Least Squares Estimates in Stochastic Regression Models with Applications to Identification and Control of Dynamic Systems
随机回归模型的最小二乘估计及其在动态系统辨识与控制中的应用
Strong consistency and asymptotic normality of least squares estimates in stochastic regression models are established under certain weak assumptions on the stochastic regressors and errors. We discuss applications of these results to interval estimation of the regression parameters and to recursive on-line identification and control schemes for linear dynamic systems.