WSN 中利用 XGBoost 和加权自适应 HFLMS 的 数据约减组合预测方法.

Aiming at the limitation of various resources such as energy, bandwidth and memory in a wireless sensor network ( WSN), this paper proposed a data reduction combination prediction method based on XGBoost and hierarchical fractional least-mean-square( HFLMS) Firstly, it used the XGBoost method to perform a second-order Taylor expansion of the loss function, which balanced the complexity of the model and the decline rate of the loss function, and achieved the stable prediction of the resource limit. Then, it employed the proposed HFLMS for data reduction in WSN, and used error estimation to predict the measured data, which would reduce the energy constraints in WSN. Finally, it used the two evaluation parameters ( energy and prediction error) to evaluate the performance of the proposed prediction method. The experimental results demonstrate that the proposed prediction method is better than that without prediction, the approximate steepest descent algorithm and the layered minimum mean square filtering technology. [ABSTRACT FROM AUTHOR]