A Cuckoo Search-Support Vector Machine Model for Predicting Dynamic Measurement Errors of Sensors

Sensors play a very important role in the Internet of Things. Error correction is of great significance to achieve sensor precision. Currently, accurately predicting the future dynamic measurement error is an effective way to improve sensor precision. Aiming to solve the problem of low model accuracy in traditional dynamic measurement error prediction, this paper employs the support vector machine (SVM) to predict the dynamic measurement error of sensors. However, the performance of the SVM depends on setting the appropriate parameters. Hence, the cuckoo search (CS) algorithm is adopted to optimize the key parameters to avoid the local minimum value which can occurs when using the traditional method of parameter optimization. To validate the predictive performance of the proposed CS-SVM model, the dynamic measurement error data for two sensors are applied to establish a predictive model. The root mean squared error and the mean absolute percentage error are employed to evaluate the models' performances. These results are also compared with those obtained from the SVM optimized by a grid search and the particle swarm optimization method. The experiments show that the SVM model based on the CS algorithm achieves more accurate prediction and is more effective in predicting dynamic measurement errors for sensors than the previous models.