Satellite attitude control through evolving a neural network

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{Li:2010:ICMA,

• author = "Shuguang Li and Jianping Yuan and Jianjun Luo and Weihua Ma",
• title = "Satellite attitude control through evolving a neural network",
• booktitle = "2010 International Conference on Mechatronics and Automation (ICMA)",
• year = "2010",
• month = "4-7 " # aug,
• pages = "553--559",
• address = "Xi'an, China",
• abstract = "We propose a pure topological recurrent network controller for satellite attitude control, which has random binary connections in hidden layer, and all hidden neurons are activated by sinusoidal functions. A direct graph encoding method and four genetic operators are implemented for using genetic programming to train this controller. Moreover, a simulated small satellite which equipped with three reaction wheels was developed, then this simulator was employed to test the controller and training method for a given simple attitude adjusting mission. The experimental results reveal that this controller has the simplicity, usability and potentials for satellite attitude control through evolutionary learning.",
• keywords = "genetic algorithms, genetic programming, direct graph encoding method, evolutionary learning, genetic operator, neural network, pure topological recurrent network controller, satellite attitude control, sinusoidal function, training method, artificial satellites, attitude control, directed graphs, encoding, neurocontrollers, recurrent neural nets",
• DOI = "doi:10.1109/ICMA.2010.5588493",
• ISSN = "2152-7431",
• notes = "Also known as \cite{5588493}",

}

Genetic Programming entries for Shu-guang Li Jianping Yuan Jianjun Luo Weihua Ma

Citations