Ant Colony Particle Swarm Optimization and LS-SVM
摘要
Abstract
In order to prevent gas disasters effectively and predict mine gas emission,an improved LS-SVM model based on ant colony optimization mixing with particle swarm optimization was presented,which was used to predict nonlinear dynamic gas emission. The regularization C and the Gaussian kernel parameter σof LS-SVM were opti⁃mized by the prediction model of gas emission based on hybrid algorithm of ant colony particle swarm optimization. The model was validated by using the historical data from Zhaogezhuang coal mine in China. The results show that both the maximum and minimum relative errors predicted by the model are 1.05%and 0.28%respectively,and the average is 0.75%. Compared with others,the model has higher generalization ability and predicting precision.关键词
瓦斯涌出量/非线性动态预测/蚁群算法/粒子群算法/最小二乘支持向量机Key words
gas emission/nonlinear dynamic prediction/ant colony optimization/particle swarm optimization/least square-support vector machine分类
信息技术与安全科学引用本文复制引用
付华,于翔,卢万杰..基于蚁群粒子群混合算法与LS-SVM瓦斯涌出量预测[J].传感技术学报,2016,29(3):373-377,5.基金项目
国家自然科学基金项目（51274118）；辽宁省教育厅基金项目（L2012119）；辽宁省科技攻关项目 （）
