改进最大Lyapunov指数的多变量瓦斯浓度预测研究

STUDY ON IMPROVED MULTIVARIATE GAS CONCENTRATION PREDICTION BASED ON LARGEST LYAPUNOV EXPONENT

煤矿井下瓦斯浓度受到多个环境参数的影响。首先对煤矿井下同一时间段的瓦斯浓度、风速、压力、温度、CO2、O2的时间序列数据进行统计学相关性分析,选择出对瓦斯浓度影响大的几种因素,并作为基于最大Lyapunov指数改进预测模型的输入参数来预测未来一段时间的瓦斯浓度。改进算法为:在用C-C方法重构多变量时间序列的相空间和Wolf方法计算最大Lyapunov指数的基础上,同时考虑夹角余弦和欧氏距离求取预测中心点的相近点。结果表明,改进预测算法提高了预测精度,平均绝对误差和标准差分别为2.11%和2.15%。

The gas concentration of coal mine is affected by lots of environmental parameters. First, we made the statistics correlation analysis on gas concentration, wind velocity, pressure, temperature, and the time series data of CO2 and 02 during the same period in coal mine, and selected some factors having big impacts on gas concentration as the input parameters of the improved prediction model, which was based on the largest Lyapunov exponent, to predict the gas concentration in coming period. The improved algorithm is that based on using C-C method to reconstruct the phase space of multivariate time series and using Wolf method to calculate the largest Lyapunov exponent, the cosine of angle and the Euclidean distance are taken into account at the same time to obtain the close points to the prediction centre point. Results showed that the improved prediction algorithm improved the prediction accuracy, its＇ average absolute error and standard deviation were 2.11% and 2.15% respectively.