Prediction Model of the Geomagnetic Variation Field by Chaotic RBF Neural Network
Abstract We propose a single station prediction model of geomagnetic variation based on chaos theory and RBF neural network. We analyze the chaotic characteristics of magnetic field data, and obtain the embedding dimension m and time delay τ.Based on this, we reconstruct the phase space. The sample set optimized by chaos theory is used as the training and test set of the neural network for simulation experiment. The results show that the RBF neural network model improved by chaos theory can accurately predict the change trend of geomagnetic field and has good applicability to geomagnetic field in China.
Key words :
chaos theory
phase space reconstruction
RBF neural network
geomagnetic variation
Cite this article:
YU Wenqiang,LI Houpu,QIN Qingliang et al. Prediction Model of the Geomagnetic Variation Field by Chaotic RBF Neural Network[J]. jgg, 2023, 43(3): 308-312.
YU Wenqiang,LI Houpu,QIN Qingliang et al. Prediction Model of the Geomagnetic Variation Field by Chaotic RBF Neural Network[J]. jgg, 2023, 43(3): 308-312.
URL:
http://www.jgg09.com/EN/ OR http://www.jgg09.com/EN/Y2023/V43/I3/308
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