基于渔夫捕鱼-最小二乘支持向量机的传感器节点定位的研究  

Research on Three-dimensional Sensor Node Localization Based on SFOA-LSSVM

作　　者：谢洪[1] 王大溪[2] 

机构地区：[1]柳州铁道职业技术学院,广西柳州545616 [2]广西科技大学,广西柳州545006

出　　处：《科技通报》2016年第10期160-163,共4页Bulletin of Science and Technology

基　　金：国家十二五科技支撑计划(2012BAF12B18)

摘　　要：为了提高无线传感器的定位精度,针对最小二乘支持向量机参数优化问题,提出了一种基于渔夫捕鱼优化-最小二乘支持向量机的传感器点定位方法。首先构建三维无线传感器定位模型的学习样本,然后采用最小二乘支持向量机构建三维节点定位模型,并采用渔夫捕鱼的行为来找到最优支持向量机参数。结果表明,本文算法能够有效地提高传感器节点的定位精度,降低定位误差,具有一定的实际应用价值。In order to improve the positioning accuracy of wireless sensor, aiming at the optimization of the minimum supporting vector parameter, this paper proposes a sensor positioning method based on Fisher Fishing Optimization — the least square support vector machine(LSSVM). Firstly, it establishes learning sample of 3D wireless sensor positioning model, and then adopts the LSSVM to establish the 3D joint positioning model as well as the fisher fishing behaviors to find the optimal support vector parameter. The results indicate that algorithm in this paper can effectively improve the positioning accuracy of sensor nodes and reduces the positioning error. Thus, it has some practical application value.

关 键 词：无线传感器网络 三维节点定位 最小二乘支持向量机 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]