Preprint Article Version 1 This version is not peer-reviewed

Dynamic Measurement Errors Prediction Model of Sensors Based on NAPSO-SVM

Version 1 : Received: 20 November 2017 / Approved: 20 November 2017 / Online: 20 November 2017 (16:56:20 CET)

A peer-reviewed article of this Preprint also exists.

Jiang, M.; Jiang, L.; Jiang, D.; Li, F.; Song, H. A Sensor Dynamic Measurement Error Prediction Model Based on NAPSO-SVM. Sensors 2018, 18, 233. Jiang, M.; Jiang, L.; Jiang, D.; Li, F.; Song, H. A Sensor Dynamic Measurement Error Prediction Model Based on NAPSO-SVM. Sensors 2018, 18, 233.

Journal reference: Sensors 2018, 18, 233
DOI: 10.3390/s18010233

Abstract

Dynamic measurement error correction is an effective method to improve the sensor precision. Dynamic measurement error prediction is an important part of error correction, support vector machine (SVM) is often used to predicting the dynamic measurement error of sensors. Traditionally, the parameters of SVM were always set by manual, which can not ensure the model’s performance. In this paper, a method of SVM based on an improved particle swarm optimization (NAPSO) is proposed to predict the dynamic measurement error of sensors. Natural selection and Simulated annealing are added in PSO to raise the ability to avoid local optimum. To verify the performance of NAPSO-SVM, three types of algorithms are selected to optimize the SVM’s parameters, they are the particle swarm optimization algorithm (PSO), the improved PSO optimization algorithm (NAPSO), and the glowworm swarm optimization (GSO). The dynamic measurement error data of two sensors are applied as the test data. The root mean squared error and mean absoluter percentage error are employed to evaluate the prediction models’ performances. The experiment results show that the NAPSO-SVM has a better prediction precision and a less prediction errors among the three algorithms, and it is an effective method in predicting dynamic measurement errors of sensors.

Subject Areas

Sensors; Dynamic measurement errors; Prediction; Improved PSO; Support Vector Machine

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