| 粒子群优化的RBF瓦斯涌出量预测 |
| |
| 引用本文： | 彭程,潘玉民. 粒子群优化的RBF瓦斯涌出量预测[J]. 中国安全生产科学技术, 2011, 7(11): 77-81 |
| |
| 作者姓名： | 彭程 潘玉民 |
| |
| 作者单位： | 华北科技学院电子信息工程系,东燕郊,101601 |
| |
| 基金项目： | 河北省教育厅计划项目(Z2006439)资助 |
| |
| 摘 要： | 瓦斯涌出量是煤矿瓦斯灾害的主要来源,它直接影响煤矿安全生产和经济技术指标。瓦斯涌出量的传统预测方法是将其影响因素线性化后提出的,具有一定的局限性。本文基于群体智能理论,提出了一种基于粒子群算法优化的RBF神经网络瓦斯涌出量预测模型。研究表明RBF神经网络预测精度与网络权值和RBF参数初始值有很大关系,因此本文采用粒子群算法优化RBF网络权值和其他参数,形成PSO-RBF预测模型。该模型通过计算种群粒子的适应度,确定全局最优值,寻找网络参数的最优值。实验结果表明PSO-RBF优于传统的RBF预测模型,训练速度和预测精度显著提高。
|
| 关 键 词： | 瓦斯涌出量 RBF神经网络 粒子群优化 预测精度 |
Particle swarm optimization RBF for gas emission prediction |
| |
| PENG Cheng,PAN Yu-min. Particle swarm optimization RBF for gas emission prediction[J]. Journal of Safety Science and Technology, 2011, 7(11): 77-81 |
| |
| Authors: | PENG Cheng PAN Yu-min |
| |
| Affiliation: | PENG Cheng,PAN Yu-min(Department of Electronic Information Engineering,North China Institute of Science and Technology,East Yanjiao 101601,China) |
| |
| Abstract: | Gas emission was the major source of coal mine disaster,which affects the coal mine safety production and economic technical indicators.Traditional prediction methods had been based on the linear relationship between gas emission and other affect factors,and there were some limitations.Based on theories of swarm intelligence,a model of RBF network for gas emission prediction based on particle swarm optimization was proposed.The prediction accuracy of RBF neural network was concerned with the network weight ... |
| |
| Keywords: | gas emission RBF neural network particle swarm optimization prediction accuracy |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
|
