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Communication Dans Un Congrès Année : 2021

Novel Reservoir Computing Approach for the Detection of Chaos

Ali Rida
Slavisa Jovanovic
S. Petit-Watelot
Hassan Rabah

Résumé

Reservoir Computing (RC) is a novel framework for data computation extending from recurrent neural networks. The high dimensionality and flexibility of such processing paradigm make it well worthy for high complex systems analysis. Conceptor-driven Network (ConDN) is a RC-based paradigm whose unique structure is well appropriate for modelling and analysing multiple-input systems. In this paper, we present a ConDN approach for chaos detection in systems. Some known parametrizable systems exhibiting chaotic and non-chaotic behaviours have been analysed. By only observing the selected output results (trace matrix) after training of the ConDN method, the systems can be classified as chaotic or non-chaotic. In addition, the robustness of this method against white noise is also investigated.
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Dates et versions

hal-03981875 , version 1 (10-02-2023)

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Citer

Ali Rida, Slavisa Jovanovic, S. Petit-Watelot, Hassan Rabah. Novel Reservoir Computing Approach for the Detection of Chaos. 2021 IEEE International Symposium on Circuits and Systems (ISCAS), May 2021, Daegu, South Korea. pp.1-5, ⟨10.1109/ISCAS51556.2021.9401735⟩. ⟨hal-03981875⟩
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