Research on the Prediction Method of Clock Tester Calibration Data Based on Radial Basis Function Neural Network

Abstract

A radial basis function (RBF) neural network-based calibration data prediction model for clock testers is proposed to address the issues of fixed calibration cycles, low efficiency, and waste of electrical energy. This provides a new method for clock tester traceability calibration. First, analyze the mechanism of clock tester calibration parameters and the influencing factors of prediction targets. Based on the learning rules of an RBF neural network, determine the data types of training and testing sets. Second, normalize the training and testing data to avoid the adverse effects of data characteristics and distribution differences on the prediction model. Finally, based on different prediction objectives, time-driven and data-driven calibration data prediction models are constructed using RBF neural networks. Through simulation analysis, it is shown that an RBF neural network is superior to a BP neural network in predicting clock tester calibration data, and time-driven prediction accuracy is superior to data-driven prediction accuracy. Moreover, the prediction error and mean square error of both prediction models are on the order of 10−9, meeting the prediction accuracy requirements.