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.

翻译：在本文中,从不对称成本功能的角度提出了一套新颖的传播适应性估算算法,将传播战略和线性线性成本(LLC)、二次赤道成本(QC)和线性电荷成本(LEC)结合起来,在所有分布式网络节点上,并分别命名为传播LLLCLMS(DLLCLMS)、传播 OCLMS(DCLMS)和扩散LECLMS(DLLCLMS)算法(DLELCLMS),这三种传播算法的平均估计错误和计算复杂性的稳定性在理论上得到分析。最后,一些实验模拟结果旨在核实这三种拟议的扩散算法的优越性。实验模拟结果表明,DLLLCLMS、DLCLMS和DLLCLOMS算法比DSELMS、DRVS SLSS SLMS和DLLLAD算法的算法更能强,在可变换的输入型噪音和不同类型的噪音环境中估计未知的线性系统。