BP (Back Propagation) neural network based high-precision correction and test method for resonance cylinder pressure sensor

A BP neural network and pressure sensor technology, applied in biological neural network models, measuring fluid pressure, instruments, etc., can solve the problem that the fitting accuracy cannot meet the accuracy requirements, solve the problem of high-precision calibration and test, enhance the approximation ability, The effect of improving the accuracy of the test

Active Publication Date: 2012-10-31
BEIJING AUTOMATION CONTROL EQUIP INST
View PDF1 Cites 10 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

This method cannot meet the accuracy requirements of the model 0.028%FS in terms of fitting accuracy

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 (Back Propagation) neural network based high-precision correction and test method for resonance cylinder pressure sensor
  • BP (Back Propagation) neural network based high-precision correction and test method for resonance cylinder pressure sensor
  • BP (Back Propagation) neural network based high-precision correction and test method for resonance cylinder pressure sensor

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0032] A high-precision calibration method for a resonant cylinder pressure sensor based on a BP neural network provided by the present invention is introduced below in conjunction with the accompanying drawings and embodiments:

[0033] A method for calibrating a pressure sensor of a resonant cylinder based on a BP neural network, including constructing a sensor BP neural network with a double-hidden layer network structure, so that the input variables of the network structure are the output period T and the temperature voltage V of the sensor, and the output variable is the pressure value P; collect the output cycle and temperature voltage of the sensor under different temperature and pressure input conditions; collect the output of the sensor under different temperature and pressure conditions as a calibration test and inspection sample point.

[0034] In the sensor BP neural network, the hyperbolic tangent S-type transfer function is used as the transfer function between th...

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 belongs to the technical field of test and measurement and particularly relates to a BP (Back Propagation) neural network based high-precision correction and test method for a resonance cylinder pressure sensor, aiming to increase the correction and test precision for the resonance cylinder pressure sensor. The method comprises the steps of: structuring a sensor BP neural network of a dual-implicit-strata network structure, wherein input variables of the network structure are respectively the output period T and the temperature voltage V of the sensor, and output variables of the network structure is the pressure value P; acquiring output periods and temperature voltages of the sensor at different temperature under different pressure input conditions; and collecting output quantities of the sensor at different temperature under different pressure conditions to serve as correction and test as well as inspection sample points. According to the method, with the dual-implicit-strata network structure, the number of network parameters is reduced while the output precision is guaranteed; and the correction and test precision of the resonance cylinder pressure sensor is increased by 25%, and the high-precision correction and test of the sensor is realized.

Description

technical field [0001] The invention belongs to the technical field of testing and measurement, and relates to a high-precision calibration method for a resonant cylinder pressure sensor, in particular to a high-precision calibration method for a resonant cylinder pressure sensor based on a BP neural network. Background technique [0002] In order to realize the precise control of the missile, the demand for the range and accuracy of the pressure measurement is increasing, so it is necessary to develop a high-precision pressure sensor. For the current resonant cylinder pressure sensor with good stability and high precision, the level of high-precision calibration technology has become an important factor affecting the accuracy of the sensor. In the past, the polynomial fitting method based on the physical model of the sensor is usually used, and the vibration cylinder pressure sensor outputs periodic signals. T and the temperature and voltage signal V, according to the physi...

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): G01L27/00G06N3/02
Inventor 时兆峰苑景春孙洪庆李邦清刘建丰李劲松周明刘栋苏赵莹
Owner BEIJING AUTOMATION CONTROL EQUIP INST
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

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

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap