Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

BP neural network-based microwave attenuation precipitation particle type identification method

A BP neural network and precipitation particle technology, applied in biological neural network models, neural learning methods, neural architectures, etc., can solve problems such as difficulty in finding analytical solutions for nonlinear integral equations, and achieve the avoidance of complicated steps, precision avoidance, The effect of improving accuracy

Inactive Publication Date: 2019-12-06
HOHAI UNIV
View PDF3 Cites 10 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, this method often requires rich experience accumulation, and there is a problem that it is difficult to find analytical solutions for nonlinear integral equations

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • BP neural network-based microwave attenuation precipitation particle type identification method
  • BP neural network-based microwave attenuation precipitation particle type identification method
  • BP neural network-based microwave attenuation precipitation particle type identification method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0032] The present invention will be described in further detail below in conjunction with the accompanying drawings.

[0033] Such as figure 1 As shown, the present invention provides a kind of identification method based on the microwave attenuation precipitation particle type of BP neural network, and the present embodiment utilizes the data of Gothenburg area in southwest Sweden to classify, and concrete steps are as follows:

[0034] Step 1: Use multiple dual-polarized microwave links with different frequencies (the number of frequencies in this embodiment is 2) to obtain characteristic quantities of microwave attenuation caused by different types of precipitation particles at different frequencies and polarization directions.

[0035] a. The selected links are shown in Table 1:

[0036] Table 1

[0037]

[0038] b. Measure the power of the transmitting end and the power of the receiving end on the 4 links, respectively denoted as P a，f，θ and P b，f，θ , f is the fre...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention discloses a BP neural network-based microwave attenuation precipitation particle type identification method. The BP neural network-based microwave attenuation precipitation particle typeidentification method comprises the following steps: firstly, taking a microwave attenuation value in each polarization direction of each frequency line as a characteristic quantity by utilizing microwave attenuation information in different polarization directions of double-frequency and above lines, and taking a plurality of groups of characteristic quantities as matrixes of rainfall input andoutput; and then, completing nonlinear mapping from m dimension to n dimension through a BP neural network, extracting a plurality of characteristic values of the precipitation particles, and completing automatic identification of the types of the precipitation particles is completed. By adopting the BP neural network-based microwave attenuation precipitation particle type identification method, special weather conditions such as rain, snow and hail can be automatically identified in real time, and the distinguishing and monitoring effects on precipitation particles such as rain, snow and hailare improved, and the research on precipitation is promoted.

Description

technical field [0001] The invention relates to the field of surface meteorological detection, in particular to a method for identifying the type of microwave attenuated precipitation particles based on a BP neural network. Background technique [0002] my country is a country with a concentrated rainy season, frequent rainstorms, and more prominent natural disasters. Precipitation is an important cause of natural disasters such as floods, landslides, and mudslides. When measuring and studying precipitation, it is first necessary to distinguish the types of precipitation particles, including rain, snow, hail, etc. Different types of precipitation particles have different formation mechanisms and their microphysical characteristics are quite different, so it is of great significance to distinguish the types of precipitation particles. At present, the identification of precipitation types is mainly based on the method of weather radar volume scan data and dual polarization Do...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06N3/048G06N3/045G06F18/241
Inventor 杨涛郑鑫陈志远秦友伟师鹏飞李振亚周旭东
Owner HOHAI UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com