Data – Driven modelling of the interaction force between permanent magnets

Nguyen, Van Tai; Bermingham, Michael; Dargusch, Matthew

doi:10.1016/j.jmmm.2021.167869

Cited by 4 publications

(1 citation statement)

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“…For several decades, data-driven techniques have been used in modeling and fault detection. For example, a surrogate model was developed based on a data-driven approach, and it can facilitate the design and optimization processes of permanent magnet systems [36]. A Matlab toolbox for data-based fault detection was developed in a unified data-driven framework.…”

Section: Introductionmentioning

confidence: 99%

Identification of the ARX Model with Random Impulse Noise Based on Forgetting Factor Multi-error Information Entropy

Jing

2021

Circuits Syst Signal Process

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Entropy has been widely applied in system identification in the last decade. In this paper, a novel stochastic gradient algorithm based on minimum Shannon entropy is proposed. Though needing less computation than the mean square error algorithm, the traditional stochastic gradient algorithm converges relatively slowly. To make the convergence faster, a multi-error method and a forgetting factor are integrated into the algorithm. The scalar error is replaced by a vector error with stacked errors. Further, a simple step size method is proposed and a forgetting factor is adopted to adjust the step size. The proposed algorithm is utilized to estimate the parameters of an ARX model with random impulse noise. Several numerical solutions and case study indicate that the proposed algorithm can obtain more accurate estimates than the traditional gradient algorithm and has a faster convergence speed.

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Section: Introductionmentioning

confidence: 99%