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

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.