Prediction of the critical frequency of the ionospheric F2 layer based on ELM

A critical frequency and prediction method technology, applied in the direction of electrical digital data processing, special data processing applications, instruments, etc., can solve the problem of ignoring the model training speed and achieve good prediction performance and fast learning speed

Pending Publication Date: 2019-01-15
TIANJIN UNIV
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Problems solved by technology

However, these research results only focus on how to improve the prediction accuracy of the model, while ignoring the problem of model training speed

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  • Prediction of the critical frequency of the ionospheric F2 layer based on ELM
  • Prediction of the critical frequency of the ionospheric F2 layer based on ELM
  • Prediction of the critical frequency of the ionospheric F2 layer based on ELM

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Embodiment 1

[0028] The invention proposes an ELM-based prediction method for the critical frequency of the F2 layer of the ionosphere. The present invention will now be described in detail in conjunction with the establishment method of the prediction model of the daily and hourly foF2 value at Darwin Station in Australia from 1995 to 2013. In this embodiment, sigmoid is selected as the activation function, and the number of neurons in the hidden layer is 50.

[0029] Step 1: Obtain the measured data of the impact factors of foF2 (including solar activity index and geomagnetic activity index) and the hourly value of foF2, and divide the data into three groups, which are used as the training data, test data and verification data of the ELM model.

[0030] Among them, the solar activity index and the geomagnetic activity index are downloaded from the National Oceanic and Atmospheric Administration ( http: / / www.noaa.gov ). The measured data of the hourly value of foF2 are downloaded from ...

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Abstract

The invention discloses a method for predicting the critical frequency of the ionospheric F2 layer based on the ELM: obtaining the measured data of the influence factor of the foF2 and the value of the foF2 hour, and dividing the data into three groups as the training data, the test data and the verification data of the ELM model respectively; determining the input and output variables of the ELMmodel; the training data and test data are imported into the ELM model, and the ELM model is trained. The prediction error RMSE of the ELM model was compared with the expected value of the precision of the ELM model until the prediction error RMSE was less than the expected value and the optimal ELM model was determined after the training. The validation data is imported into the trained optimal ELM model, and the accurate output prediction value is obtained. The invention can realize accurate and fast prediction of foF2.

Description

technical field [0001] The invention relates to the field of ionospheric parameter modeling, and more specifically relates to an ELM-based prediction method for the critical frequency of the F2 layer of the ionosphere. Background technique [0002] In the past 30 years, with the rapid development of modern technologies such as electronics and communications, many scientific and technological researches and applications have focused on the influence of the atmosphere on radio wave propagation. Therefore, a full understanding of the ionosphere is an important basis for human beings to understand and utilize their own living environment. The F2-layer critical frequency (foF2) is the main characteristic parameter of the ionosphere, and its temporal and spatial variation has an important impact on high-frequency communication, navigation, global positioning satellite and high-frequency radio systems. Predictive modeling of foF2 is instructive for the development of the above sys...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/50
CPCG06F30/20G06F2111/10
Inventor 马建国白红梅傅海鹏吴淘锁
Owner TIANJIN UNIV
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