Predicting Chaotic System Behavior using Machine Learning Techniques
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866911186214191104 |
|---|---|
| author | Rao, Huaiyuan Zhao, Yichen Lai, Qiang |
| author_facet | Rao, Huaiyuan Zhao, Yichen Lai, Qiang |
| contents | Recently, machine learning techniques, particularly deep learning, have demonstrated superior performance over traditional time series forecasting methods across various applications, including both single-variable and multi-variable predictions. This study aims to investigate the capability of i) Next Generation Reservoir Computing (NG-RC) ii) Reservoir Computing (RC) iii) Long short-term Memory (LSTM) for predicting chaotic system behavior, and to compare their performance in terms of accuracy, efficiency, and robustness. These methods are applied to predict time series obtained from four representative chaotic systems including Lorenz, Rössler, Chen, Qi systems. In conclusion, we found that NG-RC is more computationally efficient and offers greater potential for predicting chaotic system behavior. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_05702 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Predicting Chaotic System Behavior using Machine Learning Techniques Rao, Huaiyuan Zhao, Yichen Lai, Qiang Machine Learning Systems and Control Chaotic Dynamics Recently, machine learning techniques, particularly deep learning, have demonstrated superior performance over traditional time series forecasting methods across various applications, including both single-variable and multi-variable predictions. This study aims to investigate the capability of i) Next Generation Reservoir Computing (NG-RC) ii) Reservoir Computing (RC) iii) Long short-term Memory (LSTM) for predicting chaotic system behavior, and to compare their performance in terms of accuracy, efficiency, and robustness. These methods are applied to predict time series obtained from four representative chaotic systems including Lorenz, Rössler, Chen, Qi systems. In conclusion, we found that NG-RC is more computationally efficient and offers greater potential for predicting chaotic system behavior. |
| title | Predicting Chaotic System Behavior using Machine Learning Techniques |
| topic | Machine Learning Systems and Control Chaotic Dynamics |
| url | https://arxiv.org/abs/2408.05702 |