Fault Diagnosis Algorithm of Integrated Navigation Based on D-S Evidence Theory Fuses Neural Network
Authors: 
Xianli Hong, Yongxia Yang, Peng Wang, Yihan Zhao, Ruohai Di, Mianmian DongAuthors Info & Claims
Xianli Hong, Yongxia Yang, Peng Wang, Yihan Zhao, Ruohai Di, Mianmian DongAuthors Info & ClaimsPages 388 - 391
Abstract
In the field of fault diagnosis technology of integrated navigation, traditional methods require high precision of system model, and the single neural network detection has the defects of misdiagnosis, missed diagnosis and large error rate. This paper presents a fault diagnosis method based on fusion neural network. The algorithm takes the error data as the input of neural network. Firstly, BP neural network and improved dynamic particle swarm optimization (PSO) BP neural network are used for fault diagnosis, then the detection results of these two neural networks are fused by D-S evidence theory, and finally verified by simulation. The experimental results show that the method can effectively reduce the error rate of fault detection and improve the accuracy and reliability of fault detection.
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Published In
December 2020
687 pages
ISBN:9781450388665
DOI:10.1145/3452940
Copyright © 2020 ACM.
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 17 May 2021
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- Research-article
- Research
- Refereed limited
Funding Sources
- Shaanxi Province Key plan of science and Technology Department
- Shaanxi Key Laboratory of integrated and intelligent Navigation
Conference
ICITEE2020
ICITEE2020: The 3rd International Conference on Information Technologies and Electrical Engineering
December 3 - 5, 2020
Hunan, Changde City, China
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