Research on performance degradation of force sensors based on improved error back propagation algorithm

Studying the performance degradation of force sensors, a core component of aircraft control stick force measurement devices, is essential. The key to investigating equipment performance degradation lies in constructing a degradation model. When dealing with degradation data from specific devices, re...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Journal of physics. Conference series Ročník 2849; číslo 1; s. 12025 - 12030
Hlavní autoři: Wang, Pengling, Wang, Peng, Wang, Chu, Wang, Bin, Chen, Chen, Li, Liangliang
Médium: Journal Article
Jazyk:angličtina
Vydáno: Bristol IOP Publishing 01.09.2024
Témata:
Aircraft control
Aircraft performance
Back propagation networks
Control equipment
Control sticks
Error analysis
Error reduction
Force measurement
Genetic algorithms
Measuring instruments
Neural networks
Performance degradation
Sensors
ISSN:1742-6588, 1742-6596
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Studying the performance degradation of force sensors, a core component of aircraft control stick force measurement devices, is essential. The key to investigating equipment performance degradation lies in constructing a degradation model. When dealing with degradation data from specific devices, relying solely on fitting methods may not effectively describe the degradation of the equipment. This study introduces an error backpropagation neural network model for constructing the performance degradation model of force sensors, and optimization improvements are made by using a genetic algorithm. Experimental results demonstrate a 99% reduction in Root Mean Square Error with the proposed modeling approach.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/2849/1/012025