Journal
OPTICS COMMUNICATIONS
Volume 382, Issue -, Pages 519-527Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.optcom.2016.08.035
Keywords
Adaptive optics; Predictive controller; Back propagation network; Bayesian regularization; Parallel computing
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Funding
- China 973 Project [2011CB302402, NKBRPC-2011CB302402]
- National Natural Science Foundation of China [61402537, 91118001]
- West Light Foundation of the Chinese Academy of Sciences from China
- Open Project of Institute of Optics and Electronics of Chinese Academy of Sciences
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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 de formable 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. (C) 2016 Elsevier B.V. All rights reserved.
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