Dynamic measurement errors prediction for sensors based on firefly algorithm optimize support vector machine

Abstract

The accuracy of dynamic measurement error predicting has the great significant influence on the precision and stability of the sensors. In order to solve the problem of the low accuracy of the model in traditional dynamic measurement error prediction, support vector machine (SVM) is applied to predicting the dynamic measurement error of sensors. However, in prediction tasks, a proper set of design parameters are essential for the performance and efficiency of SVM model. Hence, parameters selection for SVM by firefly algorithm (FA) is proposed in this study to avoid the local minimum value which occurs in the traditional method of parameter optimization. Two sensors of dynamic measurement error data are considered for modeling. Root mean squared error (RMSE) and mean absolute percentage error (MAPE) are employed to evaluate the performances of the models. These results are also compared with the results obtained from the grid search SVM (GS-SVM)and the particle swarm optimization SVM (PSO-SVM). Experimental results show that the model of SVM based on FA algorithm predicts more accurately and effectively on dynamic measurement errors prediction for sensors than the other models.