Method for predicting occurrence probability of ionospheric frequency expansion F by using neural network

Abstract

A method for predicting the occurrence probability of ionospheric frequency expansion F by using a neural network comprises the following steps: A, reading information of ionospheric frequency expansion F from foF2 data, and calculating the occurrence probability of the ionospheric frequency expansion F; B, finding out an F10.7 index and a Dst index at the same moment as the foF2 data, and synthesizing the F10.7 index and the Dst index with the foF2 data into a sample data set; C, separating training data and test data from the sample data set, employing a neural network algorithm to invert the occurrence probability P of the ionospheric frequency extension F, generating a prediction model of the occurrence probability P of the ionospheric frequency extension F by repeatedly training the training data, and utilizing the test data to perform testing until the prediction model reaches the required precision; D, predicting the occurrence probability of ionospheric frequency expansion F through the trained prediction model. According to the invention, the occurrence probability of the ionospheric extension F can be predicted in real time, quickly and highly precisely, and the model canbe applied to other prediction systems.

Description

technical field [0001] The invention belongs to the field of prediction of the occurrence probability of ionospheric expansion F, and specifically relates to a method for predicting the occurrence probability of ionospheric frequency expansion F by using a neural network, so as to realize high-precision and rapid prediction of whether ionospheric expansion F occurs. Background technique [0002] Due to the combined effects of solar ultraviolet radiation, X-ray radiation, and cosmic rays or particle radiation, the upper Earth's atmosphere will be partially ionized, producing free electrons and ions, forming a plasma composed of free electrons, positive ions, and neutral molecules and atoms , this plasma layer is called the ionosphere (60-1000 kilometers). Since the electron concentration of the ionosphere varies with height, there are often several regions of maximum electron concentration, called layers. According to the distribution of electron density, the ionosphere can ...

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Problems solved by technology

[0005] The purpose of the present invention is to provide a method for predicting the occurrence probability of ionospheric frequency expansion F by using a neural network, aiming at the problems that the prediction of ionospheric expansion F in the above-mentioned prior art cannot provide real-time data, insufficient precision, large amount of calculation and long time consumption, It can predict the occurrence probability of ionospheric expansion F in real time, quickly and with high precision, and the model can be applied to other prediction systems

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