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A Novel Quaternion Kernel LMS Algorithm With Variable Kernel Width
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DOI：10.1109/TCSII.2021.3056452.png)
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Abstract
En 中文
The quaternion Gaussian kernel is usually used when solving quaternion nonlinear problems. However, how to choose a proper value of kernel width is still an important issue. In most previous studies, the kernel width was set manually or estimated in advance by using Silvermans rule based on the sample distribution, which can easily degrade the performance of algorithms. In this brief, the variable kernel width quaternion kernel least mean squares algorithm (VKW-QKLMS) aims to develop an online technique for optimizing the kernel width of the quaternion kernel LMS (QKLMS) algorithm, in which the filter weight and the kernel width are alternately updated by using stochastic gradient algorithm. Simulation results show that the performance of the VKW-QKLMS algorithm does not depend on the selection of initial value of the kernel width. The VKW-QKLMS algorithm can usually achieve better performances than other competing algorithms. Only when the kernel width in the QKLMS algorithm is empirically selected as a proper value, the QKLMS algorithm can achieve much the same performance as that of the VKW-QKLMS algorithm.
Keywords:
Kernel
Quaternions
Circuits and systems
Adaptive algorithms
Mean square error methods
Inference algorithms
Interference
Least mean square
mean square deviation
adaptive filtering
kernel trick
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