Summary of Controlling Chaos Using Edge Computing Hardware, by Robert M. Kent et al.
Controlling Chaos Using Edge Computing Hardware
by Robert M. Kent, Wendson A.S. Barbosa, Daniel J. Gauthier
First submitted to arxiv on: 8 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Neural and Evolutionary Computing (cs.NE)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach to creating a digital twin of a system using machine learning is presented. The goal is to develop an accurate model that can be used for controlling autonomous systems, while minimizing its size and power consumption. A nonlinear controller based on next-generation reservoir computing is designed to control a chaotic system to an arbitrary time-dependent state. This model is shown to be both accurate and efficient, requiring only 25.0 ± 7.0 nJ per evaluation, making it suitable for deployment on embedded devices without the need for cloud-computing connections. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A digital twin of a system can help control autonomous systems by predicting their behavior. This paper uses machine learning to create such a model. The goal is to make it small and energy-efficient so it can be used in real-life situations where a connection to the internet isn’t possible. The researchers created a special kind of computer program that can control a chaotic system, which means it can be unpredictable. Their model works well and only uses a tiny amount of energy. |
Keywords
» Artificial intelligence » Machine learning
