High-precision chaotic radial basis function neural network model:Data forecasting for the Earth electromagnetic signal before a strong earthquake

作     者：Guocheng Hao Juan Guo Wei Zhang Yunliang Chen David AYuen Guocheng Hao;Juan Guo;Wei Zhang;Yunliang Chen;David A.Yuen

作者机构：School of Mechanical Engineering and Electronic InformationChina University of GeosciencesWuhan 430074China School of Computer ScienceChina University of GeosciencesWuhan 430074China Department of MathematicsDuke UniversityDurhamNC 27708USA Department of Applied Physics and Applied MathematicsColumbia UniversityNew YorkNY 10027USA 

出 版 物：《Geoscience Frontiers》 (地学前缘（英文版）)

年 卷 期：2022年第13卷第1期

页      面：364-373页

核心收录：

学科分类：081801[工学-矿产普查与勘探] 081802[工学-地球探测与信息技术] 08[工学] 0818[工学-地质资源与地质工程] 

基　　金：sponsored by the National Natural Science Foundation of China(61333002) Open Research Foundation of the State Key Laboratory of Geodesy and Earth’s Dynamics(SKLGED2018-5-4-E) Foundation of the Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems(ACIA2017002) 111 projects under Grant(B17040) Open Research Project of the Hubei Key Laboratory of Intelligent Geo-Information Processing(KLIGIP-2017A02) supported by the Three Gorges Research Center for geo-hazard Ministry of Education cooperation agreements of Krasnoyarsk Science Center and Technology Bureau Russian Academy of Sciences 

主　　题：Earth’s natural pulse electromagnetic field Chaos theory Radial Basis Function neural network Forecasting model 

摘      要：The Earth’s natural pulse electromagnetic field data consists typically of an underlying variation tendency of intensity and *** change tendency may be related to the occurrence of earthquake *** of the underlying intensity trend plays an important role in the analysis of data and disaster *** chaos theory and the radial basis function neural network,this paper proposes a forecasting model of the chaotic radial basis function neural network to conduct underlying intensity trend forecasting by the Earth’s natural pulse electromagnetic field *** main strategy of this forecasting model is to obtain parameters as the basis for optimizing the radial basis function neural network and to forecast the reconstructed Earth’s natural pulse electromagnetic field *** verification experiments,we employ the 3 and 6 days’data of two channels as training samples to forecast the 14 and 21-day Earth’s natural pulse electromagnetic field data *** to the forecasting results and absolute error results,the chaotic radial basis function forecasting model can fit the fluctuation trend of the actual signal strength,effectively reduce the forecasting error compared with the traditional radial basis function ***,this network may be useful for studying the characteristics of the Earth’s natural pulse electromagnetic field signal before a strong earthquake and we hope it can contribute to the electromagnetic anomaly monitoring before the earthquake.