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Received:May 10, 2011 Revised:June 06, 2011
Received:May 10, 2011 Revised:June 06, 2011
中文摘要: 矿井瓦斯含量的预测模型是一个多变量、非线性的函数关系,预测模型建立的准确与否决定于各个影响因素之间的相互作用、相互耦合的特性。将神经网络与粒子群算法有机地结合起来,以神经网络理论为基础,利用粒子群算法优化隐含层神经元个数和网络中的连接权值,建立瓦斯含量预测模型,解决了Bp神经网络收敛速度慢、易陷入局部优化的缺陷。并在历史数据的基础上,建立遗传神经网络训练和检验样本集,利用MATLAB进行仿真,结果表明粒子群神经网络模型可靠性强,预测精度高。
中文关键词: 粒子群算法 Bp 神经网络 Pso-Bp 耦合神经网络 预测 瓦斯突出
Abstract:Mine gas content prediction model is a multivariable nonlinear function relation, the accurate prediction model is established in various influence factors depends on the interaction between the mutual coupling. The neural network and the particle swarm algorithm organically, based on neural network theory, using particle swarm optimization algorithm and the number of hidden neurons in the network connection weights, gas content prediction model is established. Solved the bp neural network, slow convergence speed, easy in local optimum. And according to the historical data, establishing genetic neural network training and testing samples, and use of matlab simulation, the results show that particle swarm neural network model reliability, high precision.
keywords: particle swarm algorithm Bp neural network Pso-Bp coupled neural network forecast fuel gas outburst
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付华,顾东,李俊平.Pso-Bp 耦合算法在矿井瓦斯突出预测中的应用.计算机系统应用,2012,21(1):136-139
FU Hua,GU Dong,LI Jun-Ping.Application of Pso-Bp Coupled Algorithm to Mine Gas Outburst Predictive.COMPUTER SYSTEMS APPLICATIONS,2012,21(1):136-139
付华,顾东,李俊平.Pso-Bp 耦合算法在矿井瓦斯突出预测中的应用.计算机系统应用,2012,21(1):136-139
FU Hua,GU Dong,LI Jun-Ping.Application of Pso-Bp Coupled Algorithm to Mine Gas Outburst Predictive.COMPUTER SYSTEMS APPLICATIONS,2012,21(1):136-139
