摘要
我国煤矿的重大灾害事故中70%以上是瓦斯事故,煤矿瓦斯是影响煤矿安全生产的重要因素;针对瓦斯煤尘爆炸和煤与瓦斯突出给煤炭矿山带来的危害极大的问题,引入了基于改进PSO算法的RBF神经网络的混合优化算法(MPSO-RBF算法),即将PSO算法的全局搜索能力和RBF神经网络局部优化相结合,并建立了瓦斯预测模型;仿真与实际数据验证表明,优化算法所求的最优解具有良好的收敛能力,瓦斯涌出量的预测结果与实际值的误差在+1.44%至-0.63%之间,改进的粒子群算法优化的RBF神经网络对瓦斯涌出量预测能达到良好的效果。
Our country coal mine disaster in 70% above are to gas accident, coal mine gas is the important factor affecting coal mine safety production. In view of the harmful effects of gas and coal dust explosion and gas outburst in coal mine, a kind of hybrid optimization algorithm based on the improved PSO algorithm and RBF neural network neural network technology （MPSO--RBF） are introduced to solve the prediction of gas emission, it combined the PSO algorithm global search ability with local optimization of RBF neural network to establish the prediction models of gas emission. Simulation and actual data show that the optimal solution of optimization algorithm has good convergence ability, the error of gas emission prediction result and the actual value ranges from ＋1.44% to -0. 63%, improved particle swarm optimization algorithm for RBF neural network to the prediction of gas emission can achieve good results.
出处
《计算机测量与控制》
CSCD
北大核心
2012年第10期2625-2627,共3页
Computer Measurement &Control
基金
国家自然科学基金(70971059)
辽宁省科技攻关项目(2011229011)
