| Micro-resistance are the core components of precision instruments,and the accuracy of micro-resistance values largely determines the performance of precision instruments.However,there is a bottleneck in measuring micro-resistance in the laboratory,so we hope to combine mathematical methods to obtain a high-precision model for micro-resistance measurement.In this paper,we use mathematical methods to further investigate the measurement of micro-resistance,and use statistical modeling software to perform mathematical modeling and numerical analysis for the high-precision measurement of micro-resistance,with the aim of providing a high-precision model for micro-resistance measurement and establishing a mathematical model that can guide actual production.In this paper,a least-squares fitting model is used to obtain the reference error,and finally the model comparison is performed by interpolation,and the data set is trained by the above numerical analysis method.And in order to improve the accuracy and optimize the model,the idea of piece-wise fitting is introduced to the model,and finally the model is found to be better under segmented fitting.In order to improve the generalization ability of the model,some random perturbations are added to the dataset and then fitted,and the comparison of the fitting error with the error before adding random perturbations is observed.Finally,a part of the dataset is selected as the test set to test and optimize the model,expecting to get the fitting absolute error less than 0.003 and relative error less than 0.006.The final empirical evidence indicates that the final resulting model demonstrates higher accuracy and lower complexity of model operation compared to previous work on tiny resistance measurements,with an optimal accuracy of up to 0.001 and a validation test set accuracy of nearly 0.003.This allows for a substantial increase in the likelihood and accuracy of production for practical engineering applications. |
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