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Robust Multitask Diffusion Affine Projection Algorithm for Distributed Estimation
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DOI：10.1109/TCSII.2021.3103868.png)
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Abstract
En 中文
When the disturbance of impulsive noise exists in the multitask network, the convergence behavior of the traditional multitask diffusion affine projection (AP) algorithm (MD-APA) is significantly suppressed. To alleviate this problem, in this brief, a robust MD-APA is proposed based on maximum correntropy criterion (MCC), which is called MD-APMCC algorithm. Due to the shortcomings of the fixed kernel width, this brief adopts a robust adaptive kernel width strategy to increase the estimation behavior of the MD-APMCC algorithm. Besides, the convergence behavior of MD-APMCC algorithm is studied to derive the convergence range of step-size and the theoretical steady-state mean square deviation (MSD) of the whole network. The simulation verification demonstrates that the proposed MD-APMCC algorithm appears better estimation behavior than MD-APA and MD-APSA for multitask distributed estimation under impulsive noise interference, and the theoretical steady-state MSD of MD-APMCC algorithm is obtained through mean square analysis, which has been well verified by several simulations.
Keywords:
Kernel
Estimation
Steady-state
Artificial neural networks
Convergence
Clustering algorithms
Interference
Multitask network
impulsive noise
affine projection (AP)
maximum correntropy criterion (MCC)
adaptive kernel width
steady-state mean square deviation (MSD)
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