| A Bayesian regularized artificial neural network for adaptive optics forecasting | |
| Sun, Zhi1,3; Chen, Ying2; Li, Xinyang2; Qin, Xiaolin1; Wang, Huiyong1,3,4 | |
| Source Publication | Optics Communications
 |
| Volume | 382Pages:519-527 |
| 2017 | |
| Language | 英语 |
| ISSN | 0030-4018 |
| Indexed By | SCI ; Ei |
| Abstract | Real-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. |
| Keyword | Adaptive control systems - Atmospheric turbulence - Backpropagation - Complex networks - Errors - Mean square error - Network layers - Neural networks - Parallel processing systems |
| Document Type | 期刊论文 |
| Identifier | http://ir.ioe.ac.cn/handle/181551/8862 |
| Collection | 自适应光学技术研究室（八室） |
| Affiliation | 1.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. |
| Files in This Item: | ||||||
| File Name/Size | DocType | Version | Access | License | ||
| 2017-2102.pdf（1659KB） | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | Application Full Text | |
Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.
Edit Comment