In this study a novel method was presented to parameterize the critical current of Yttrium Barium Copper Oxide(YBCO)tapes based on their width,thickness,magnetothermal operational conditions,and the applied strain.For...In this study a novel method was presented to parameterize the critical current of Yttrium Barium Copper Oxide(YBCO)tapes based on their width,thickness,magnetothermal operational conditions,and the applied strain.For this purpose,a fuzzy‐logic‐based model was developed that take tapes structures and their operational conditions as inputs to calculate their critical current,as output.The results of critical current parame-terization by fuzzy‐logic‐based model showed that the relative error of the proposed model is less than 3%comparing to experimentally acquired data.Then,the results of presented model was compared to results of semi‐analytical fitting‐based models and fully‐analytical fitting based models.The comparisons showed the better performance in terms of accuracy and error of fuzzy logic model over fitting‐based methods.At last,the results were also compared with the Artificial Neural Network(ANN)‐based parameterization model and Adaptive Nero‐Fuzzy Interference System(ANFIS)‐based parameterization model.The proposed method had 6%to 8%higher accuracy and about 47%to 54%lower root mean squared error.展开更多
文摘In this study a novel method was presented to parameterize the critical current of Yttrium Barium Copper Oxide(YBCO)tapes based on their width,thickness,magnetothermal operational conditions,and the applied strain.For this purpose,a fuzzy‐logic‐based model was developed that take tapes structures and their operational conditions as inputs to calculate their critical current,as output.The results of critical current parame-terization by fuzzy‐logic‐based model showed that the relative error of the proposed model is less than 3%comparing to experimentally acquired data.Then,the results of presented model was compared to results of semi‐analytical fitting‐based models and fully‐analytical fitting based models.The comparisons showed the better performance in terms of accuracy and error of fuzzy logic model over fitting‐based methods.At last,the results were also compared with the Artificial Neural Network(ANN)‐based parameterization model and Adaptive Nero‐Fuzzy Interference System(ANFIS)‐based parameterization model.The proposed method had 6%to 8%higher accuracy and about 47%to 54%lower root mean squared error.
