@article{NSCE05500,
  title={Predicting chaotic system behavior using machine learning techniques},
  author={Rao, Huaiyuan and Zhao, Yichen and Chen, Hsuan-Pin},
  journal={Nonlinear Science and Control Engineering},
  issn={TBA},
  volume={1},
  number={1},
  abstract={Recently, the superior performance of machine learning approaches over classical forecasting models for complex time series analysis has been demonstrated in various domains. However, predicting chaotic time series continues to be a major challenge due to their inherent complexity. This paper investigates the accuracy, efficiency, and robustness of three recurrent neural network architectures: (i) next generation reservoir computing, (ii) reservoir computing, (iii) long short-term memory for the task of chaotic systems prediction. Four canonical chaotic systems, namely the Lorenz, Rössler, Chen, and Qi systems, are used for comparing these three methods. Numerical results demonstrate that the next generation reservoir computing is more computationally efficient and offers greater potential for predicting long-term chaotic time series.},
  pages={025290003},
  doi={https://doi.org/10.36922/NSCE025290003},
  url={https://accscience.com/journal/NSCE/1/1/10.36922/NSCE025290003},
  year={2025}
}