Type-II Fuzzy Neural Networks for Image Stabilization of the Airborne Camera

The vibration rule of the airborne camera was studied to solve the image vibration in aerial photography of the Micro Aircraft Vehicle. A method based on the ability of function approximation of type 2 fuzzy neural networks with self-organizing recurrent intervals (SRIT2FNN) to simulate the vibratio...

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Bibliographic Details
Published in:IOP conference series. Materials Science and Engineering 2020-03, Vol.790 (1), p.12150
Main Authors: Zhang, Yingbo, Sheng, Ziheng, Li, Di
Format: Article
Language:English
Subjects:
Aerial photography
airborne camera in micro aircraft vehicle
Artificial neural networks
Back propagation networks
Cameras
Fuzzy logic
Image stabilizers
Neural networks
real-time image stabilization
Stabilization
Type-II fuzzy neural networks
Vibration
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Summary:The vibration rule of the airborne camera was studied to solve the image vibration in aerial photography of the Micro Aircraft Vehicle. A method based on the ability of function approximation of type 2 fuzzy neural networks with self-organizing recurrent intervals (SRIT2FNN) to simulate the vibration rule of airborne camera in the MAV and predict the vibration displacement vectors during image stabilization was proposed. The SRIT2FNN has no initial rules, which are generated from the simultaneous on-line parameter and structure learning. The results show that SRIT2FNN control system is more stable and the higher precision, and good real-time performance than combined BP neural networks.
ISSN:1757-8981
1757-899X
DOI:10.1088/1757-899X/790/1/012150