Distributed adaptive algorithm based on the asymmetric cost of error functions

TL;DR

This paper introduces three novel diffusion adaptive algorithms based on asymmetric error cost functions, demonstrating improved robustness and performance in noisy environments through theoretical analysis and simulations.

Contribution

It proposes a new family of diffusion algorithms incorporating asymmetric cost functions, with theoretical stability analysis and superior experimental performance.

Findings

Algorithms are more robust to impulsive noise.

They outperform existing algorithms in changing noise environments.

Theoretical analysis confirms stability and efficiency.

Abstract

In this paper, a family of novel diffusion adaptive estimation algorithm is proposed from the asymmetric cost function perspective by combining diffusion strategy and the linear-linear cost (LLC), quadratic-quadratic cost (QQC), and linear-exponential cost (LEC), at all distributed network nodes, and named diffusion LLCLMS (DLLCLMS), diffusion QQCLMS (DQQCLMS), and diffusion LECLMS (DLECLMS), respectively. Then the stability of mean estimation error and computational complexity of those three diffusion algorithms are analyzed theoretically. Finally, several experiment simulation results are designed to verify the superiority of those three proposed diffusion algorithms. Experimental simulation results show that DLLCLMS, DQQCLMS, and DLECLMS algorithms are more robust to the input signal and impulsive noise than the DSELMS, DRVSSLMS, and DLLAD algorithms. In brief, theoretical analysis and experiment results show that those proposed DLLCLMS, DQQCLMS, and DLECLMS algorithms have superior performance when estimating the unknown linear system under the changeable impulsive noise environments and different types of input signals.