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Tracking Analysis of Gaussian Kernel Signed Error Algorithm for Time-Variant Nonlinear Systems
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DOI：10.1109/TCSII.2019.2957781.png)
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Abstract
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This brief establishes a novel kernel-based model with a random walk variation of the optimum weight coefficients to characterize the time-variant nonlinear system. Then, the steady-state tracking performance of the kernel signed error algorithm (KSEA) with Gaussian kernel is analyzed for the proposed time-variant nonlinear system in the presence of non-Gaussian impulsive noise. The theoretical findings enable us to determine the optimal step-size that minimizes the steady-state excess mean-square error under this non-stationary environment. Simulation results illustrate the usefulness and accuracy of the derived analytical models for characterizing the steady-state tracking behavior of Gaussian KSEA.
Keywords:
Kernel
Nonlinear systems
Steady-state
Adaptation models
Estimation error
Analytical models
Circuits and systems
Kernel signed error algorithm
tracking analysis
time-variant nonlinear system
non-Gaussian impulsive noise
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