|本期目录/Table of Contents|

[1]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.
 PENG Cheng,PAN Yu-min.Particle swarm optimization RBF for gas emission prediction[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2011,7(11):77.
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粒子群优化的RBF瓦斯涌出量预测
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《中国安全生产科学技术》[ISSN:1673-193X/CN:11-5335/TB]

卷:
7
期数:
2011年11期
页码:
77
栏目:
学术论著
出版日期:
2011-11-30

文章信息/Info

Title:
Particle swarm optimization RBF for gas emission prediction
作者:
彭程; 潘玉民;
华北科技学院电子信息工程系;
Author(s):
PENG ChengPAN Yu-min
Department of Electronic Information Engineering,North China Institute of Science and Technology,East Yanjiao 101601,China
关键词:
瓦斯涌出量 RBF神经网络 粒子群优化 预测精度
Keywords:
gas emission RBF neural network particle swarm optimization prediction accuracy
分类号:
TD712.5;TP183
DOI:
-
文献标志码:
-
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。
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 and initial RBF parameters.So particle swarm optimization(PSO)was investigated for the network weight and initial RBF parameters,then a model of PSO-RBF was formed.The model could determine the global optimal value and find the optimal value of network parameters by calculate the swarm fitness value.The results showed that the model of PSO-RBF was better than traditional RBF network prediction model,the training speed and prediction accuracy increased significantly.

参考文献/References:

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备注/Memo

备注/Memo:
河北省教育厅计划项目(Z2006439)资助
更新日期/Last Update: 2012-01-09