Research on the Error Compensation System of Automatic Calibration of Instruments Based on Deep Neural Network

In order to improve the calibration accuracy of the instrument and reduce the measurement deviation, this paper proposes a method of using deep neural network to carry out instrument calibration. This project constructs an error compensation model based on multi-layer perceptron (MLP) and convolutional neural network (CNN) to enhance the robustness and accuracy of the calibration system. In order to meet the automatic calibration requirements of multiple instruments, this system plans data preprocessing, network structure optimization and error compensation mechanism. Finally, the proposed method is verified through simulation experiments. The constructed model is trained and tested by combining random sampling and actual measurement. The simulation test shows that under various test conditions, the calibration accuracy of the system can reach 99.3%, and the calibration efficiency is 15% higher than that of conventional calibration methods. Deep neural network can effectively learn instrument error patterns and accurately compensate for various nonlinear errors, which significantly improves the accuracy and reliability of instrument automatic calibration. The research results of this project will provide a new idea and method for achieving automatic calibration of instruments and equipment, and also provide technical support for the development of intelligent instrument calibration systems in the future.