Feature-Based Echo-State Networks: A Step Towards Interpretability and Minimalism in Reservoir Computer
<|reference_start|>Feature-Based Echo-State Networks: A Step Towards Interpretability and Minimalism in Reservoir Computer: This paper proposes a novel and interpretable recurrent neural-network structure using the echo-state network (ESN) paradigm for time-series prediction. While the traditional ESNs perform well for dynamical systems prediction, it needs a large dynamic reservoir with increased computational complexity. It also lacks interpretability to discern contributions from different input combinations to the output. Here, a systematic reservoir architecture is developed using smaller parallel reservoirs driven by different input combinations, known as features, and then they are nonlinearly combined to produce the output. The resultant feature-based ESN (Feat-ESN) outperforms the traditional single-reservoir ESN with less reservoir nodes. The predictive capability of the proposed architecture is demonstrated on three systems: two synthetic datasets from chaotic dynamical systems and a set of real-time traffic data.<|reference_end|>
@article{goswami2024feature-based,
    title={Feature-Based Echo-State Networks: A Step Towards Interpretability and
  Minimalism in Reservoir Computer},
    author={Debdipta Goswami},
    journal={arXiv preprint arXiv:2403.19806},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.19806},
    primaryClass={cs.LG cs.SY eess.SY}
}