论文标题
minimax凹惩罚正规化自适应系统识别
Minimax Concave Penalty Regularized Adaptive System Identification
论文作者
论文摘要
我们开发了一种递归最小二平方（RLS）类型算法，并具有minimax凹点惩罚（MCP），用于自适应识别代表通信通道的稀疏Tap-tap-weight量矢量。提出的算法通过使用预期最大化（EM）更新从接收信号的噪声流观测值（EM）更新中递归地得出了对TAP-vector的估计。我们证明了算法与局部最优值的收敛性，并为稳态误差提供了界限。利用雷利褪色通道，伏特拉系统和多元时间序列模型的仿真研究，我们证明了我们的算法在于点误差（MSE）sense，标准RLS和$ \ ell_1 $ regarlized rls中优于均值误差（MSE）sense。
We develop a recursive least square (RLS) type algorithm with a minimax concave penalty (MCP) for adaptive identification of a sparse tap-weight vector that represents a communication channel. The proposed algorithm recursively yields its estimate of the tap-vector, from noisy streaming observations of a received signal, using expectation-maximization (EM) update. We prove the convergence of our algorithm to a local optimum and provide bounds for the steady state error. Using simulation studies of Rayleigh fading channel, Volterra system and multivariate time series model, we demonstrate that our algorithm outperforms, in the mean-squared error (MSE) sense, the standard RLS and the $\ell_1$-regularized RLS.
