Aerosol extinction coefficient inversion method base on feedback type RBF neural network

A technology of extinction coefficient and neural network, which is applied in the direction of material analysis, measurement device, suspension and porous material analysis by optical means, and can solve the fine inversion of unfavorable aerosol optical properties and the uncertainty of extinction coefficient inversion, etc. problems, to achieve the effect of avoiding errors, increasing credibility, and accurate inversion

Active Publication Date: 2018-11-27
NANJING UNIV OF INFORMATION SCI & TECH
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Then when inverting the extinction coefficient, since there are many variables in the radar equation, many variables are often replaced by empirical values ​​or assumptions for the convenience of calculation, such as the boundary value of the aer...

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  • Aerosol extinction coefficient inversion method base on feedback type RBF neural network
  • Aerosol extinction coefficient inversion method base on feedback type RBF neural network
  • Aerosol extinction coefficient inversion method base on feedback type RBF neural network

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[0032] The present invention will be further described below in conjunction with the accompanying drawings. The following examples are only used to illustrate the technical solution of the present invention more clearly, but not to limit the protection scope of the present invention.

[0033] Such as figure 1 As shown, an aerosol extinction coefficient inversion method based on a feedback RBF neural network includes the following steps:

[0034] Step 1, get training samples.

[0035] The training samples include input and expected output. The historical echo signal power is used as the input of the RBF neural network, and the aerosol extinction coefficient obtained from the historical echo signal is used as the expected output of the RBF neural network.

[0036] In order to ensure the accuracy of the final extinction coefficient of the network, the expected output should be as accurate as possible. Therefore, when using the Fernald method to obtain the expected output, it is...

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Abstract

The invention discloses an aerosol extinction coefficient inversion method base on a feedback type RBF neural network. The method includes the following steps: 1) training the RBF neural network by using input and expected output, wherein the power of historical echo signals is used as the input of the RBF neural network and aerosol extinction coefficients obtained from the historical echo signalsare used as the expected output of the RBF neural network; and 2) inverting the aerosol extinction coefficients based on the feedback type RBF neural network. The method uses the feedback type RBF neural network to invert the aerosol extinction coefficients and stores internal mechanisms between information in a network through the learning of sample patterns, which effectively avoids the uncertainty caused by a plurality of assumptions. The method has fast response speed and good robustness.

Description

technical field [0001] The invention relates to an aerosol extinction coefficient inversion method based on a feedback RBF neural network, belonging to the technical field of aerosol measurement. Background technique [0002] Atmospheric aerosol is a multiphase system composed of objects in different phases, suspended in the atmosphere, and composed of various solid or liquid particles, which will affect the development and changes of many physical and chemical processes in the atmospheric environment. The diameter of aerosol particles ranges from 0.001 to 100 μm, and they can stay in the atmosphere for at least several hours or even several days, thereby changing the composition and structure of the atmosphere, disrupting and destroying the original normal ecosystem. It is mainly distributed throughout the atmosphere and affects climate effects and thus human health. Therefore, it has very important practical significance to improve the atmospheric environment by detecting...

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Application Information

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IPC IPC(8): G01N15/06G01N21/17
CPCG01N15/06G01N21/17
Inventor 常建华李红旭房久龙刘振兴杨镇博刘秉刚徐帆
Owner NANJING UNIV OF INFORMATION SCI & TECH
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