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• W2897482021 abstract "In this brief, the steady-state tracking performance of minimum kernel risk-sensitive loss in a non-stationary environment is analyzed. In order to model a non-stationary environment, a first-order random-walk model is used to describe the variations of optimum weight vector over time. Moreover, the measurement noise is considered to have non-Gaussian distribution. The energy conservation relation is utilized to extract an approximate closed-form expression for the steady-state excess mean square error (EMSE). Our analysis shows that unlike for the stationary case, the EMSE curve is not an increasing function of step-size parameter. Hence, the optimum step-size which minimizes the EMSE is derived. We also discuss that our approach can be used to extract steady-state EMSE for a general class of adaptive filters. The simulation results with different noise distributions support the theoretical derivations." @default.
• W2897482021 created "2018-10-26" @default.
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• W2897482021 date "2019-07-01" @default.
• W2897482021 modified "2023-09-23" @default.
• W2897482021 title "Tracking Analysis of Minimum Kernel Risk-Sensitive Loss Algorithm Under General Non-Gaussian Noise" @default.
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• W2897482021 doi "https://doi.org/10.1109/tcsii.2018.2874969" @default.
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