基于KPCA与LSSVM的网络控制系统时延预测方法

针对网络控制系统中随机时延很难精确预测的问题，首次将核主成分分析（kernel principal component analysis, KPCA）与最小二乘支持向量机（least squares support vector machine, LSSVM）结合对随机时延进行预测，KPCA对输入随机时延序列降维，消除重复性与噪声，减少LSSVM的运算量，降维后的时延序列通过LSSVM算法预测时延值。仿真结果表明，基于KPCA与LSSVM的时延预测方法的预测精度高于其他的预测方法。

Networked control system time delay prediction method based on KPCA and LSSVM

The random delay of networked control system is difficult to predict accurately. Firstly the kernel principal component analysis (KPCA) and the least squares support vector machine (LSSVM) algorithm are combined to predict the random time delay. The KPCA can reduce dimensionality of input random time delay sequence, eliminate the noise and interference and reduce the amount of computation of the LSSVM algorithm. The time delay is predicted by the LSSVM algorithm through the time delay sequence after dimensionality reduction. The simulation results show that the prediction accuracy of the time delay prediction based on the KPCA and LSSVM algorithm is higher than the other prediction methods.