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Electrical Engineering and Systems Science > Systems and Control

arXiv:2108.08001 (eess)
[Submitted on 18 Aug 2021]

Title:Nonlinear Autoregression with Convergent Dynamics on Novel Computational Platforms

Authors:J. Chen, H. I. Nurdin
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Abstract:Nonlinear stochastic modeling is useful for describing complex engineering systems. Meanwhile, neuromorphic (brain-inspired) computing paradigms are developing to tackle tasks that are challenging and resource intensive on digital computers. An emerging scheme is reservoir computing which exploits nonlinear dynamical systems for temporal information processing. This paper introduces reservoir computers with output feedback as stationary and ergodic infinite-order nonlinear autoregressive models. We highlight the versatility of this approach by employing classical and quantum reservoir computers to model synthetic and real data sets, further exploring their potential for control applications.
Comments: 7 pages (double column), 5 figures. Submitted for publication, comments welcome
Subjects: Systems and Control (eess.SY); Quantum Physics (quant-ph); Machine Learning (stat.ML)
Cite as: arXiv:2108.08001 [eess.SY]
  (or arXiv:2108.08001v1 [eess.SY] for this version)
  https://doi.org/10.48550/arXiv.2108.08001

Submission history

From: Hendra Nurdin [view email]
[v1] Wed, 18 Aug 2021 07:01:16 UTC (3,489 KB)
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