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Solar radio filtering method and system based on improved LSTM network

An improved solar radio technology, applied in the field of solar radio filtering, can solve problems such as inaccurate prediction of numerical values, and achieve the effects of improving accuracy, reducing workload, and reducing errors

Active Publication Date: 2021-07-09
SHANDONG UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, in predicting the signal value of interfering radio stations in the solar radio burst period, there is often a problem that the predicted value is not accurate enough

Method used

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  • Solar radio filtering method and system based on improved LSTM network
  • Solar radio filtering method and system based on improved LSTM network
  • Solar radio filtering method and system based on improved LSTM network

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Experimental program
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Embodiment 1

[0064] According to an embodiment of the present invention, an embodiment of a solar radio filtering method based on an improved LSTM network is disclosed, including the following steps:

[0065] (1) Obtain the radio channel that needs to be processed, and for each radio channel, select the channel data of the set number before the solar eruption event;

[0066] (2) Preprocessing the channel data, and extracting data by segmented sliding windows;

[0067] (3) Input the extracted data into the trained recurrent neural network model based on the digital mapping method, and output the solar radio prediction value when the sun erupts;

[0068] Wherein, the cyclic neural network model based on the digital mapping method regards the numerical values ​​appearing in the sequence as different classes, and the output prediction value is the numerical value with the highest prediction probability in the classification.

[0069] The method described in this embodiment will be described i...

Embodiment 2

[0136] According to an embodiment of the present invention, a solar radio filtering system based on an improved LSTM network is disclosed, including:

[0137] The data acquisition module is used to obtain the radio channel that needs to be processed, and for each radio channel, select the channel data of the set number before the solar eruption event;

[0138] A data processing module, configured to preprocess the channel data and extract data using segmented sliding windows;

[0139] The model prediction module is used to input the extracted data into the trained cyclic neural network model based on the digital mapping method, and output the solar radio prediction value when the sun erupts; wherein, the cyclic neural network model based on the digital mapping method will sequence The values ​​that appear are regarded as different classes, and the output prediction value is the value with the highest prediction probability in the classification.

[0140] It should be noted th...

Embodiment 3

[0142] According to an embodiment of the present invention, an embodiment of a terminal device is disclosed, which includes a processor and a memory, the processor is used to implement instructions; the memory is used to store multiple instructions, and the instructions are suitable for being loaded and executed by the processor The solar radio filtering method based on the improved LSTM network described in the first embodiment.

[0143] In some other embodiments, a computer-readable storage medium is disclosed, in which a plurality of instructions are stored, and the instructions are suitable for being loaded by the processor of the terminal device and executing the improved LSTM network based on the first embodiment described in the first embodiment. Solar radio filtering method.

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Abstract

The invention discloses a solar radio filtering method and system based on an improved LSTM network; the method comprises the steps: obtaining radio channels needing to be processed, and selecting a set number of channel data before a solar burst event for each radio channel; preprocessing the channel data, and extracting the data by using a segmented sliding window; and inputting the extracted data into a trained recurrent neural network model based on a digital mapping method, and outputting a solar radio prediction value during solar burst, wherein the recurrent neural network model based on the digital mapping method regards numerical values appearing in the sequence as different classes, and the output predicted value is the numerical value with the maximum prediction probability in the classification. According to the method, a recurrent neural network structure based on a digital mapping method is provided, before data enters the network, single-frequency channel radio data is classified, mapping of time periods to time points is established, the accuracy of prediction results is greatly improved, and possibility is provided for solar spectrum lossless filtering.

Description

technical field [0001] The invention relates to the technical field of solar radio filtering, in particular to a solar radio filtering method and system based on an improved LSTM network. Background technique [0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art. [0003] Solar activity is closely related to our daily life, so solar radio has become an important field in astrophysics research. Especially the research on the process of solar radio bursts, the important information it carries not only helps to explain the physical process of related plasma changes, but also can find out the law of energy changes and analyze important physical phenomena such as the movement of matter. [0004] In order to better study the fine structure of solar radio bursts, the project team established a solar radio spectrometer with high time resolution and high frequency resolution in Chash...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06N3/047G06N3/044G06F2218/02G06F2218/08G06F2218/12G06F18/241G06F18/2415
Inventor 杜清府张巧曼高昌林侯宜春苗青韩成生冯士伟
Owner SHANDONG UNIV
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