【摘要】 提出了一种基于混沌参数优化径向基函数(radial basis function,RBF)神经网络的预测模型.通过混沌理论获得了ENPEMF信号的有效嵌入维数和最优时延,然后利用所获得的参数优化RBF神经网络.采用训练好的参数优化RBF神经网络预测ENPEMF.数值仿真结果表明,改进的RBF算法可以较为准确地预测Rossler混沌时间序列且误差较小.将优化的RBF模型应用于芦山Ms7.0级地震前ENPEMF数据,可以有效预测震前14 d的ENPEMF数据强度趋势,且预测效果及精度优于传统RBF神经网络算法,期望为地质灾害及强震前的电磁监测分析提供支持.更多还原

【Abstract】 A chaotic parameter-optimized radial basis function( RBF) forecasting model was proposed. The chaos theory was used to obtain the embedded dimension and delay time of the ENPEMF,and the obtained parameters were used to optimize the RBF neural network. Finally,the trained optimized-RBF was utilized to forecast the strength trend of 14 d ENPEMF data.Numerical simulation results show that the improved RBF model could forecast the Rossler time series well with small error. Applying the improved RBF algorithm to the ENPEMF data before Ms7. 0 earthquake in Lushan,it can effectively forecast the ENPEMF intensity trend 14 d before earthquake. The forecasting effect and accuracy are significantly better than that of the traditional RBF algorithm,which is expected to provide support for electromagnetic monitoring and analysis before earthquakes and geological disasters.更多还原