Open AccessType-II Fuzzy Neural Networks for Image Stabilization of the Airborne Camera
Author(s) -
Yingbo Zhang,
Ziheng Sheng,
Di Li
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/790/1/012150
Subject(s) - artificial neural network , displacement (psychology) , artificial intelligence , vibration , computer science , image (mathematics) , computer vision , photography , line (geometry) , fuzzy logic , function (biology) , fuzzy control system , control theory (sociology) , mathematics , control (management) , acoustics , physics , psychotherapist , biology , psychology , art , geometry , evolutionary biology , visual arts
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.