College of Mathematics and Computer Science, Fuzhou University, China.
Neural Netw. 2010 Apr;23(3):396-405. doi: 10.1016/j.neunet.2009.11.004. Epub 2009 Dec 11.
In this paper, a novel noise-constrained least-squares (NCLS) method for online autoregressive (AR) parameter estimation is developed under blind Gaussian noise environments, and a discrete-time learning algorithm with a fixed step length is proposed. It is shown that the proposed learning algorithm converges globally to an AR optimal estimate. Compared with conventional second-order and high-order statistical algorithms, the proposed learning algorithm can obtain a robust estimate which has a smaller mean-square error than the conventional least-squares estimate. Compared with the learning algorithm based on the generalized least absolute deviation method, instead of minimizing a non-smooth linear L(1) function, the proposed learning algorithm minimizes a quadratic convex function and thus is suitable for online parameter estimation. Simulation results confirm that the proposed learning algorithm can obtain more accurate estimates with a fast convergence speed.
本文提出了一种新的噪声约束最小二乘（NCLS）方法，用于在盲高斯噪声环境下进行在线自回归（AR）参数估计，并提出了一种具有固定步长的离散时间学习算法。结果表明，所提出的学习算法全局收敛到 AR 最优估计。与传统的二阶和高阶统计算法相比，所提出的学习算法可以获得比传统最小二乘估计具有更小均方误差的鲁棒估计。与基于广义最小绝对偏差方法的学习算法相比，所提出的学习算法不是最小化非光滑线性 L(1)函数，而是最小化二次凸函数，因此适用于在线参数估计。仿真结果证实，所提出的学习算法可以以较快的收敛速度获得更准确的估计。
