Distributed adaptive algorithm based on the asymmetric cost of error functions
This work addresses adaptive estimation for distributed networks in noisy environments, representing an incremental improvement by combining diffusion strategies with asymmetric cost functions.
The paper tackled the problem of adaptive estimation in distributed networks under impulsive noise and varying input signals by proposing three diffusion algorithms (DLLCLMS, DQQCLMS, DLECLMS) based on asymmetric cost functions, resulting in superior robustness compared to existing methods like DSELMS, DRVSSLMS, and DLLAD.
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.