Adaptive state augmented clustering-based fuzzy learning control of a passive torque simulator

Saadat, Seyyed Amirhossein; Fateh, Mohammad Mehdi; Keighobadi, Javad

doi:10.1007/s40435-021-00844-x

Cited by 2 publications

(1 citation statement)

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“…where, m l i , σ l i are parameters of Gaussian membership functions that are regulated by the gradient descent method to minimize the MSE [23,24]…”

Section: The Proposed Anfis Methodologymentioning

confidence: 99%

Advanced experimental-based data-driven model for the electromechanical behavior of twisted YBCO tapes considering thermomagnetic constraints

Yazdani-Asrami

Sadeghi

Seyyedbarzegar

et al. 2022

Supercond. Sci. Technol.

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Data-driven models can predict, estimate, and monitor any highly nonlinear and multi-variable behavior of high temperature superconducting (HTS) materials, and superconducting devices to analyze their characteristics with very high accuracy in almost real time, which is a significant figure of merit as compared with traditional numerical approaches. The electromechanical behavior of twisted HTS tapes under different strains, magnetic fields, and temperatures is a complicated problem to be solved using conventional approaches, including finite element-based methods, otherwise, experimental testing is needed to characterize it. This paper aims to offer a data-driven model based on artificial intelligence techniques to predict the electromechanical behavior of HTS tapes operating under various thermomagnetic conditions. By using the proposed model, normalized critical current value and stress of twisted tapes can be predicted under different temperatures and magnetic flux densities. For this purpose, experimental data were used as inputs to design an Adaptive Neuro-Fuzzy Inference System (ANFIS). To gain the best performance of the designed prediction system, multiple clustering methods have been used, such as the grid partitioning method, fuzzy c-means clustering method, and sub-clustering method. Sensitivity analyses were conducted to find the best architecture of ANFIS to predict and model electromechanical behavior of twisted tapes with high accuracy.

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“…where, m l i , σ l i are parameters of Gaussian membership functions that are regulated by the gradient descent method to minimize the MSE [23,24]…”

Section: The Proposed Anfis Methodologymentioning

confidence: 99%