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A Bayesian regularized artificial neural network for adaptive optics forecasting
Sun, Zhi1,3; Chen, Ying2; Li, Xinyang2; Qin, Xiaolin1; Wang, Huiyong1,3,4
Source PublicationOptics Communications
Volume382Pages:519-527
2017
Language英语
ISSN0030-4018
Indexed BySCI ; Ei
AbstractReal-time adaptive optics is a technology for enhancing the resolution of ground-based optical telescopes and overcoming the disturbance of atmospheric turbulence. The performance of the system is limited by delay errors induced by the servo system and photoelectrons noise of wavefront sensor. In order to cut these delay errors, this paper proposes a novel model to forecast the future control voltages of the deformable mirror. The predictive model is constructed by a multi-layered back propagation network with Bayesian regularization (BRBP). For the purpose of parallel computation and less disturbance, we adopt a number of sub-BP neural networks to substitute the whole network. The Bayesian regularized network assigns a probability to the network weights, allowing the network to automatically and optimally penalize excessively complex models. The simulation results show that the BRBP introduces smaller mean absolute percentage error (MAPE) and mean square errors (MSE) than other typical algorithms. Meanwhile, real data analysis results show that the BRBP model has strong generalization capability and parallelism. © 2016 Elsevier B.V.
KeywordAdaptive control systems - Atmospheric turbulence - Backpropagation - Complex networks - Errors - Mean square error - Network layers - Neural networks - Parallel processing systems
Document Type期刊论文
Identifierhttp://ir.ioe.ac.cn/handle/181551/8862
Collection自适应光学技术研究室(八室)
Affiliation1.Chengdu Institute of Computer Applications, Chinese Academy of Sciences, Chengdu; 610041, China;
2.Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu; 610209, China;
3.University of Chinese Academy of Sciences, Beijing; 100049, China;
4.Guangxi Key Laboratory of Cryptography and Information Security, Guilin University of Electronic Technology, Guilin; 541004, China
Recommended Citation
GB/T 7714
Sun, Zhi,Chen, Ying,Li, Xinyang,et al. A Bayesian regularized artificial neural network for adaptive optics forecasting[J]. Optics Communications,2017,382:519-527.
APA Sun, Zhi,Chen, Ying,Li, Xinyang,Qin, Xiaolin,&Wang, Huiyong.(2017).A Bayesian regularized artificial neural network for adaptive optics forecasting.Optics Communications,382,519-527.
MLA Sun, Zhi,et al."A Bayesian regularized artificial neural network for adaptive optics forecasting".Optics Communications 382(2017):519-527.
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