一种基于自适应KLMS的卫星网络流量预测算法

针对传统预测模型已不再适用于卫星网络的问题，提出了一种自适应步长和自适应核宽度的核最小均方算法（AKLMS）.通过核函数将非线性数据从低维输入空间映射到高维特征空间进行操作，并且在迭代过程中根据瞬时误差自适应地调整步长和核宽度.仿真结果证明，与核最小均方算法（KLMS）和最小均方算法（LMS）相比，AKLMS算法在收敛速度和预测流量精度方面都有大幅提升，为卫星网络的流量规划和路由设计提供了强有力的决策支持.

An Adaptive KLMS Traffic Prediction Algorithm for Satellite Network

Due to the resource limitation and topology change in satellite network, the article puts forward higher requirements for the accuracy and efficiency of the network traffic prediction algorithm, and the traditional prediction model is no longer suitable for the satellite network. The author presents a kernel least mean square algorithm (KLMS) with adaptive step length and adaptive kernel width, namely AKLMS, which maps the nonlinear data from low dimensional input space to high dimensional feature space through kernel function, and the algorithm will adaptively adjust the step length and kernel width based on the instantaneous error in the iterative process. Simulations show that the AKLMS algorithm has great improvement on the convergence speed and prediction accuracy of the flow compared with the KLMS and least mean square (LMS), which will provide strong decision support for traffic planning and routing design in satellite network.