基于改进最小二乘支持向量回归机的链路质量预测

doi:10.13190/j.jbupt.2017-024

北京邮电大学学报 ›› 2018, Vol. 41 ›› Issue (2): 44-49.doi: 10.13190/j.jbupt.2017-024

基于改进最小二乘支持向量回归机的链路质量预测

舒坚, 贾晨浩, 陶娟

南昌航空大学软件学院, 南昌 330063

收稿日期:2017-07-21 出版日期:2018-04-28 发布日期:2018-03-17
作者简介:舒坚(1964-),男,教授,E-mail:shujian@nchu.edu.cn.
基金资助:
国家自然科学基金项目（61762065，61363015，61501218）；江西省自然科学基金资助重点项目（20171BAB202009，20171ACB20018）；江西省研究生创新专项资金项目（YC2016-S356）

Link Quality Prediction for Sensor Network Based on Improved LS-SVR

SHU Jian, JIA Chen-hao, TAO Juan

School of Software, Nanchang Hangkong University, Nanchang 330063, China

Received:2017-07-21 Online:2018-04-28 Published:2018-03-17

摘要/Abstract

摘要： 为了准确地预测链路质量，提出基于改进最小二乘支持向量回归机的无线传感器网络链路质量预测模型.采用粗糙集理论约简链路质量参数，以提取出有效反映链路质量的特征参数；利用遗传算法优化最小二乘支持向量回归机的惩罚因子和核函数宽度.实验结果表明，与Experts Advice预测模型相比，提出的预测模型具有更高的精度.

关键词: 无线传感器网络, 链路质量预测, 遗传算法, 最小二乘支持向量回归机

Abstract: In order to predict the link quality accurately, a link quality prediction model was proposed to predict link quality for sensor networks based on improved least square support vector regression machine (LS-SVR). The rough set (RS) was introduced to reduce the link quality metrics so as to extract the effective characteristic metrics of the link quality. And the genetic algorithm (GA) was employed in LS-SVR to optimize the penalty factor and kernel width. Experiments show that compared with the experts advice-based prediction model, the proposed prediction model achieves better accuracy.

Key words: wireless sensor networks, link quality prediction, genetic algorithm, least squares support vector regression machine

中图分类号:

TN929.53

舒坚, 贾晨浩, 陶娟. 基于改进最小二乘支持向量回归机的链路质量预测[J]. 北京邮电大学学报, 2018, 41(2): 44-49.

SHU Jian, JIA Chen-hao, TAO Juan. Link Quality Prediction for Sensor Network Based on Improved LS-SVR[J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2018, 41(2): 44-49.

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基于改进最小二乘支持向量回归机的链路质量预测

Link Quality Prediction for Sensor Network Based on Improved LS-SVR

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