ArticleChaos (Woodbury, N.Y.)2020
Assessing observability of chaotic systems using Delay Differential Analysis.
Article in Chaos (Woodbury, N.Y.), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- An open-access EEG dataset for speech decoding: Exploring the role of articulation and coarticulation.Scientific data · 2025Article
- Predicting chromosomal compartments directly from the nucleotide sequence with DNA-DDA.Briefings in bioinformatics · 2023Article
- Dynamical ergodicity DDA reveals causal structure in time series.Chaos (Woodbury, N.Y.) · 2021Article
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Authors and funding
4 authors.
Funding
Abstract
Observability can determine which recorded variables of a given system are optimal for discriminating its different states. Quantifying observability requires knowledge of the equations governing the dynamics. These equations are often unknown when experimental data are considered. Consequently, we propose an approach for numerically assessing observability using Delay Differential Analysis (DDA). Given a time series, DDA uses a delay differential equation for approximating the measured data. The lower the least squares error between the predicted and recorded data, the higher the observability. We thus rank the variables of several chaotic systems according to their corresponding least square error to assess observability. The performance of our approach is evaluated by comparison with the ranking provided by the symbolic observability coefficients as well as with two other data-based approaches using reservoir computing and singular value decomposition of the reconstructed space. We investigate the robustness of our approach against noise contamination.
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