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A Satellite Task Planning Algorithm Based on a Symmetric Recurrent Neural Network

by Sikai Liu * and Jun Yang
College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China
*
Author to whom correspondence should be addressed.
Symmetry 2019, 11(11), 1373; https://doi.org/10.3390/sym11111373
Received: 16 October 2019 / Revised: 29 October 2019 / Accepted: 3 November 2019 / Published: 6 November 2019
(This article belongs to the Special Issue Optimized Machine Learning Algorithms for Modeling Dynamical Systems)
The intelligent satellite, iSAT, is a concept based on software-defined satellites. Earth observation is one of the important applications of intelligent satellites. With the increasing demand for rapid satellite response and observation tasks, intelligent satellite in-orbit task planning has become an inevitable trend. In this paper, a mixed integer programming model for observation tasks is established, and a heuristic search algorithm based on a symmetric recurrent neural network is proposed. The configurable probability of the observation task is obtained by constructing a structural symmetric recurrent neural network, and finally, the optimal task planning scheme is obtained. The experimental results are compared with several typical heuristic search algorithms, which have certain advantages, and the validity of the paper is verified. Finally, future application prospects of the method are discussed. View Full-Text
Keywords: in-orbit observation; task planning; recurrent neural network; heuristic algorithm in-orbit observation; task planning; recurrent neural network; heuristic algorithm
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Liu, S.; Yang, J. A Satellite Task Planning Algorithm Based on a Symmetric Recurrent Neural Network. Symmetry 2019, 11, 1373.

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