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pro vyhledávání: '"Ortega,Juan"'
Next-generation reservoir computing (NG-RC) has attracted much attention due to its excellent performance in spatio-temporal forecasting of complex systems and its ease of implementation. This paper shows that NG-RC can be encoded as a kernel ridge r
Externí odkaz:
http://arxiv.org/abs/2412.09800
Quantum reservoir computing is an emergent field in which quantum dynamical systems are exploited for temporal information processing. In previous work, it was found a feature that makes a quantum reservoir valuable: contractive dynamics of the quant
Externí odkaz:
http://arxiv.org/abs/2412.08322
Vehicles are sophisticated machines equipped with sensors that provide real-time data for onboard driving assistance systems. Due to the wide variety of traffic, road, and weather conditions, continuous system enhancements are essential. Connectivity
Externí odkaz:
http://arxiv.org/abs/2410.21934
Several topological and analytical notions of continuity and fading memory for causal and time-invariant filters are introduced, and the relations between them are analysed. A significant generalization of the convolution theorem that establishes the
Externí odkaz:
http://arxiv.org/abs/2408.07386
An iterated multistep forecasting scheme based on recurrent neural networks (RNN) is proposed for the time series generated by causal chains with infinite memory. This forecasting strategy contains, as a particular case, the iterative prediction stra
Externí odkaz:
http://arxiv.org/abs/2405.02536
A probabilistic framework to study the dependence structure induced by deterministic discrete-time state-space systems between input and output processes is introduced. General sufficient conditions are formulated under which output processes exist a
Externí odkaz:
http://arxiv.org/abs/2404.08717
Autor:
Grigoryeva, Lyudmila, Hamzi, Boumediene, Kemeth, Felix P., Kevrekidis, Yannis, Manjunath, G, Ortega, Juan-Pablo, Steynberg, Matthys J.
Using short histories of observations from a dynamical system, a workflow for the post-training initialization of reservoir computing systems is described. This strategy is called cold-starting, and it is based on a map called the starting map, which
Externí odkaz:
http://arxiv.org/abs/2403.10325
A structure-preserving kernel ridge regression method is presented that allows the recovery of potentially high-dimensional and nonlinear Hamiltonian functions out of datasets made of noisy observations of Hamiltonian vector fields. The method propos
Externí odkaz:
http://arxiv.org/abs/2403.10070
Publikováno v:
Foro Internacional, 2024 Jul 01. 643 (257), 573-610.
Externí odkaz:
https://www.jstor.org/stable/27333532
