机译:输入和输出噪声存在下具有无偏准则的比例NLMS用于稀疏系统识别
School of Automation and Information Engineering, Xi’an University of Technology, Xi’an, China;
School of Automation and Information Engineering, Xi’an University of Technology, Xi’an, China;
School of Automation and Information Engineering, Xi’an University of Technology, Xi’an, China;
School of Automation and Information Engineering, Xi’an University of Technology, Xi’an, China;
School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, China;
Noise measurement; Algorithm design and analysis; System identification; Convergence; Simulation; Adaptive filters; Circuits and systems;
机译:稀疏系统识别的基于混合方差/四误差准则和无偏准则的比例自适应滤波算法
机译:输入和输出噪声存在下用于线性FIR系统的无偏辨识的双线性方程法
机译:基于输入输出白噪声调制函数的连续系统无偏参数估计
机译:在存在输入和输出干扰的情况下进行无偏FIR系统识别
机译:通过利用AFD型算法,频域系统识别线性时间不变的单输入单输出二维系统和多输入多输出1维系统
机译:一种基于正管的比例仿射投影算法用于抑制脉冲噪声的稀疏通道
机译:输入输出干扰下的无偏FIR系统辨识
机译:比例型NLms算法中的增益分配,用于随时快速衰减输出误差