基于改进最小二乘支持向量回归机的链路质量预测

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.

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

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.