Online learning compensation control of an electro-hydraulic shaking table using Echo State Networks

Liang, Jia; Ding, Zhen; Han, Qinghua; Wu, Hao; Ji, Jinbao

doi:10.1016/j.engappai.2023.106274

Cited by 4 publications

(2 citation statements)

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“…Continuous Time Echo State Networks are a variant of ESNs that model time as a continuous rather than discrete quantity. The model equation for a CTESN is given by: ṙ = σ(W in x + Wr), (5) with all variables having the same definition as in the previous section. The projection equation reads:…”

Section: Continuous Time Echo State Network (Ctesns)mentioning

confidence: 99%

“…Recently, Echo State Networks (ESNs) have seen an increase in popularity for modeling highly nonlinear and chaotic phenomena in domains such as optimal control planning [5], chaotic time series prediction [6,7], signals analysis [8] and even turbulent fluid flow [9]. These applications leverage the ability of ESNs to capture highly nonlinear transient behavior accurately, as well as the extremely low cost of training them, with the empirical success of ESNs on a wide range of approximation tasks discussed and explained in several works [10,11].…”

Section: Introductionmentioning

confidence: 99%

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Investigating the Surrogate Modeling Capabilities of Continuous Time Echo State Networks

Bhatnagar

2024

MCA

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Continuous Time Echo State Networks (CTESNs) are a promising yet under-explored surrogate modeling technique for dynamical systems, particularly those governed by stiff Ordinary Differential Equations (ODEs). A key determinant of the generalization accuracy of a CTESN surrogate is the method of projecting the reservoir state to the output. This paper shows that of the two common projection methods (linear and nonlinear), the surrogates developed via the nonlinear projection consistently outperform those developed via the linear method. CTESN surrogates are developed for several challenging benchmark cases governed by stiff ODEs, and for each case, the performance of the linear and nonlinear projections is compared. The results of this paper demonstrate the applicability of CTESNs to a variety of problems while serving as a reference for important algorithmic and hyper-parameter choices for CTESNs.

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Section: Continuous Time Echo State Network (Ctesns)mentioning

confidence: 99%