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

Abstract

The Earth’s natural pulse electromagnetic field data consists typically of an underlying variation tendency of intensity and irregularities. The change tendency may be related to the occurrence of earthquake disasters. Forecasting of the underlying intensity trend plays an important role in the analysis of data and disaster monitoring. Combining 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 signal. The 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 data. In 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 respectively. According 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 model. Hence, 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.