Affiliation:
1. School of Microelectronics, Tianjin University, Tianjin 300072, China
2. Qingdao Key Laboratory of Marine Information Perception and Transmission, Qingdao Institute for Ocean Technology, Tianjin University, Qingdao 266200, China
3. Shandong Engineering Technology Research Center of Ocean Information Awareness and Transmission, Qingdao 266200, China
Abstract
In order to improve the prediction accuracy of ionospheric total electron content (TEC), a combined intelligent prediction model (MMAdapGA-BP-NN) based on a multi-mutation, multi-cross adaptive genetic algorithm (MMAdapGA) and a back propagation neural network (BP-NN) was proposed. The model combines the international reference ionosphere (IRI), statistical machine learning (SML), BP-NN, and MMAdapGA. Compared with the IRI, SML-based, and other neural network models, MMAdapGA-BP-NN has higher accuracy and a more stable prediction effect. Taking the Athens station in Greece as an example, the root mean square errors (RMSEs) of MMAdapGA-BP-NN in 2015 and 2020 are 2.84TECU and 0.85TECU, respectively, 52.27% and 72.13% lower than the IRI model. Compared with the single neural network model, the MMAdapGA-BP-NN model reduced RMSE by 28.82% and 24.11% in 2015 and 2020, respectively. Furthermore, compared with the neural network optimized by a single mutation genetic algorithm, MMAdapGA-BP-NN has fewer iterations ranging from 10 to 30. The results show that the prediction effect and stability of the proposed model have obvious advantages. As a result, the model could be extended to an alternative prediction scheme for more ionospheric parameters.
Funder
State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System
Subject
General Earth and Planetary Sciences
Cited by
2 articles.
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