具有最大相关系数准则的核最小均方

2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2022-11-26 DOI:10.1109/ccis57298.2022.10016417

Yawen Li, Wenling Li, Zhe Xue, Ang Li

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引用次数: 0

摘要

我们为非线性输入输出模型引入了一种新颖的核最小均方(KLMS)算法，其中输出是相对于多个输入以耦合方式生成的。为了保证鲁棒性，在最大熵准则下提出了KLMS算法。进行了均方收敛，建立了能量守恒关系，反映了耦合参数的影响。为了保证KLMS算法的收敛性，给出了与数据无关的步长上界。仿真结果证明了该方法的优良性能。

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Kernel Least Mean Square With Maximum Correntropy Criterion

We introduce a novel kernel least mean square (KLMS) algorithm for nonlinear input-output models, where the output is generated with respect to multiple inputs in a coupled fashion. The KLMS algorithm is proposed under maximum correntropy criterion for robustness. The mean square convergence has been carried out and the energy conservation relation is also established, which reflect the effects of the coupling parameter. A data-independent upper bound on the stepsize is derived to guarantee the convergence of the KLMS algorithm. Simulation results are provided to demonstrate the excellent performance.

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来源期刊

2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS)

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