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1 August 2021 Characterization of feedback systems with time delay using machine learning
Aykut Argun, Tobias Thalheim, Stefano Bo, Frank Cichos, Giovanni Volpe
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Proceedings Volume 11804, Emerging Topics in Artificial Intelligence (ETAI) 2021; 1180414 (2021) https://doi.org/10.1117/12.2593592
Event: SPIE Nanoscience + Engineering, 2021, San Diego, California, United States
Abstract
There is a very limited number of methods to analyze experimental trajectories of systems with feedback and time delay. In most cases, an analytical approach is not even possible. In this study, we show that the feedback parameters and the delay can be accurately characterized using machine learning, namely recurrent neural networks. We demonstrate that our method can dramatically expand the number of time-delayed feedback scenarios that we can characterize. We exemplify our findings on different numerical and experimental scenarios.
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Aykut Argun, Tobias Thalheim, Stefano Bo, Frank Cichos, and Giovanni Volpe "Characterization of feedback systems with time delay using machine learning", Proc. SPIE 11804, Emerging Topics in Artificial Intelligence (ETAI) 2021, 1180414 (1 August 2021); https://doi.org/10.1117/12.2593592
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KEYWORDS
Machine learning
Bacteria
Neural networks
Particles
Stochastic processes
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