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

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.