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Sensors 2018, 18(1), 233;

A Sensor Dynamic Measurement Error Prediction Model Based on NAPSO-SVM

College of Mathematics, Physics and Information Engineering, Zhejiang Normal University, Jinhua 321004, China
School of Astronautics and Aeronautic, University of Electronic Science and Technology of China, Chengdu 611731, China
Department of Electrical, Computer, Software, and Systems Engineering, Embry-Riddle Aeronautical University, Daytona Beach, FL 32114, USA
Authors to whom correspondence should be addressed.
Received: 16 November 2017 / Revised: 15 December 2017 / Accepted: 11 January 2018 / Published: 15 January 2018
(This article belongs to the Special Issue Smart Communication Protocols and Algorithms for Sensor Networks)
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Dynamic measurement error correction is an effective way to improve sensor precision. Dynamic measurement error prediction is an important part of error correction, and support vector machine (SVM) is often used for predicting the dynamic measurement errors of sensors. Traditionally, the SVM parameters were always set manually, which cannot ensure the model’s performance. In this paper, a SVM method based on an improved particle swarm optimization (NAPSO) is proposed to predict the dynamic measurement errors of sensors. Natural selection and simulated annealing are added in the PSO to raise the ability to avoid local optima. To verify the performance of NAPSO-SVM, three types of algorithms are selected to optimize the SVM’s parameters: 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 absolute percentage error are employed to evaluate the prediction models’ performances. The experimental results show that among the three tested algorithms the NAPSO-SVM method has a better prediction precision and a less prediction errors, and it is an effective method for predicting the dynamic measurement errors of sensors. View Full-Text
Keywords: sensors; dynamic measurement errors; prediction; improved PSO; support vector machine sensors; dynamic measurement errors; prediction; improved PSO; support vector machine

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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.

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