DocumentCode :
2783550
Title :
Structural optimization for deformable mirror hexagon support based on artificial intelligence technology
Author :
Zhao, Fu ; Gong, Yanjue ; Zhang, Li ; Xiang, Huiyu ; Wang, Ping
Author_Institution :
Coll. of Mech. Eng., Beijing Technol. & Bus. Univ., Beijing, China
fYear :
2009
fDate :
6-8 Nov. 2009
Firstpage :
491
Lastpage :
494
Abstract :
Taking account of the complexity of optimization of the deformable mirror support structure, this paper puts forward an intelligent optimized method for multivariable structure. On the basis of modal analysis which is implemented by the finite element software, a method combining orthogonal experiment, artificial neural network (ANN) and genetic algorithms (GA) is applied to optimization of the structural parameters for the DM hexagon support structure. Finally, the random vibration analysis is given for the optimized support structure and the results indicate the optimization method based on artificial intelligent technology is effective.
Keywords :
deformation; finite element analysis; genetic algorithms; mirrors; neural nets; structural engineering computing; vibrations; ANN; DM hexagon support structure; artificial intelligence technology; artificial neural network; finite element software; genetic algorithms; intelligent optimized method; mirror deformability; modal analysis; multivariable structure; random vibration analysis; structural optimization; Artificial intelligence; Artificial neural networks; Delta modulation; Finite element methods; Genetic algorithms; Intelligent structures; Mirrors; Modal analysis; Optimization methods; Structural engineering; artificial intelligent; artificial neural network (ANN); genetic algorithms (GA); optimization;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Network Infrastructure and Digital Content, 2009. IC-NIDC 2009. IEEE International Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4244-4898-2
Electronic_ISBN :
978-1-4244-4900-6
Type :
conf
DOI :
10.1109/ICNIDC.2009.5360971
Filename :
5360971
Link To Document :
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