基于相空间重构与鲁棒极限学习机的时延预测

针对网络控制系统(networked control system,NCS)诱导时延具有的时变、随机、非线性等特点,提出了一种相空间重构与鲁棒极限学习机(robust extreme learning machine,RELM)的时延预测算法。首先利用0-1测试对时延序列进行混沌特性检测,再通过改进关联积分法确定重构延迟参数和嵌入维数,进而对时延序列进行重构,新的样本更能真实反映时延变化特性。以重构后的时延序列为训练样本,同时,考虑异常值的稀疏特性,运用RELM进行时延序列预测。该方法具有学习速度快、泛化性能好、可有效降低异常值影响等优点。

Time delay prediction based on phase space reconstruction and robust extreme learning machine

Aiming at the time varying, random and nonlinear characteristics induced delay in the networked control system (NCS), a delay prediction algorithm based on phase space reconstruction and robust extreme learning machine (RELM) is proposed. Firstly, the chaotic property of delay sequence is detected by 0-1 test, and then the reconstruction delay parameters and embedding dimension are determined by an improved correlation integral method, and then the delay sequence is reconstructed. The new samples can more accurately reflect the delay variation features. Using the reconstructed delay sequence as a training sample, and considering the sparse characteristics of outliers, a robust limit learning machine is used to perform delay sequence prediction. The method has the advantages of fast learning, good generalization performance, effectively reducing the impact of outliers.