Abstract:Reservoir observers provide a data-driven approach to the inference of unmeasured variables from observed ones for nonlinear dynamical systems. While previous studies have demonstrated wide applicability, their performance may vary considerably with different input variables, even compromising reliability in the worst cases. To enhance the performance of inference, we integrate residual calibration and attention mechanism into the reservoir observer design. The residual calibration module leverages information from the estimation residuals to refine the observer output, and the attention mechanism exploits the temporal dependencies of the data to enrich the representation of reservoir internal dynamics. Experiments on typical chaotic systems demonstrate that our method substantially improves inference accuracy, especially for the worst cases resulting from the traditional reservoir observers. We also invoke the notion of transfer entropy to explain the reason for the input-dependent observation discrepancy and the effectiveness of the proposed method.
| Subjects: | Machine Learning (cs.LG); Chaotic Dynamics (nlin.CD) |
| Cite as: | arXiv:2604.08592 [cs.LG] |
| (or arXiv:2604.08592v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2604.08592 arXiv-issued DOI via DataCite |
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| Journal reference: | Physical Review E 2026 |
| Related DOI: | https://doi.org/10.1103/jjcf-w1st
DOI(s) linking to related resources |
From: Yichen Liu [view email]
[v1]
Wed, 1 Apr 2026 07:49:36 UTC (8,774 KB)
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