混沌蚁群算法在瓦斯爆炸性检测仪中的应用  

Application of chaotic ant colony algorithm in methane explosion detector

作　　者：彭继慎[1] 李杨[1] 王国辉[2] 陈正一[1] 

机构地区：[1]辽宁工程技术大学电气与控制工程学院,辽宁葫芦岛125105 [2]煤炭科学研究总院沈阳研究院,辽宁沈阳110016

出　　处：《传感器与微系统》2010年第11期135-137,共3页Transducer and Microsystem Technologies

基　　金：辽宁省教育厅创新团队计划资助项目(2009T047)

摘　　要：针对瓦斯爆炸性检测仪传感器在实际生产中因构造和环境因素所产生的输入、输出非线性问题,将混沌蚁群算法和BP神经网络算法应用到瓦斯爆炸性检测仪中,提高瓦斯传感器精度。通过混沌蚁群算法对BP神经网络进行优化,改善BP神经网络收敛速度慢、容易陷入局部最优的缺点。实验结果表明:基于混沌蚁群优化算法的BP神经网络能够提高瓦斯检测系统的精度。In the actual production,in the view of structural and environmental factors which bnng the problem of nonlinear input and output of the methane explosion detector,chaotic ant colony algorithm and BP neural network can be used in the methane explosion detector to improve the gas sensor precision. Shortcomings of slow rate of convergence and easy to fall into local optimum in the BP neural network can be improved by the optimization of chaotic ant colony algorithm. The experiment show that BP Neural Network based on chaotic ant colony algorithm optimization can improve the precision of gas detection system.

关 键 词：瓦斯爆炸性检测仪 混沌蚁群算法 BP神经网络 

分 类 号：TD713[矿业工程—矿井通风与安全]