基于PSO优化LSSVR的三维WSN节点定位方法简

提出了一种基于粒子群优化最小二乘支持向量回归机的三维无线传感器网络节点定位方法。该方法首先运用最小二乘支持向量回归机构建三维节点定位模型，再利用粒子群优化算法对最小二乘支持向量回归机核函数参数和规则化参数寻优。然后，根据若干虚拟节点定位的预测位置与实际位置的均方差构造粒子群算法适应度函数，通过有限次建模参数迭代寻优获得最小二乘支持向量回归机全局最优参数。最后，返回回归模型中进行定位计算，实现节点定位。仿真结果表明，所提出的方法与最小二乘和最小二乘支持向量回归机定位方法相比，可以提高节点定位精度。

Three-dimensional WSN node localization method based on LSSVR optimized by PSO algorithm

A localization method based on LSSVR optimized by particle swarm optimization （PSO）algorithm is proposed in this paper.Firstly,the three-dimensional（3D）node localization model is built through least squares support vector regression （LSS-VR）and the kernel function parameters and the regularization parameters are optimized by PSO algorithm.Then,the fitness function of the particle swarm optimization algorithm is constructed according to the mean square error of a number of virtual nodes from the predicted position and their actual position,and the global optimal parameters of LSSVR is acquired through limit-ed modeling parameters iterative searching method.Finally,the LSSVR optimized by PSO algorithm is used to realize the node localization.The simulation results show that the localization accuracy of the proposed algorithm is superior to that of least square （LS）and LSSVR methods.