Robust kernel adaptive filters based on mean p-power error for noisy chaotic time series prediction

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• 作者：Wentao Ma^a ; ^{mwt7980@aliyun.com} ; Jiandong Duan^a ; Weishi Man^a ; Haiquan Zhao^b ; Badong Chen^c
• 关键词：Kernel trick ; Mean p-power error criterion ; Noisy chaotic time series prediction ; Impulsive noises
• 刊名：Engineering Applications of Artificial Intelligence
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：58
• 期：Complete
• 页码：101-110
• 全文大小：1289 K
• 卷排序：58

文摘

Two robust kernel adaptive filter (KAF) algorithms, called the kernel least mean p-power (KLMP) and kernel recursive least mean p-power (KRLP), are developed by combining mean p-power error (MPE) criterion and kernel trick for noisy chaotic time series prediction (CTSP). The proposed algorithms employ the MPE to overcome the performance degradation of the CTSP when training data are corrupted by impulsive noises (especially the α-stable noises). First, the KLMP algorithm is proposed by the gradient decent method to improve the robustness of the traditional kernel least mean square (KLMS). Second, the recursion idea and the kernel method are utilized to develop a recursive KAF, namely KRLP, to improve the robustness of the traditional kernel recursive least squares (KRLS). Simulation results show that the proposed algorithms display notable robustness in CTSP when the training data contain different levels of noises, and can perform better in terms of testing MSE than other algorithms.