Satellite attitude control through evolving a neural network
Created by W.Langdon from
gp-bibliography.bib Revision:1.7975
- @InProceedings{Li:2010:ICMA,
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author = "Shuguang Li and Jianping Yuan and Jianjun Luo and
Weihua Ma",
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title = "Satellite attitude control through evolving a neural
network",
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booktitle = "2010 International Conference on Mechatronics and
Automation (ICMA)",
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year = "2010",
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month = "4-7 " # aug,
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pages = "553--559",
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address = "Xi'an, China",
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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.",
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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",
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DOI = "doi:10.1109/ICMA.2010.5588493",
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ISSN = "2152-7431",
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notes = "Also known as \cite{5588493}",
- }
Genetic Programming entries for
Shu-guang Li
Jianping Yuan
Jianjun Luo
Weihua Ma
Citations