基于奇异谱分析与LSSVM算法的列车 无线网络控制时延预测方法

无线网络控制是推进高速列车智能化的有利因素，无线网络时延作为一种典型的时间序列，存在随机性强、波动性大等问题导致预测难度大，针对这些问题提出了一种奇异谱分析-改进粒子群优化LSSVM的无线网络时延预测模型。首先将获取到的时延序列通过Cao方法确定窗口长度，再将时延序列通过奇异谱分析得到一系列子序列，将各子序列采用混沌粒子群优化后的LSSVM模型进行预测，最后将所有子序列预测值进行叠加得到最终预测结果，仿真结果表明，该模型MAPE、MSE及MAE相比小波分解模型分别降低了2.8%、1.055、0.44;相比EMD分解模型分别降低了7.4%、3.377、1.118;相比CEEMD分解模型分别降低了6.2%、2.568、0.974,精度明显高于其他模型。

Prediction method of train wireless network control delay based on singular spectrum analysis and LSSVM algorithm

Wireless network control is a favorable factor to promote the intelligence of high-speed trains. As a typical time series, wireless network delay has strong randomness, large volatility and other problems leading to difficult prediction. In view of these problems, a wireless network delay prediction model with singular spectrum analysis-improved particle swarm optimization and LSSVM is proposed. The length of the window was first determined by the Cao method, the delay sequences were analyzed by singular spectral analysis to obtain a series of subsequences. Each subsequence was predicted using the LSSVM model optimized for the chaotic particle swarm. Finally, all the subsequence predicted values were superimposed to obtain the final prediction results, the simulation results show that the average absolute percentage error (MAPE), mean squared error (MSE) and average absolute error (MAE) are 2.8%, 1.055 and 0.44 lower respectively compared with the wavelet decomposition model. Compared with the EMD decomposition model, 7.4%, 3.377 and 1.118 decreased, respectively. Compared with the CEEMD decomposition model, it was reduced by 6.2%, 2.568, and 0.974, respectively. The accuracy was significantly higher than that in the other models.