A Deep Learning Approach to Optimal Sampling Problems

Time-triggered and event-triggered sampling methods have been widely adopted in control systems. Optimal sampling problems of the two mechanisms have also received great attentions. However, for high-dimensional systems, analytical methods have some limitations. In this study, we propose a model-fre...

Full description

Saved in:
Bibliographic Details
Published inMathematical problems in engineering Vol. 2022; pp. 1 - 7
Main Authors Wang, Xinxin, Meng, Xiangyu, Li, Fangfei
Format Journal Article
LanguageEnglish
Published New York Hindawi 16.06.2022
Hindawi Limited
Subjects
Algorithms
Approximation
Deep learning
Dimensional analysis
Machine learning
Methods
Neural networks
Partial differential equations
Random variables
Sampling methods
Online AccessGet full text

Cover

More Information
Summary:Time-triggered and event-triggered sampling methods have been widely adopted in control systems. Optimal sampling problems of the two mechanisms have also received great attentions. However, for high-dimensional systems, analytical methods have some limitations. In this study, we propose a model-free method, called soft greedy policy for neural network fitting, to calculate the optimal sampling period of the time-triggered impulse control and the optimal threshold of the event-triggered impulse control. A neural network is used to approximate the objective function and then is trained. This approach is more widely applicable than the analytical method. At the same time, compared with different ways of generating data, the algorithm can carry out real-time update with greater flexibility and higher accuracy. Simulation results are provided to verify the effectiveness of the proposed algorithm.
ISSN:1024-123X
1563-5147
DOI:10.1155/2022/4453150