Stochastic Analysis of the Recursive Least Squares Algorithm for Cyclostationary Colored Inputs This paper studies the stochastic behavior of the recursive least squares (RLS) algorithm in a system identification framework for a cyclostationary colored input. The input cyclostationary signal is modeled by a colored random process with periodically timevarying power. The system parameters vary according to a randomwalk. Mathematical models are derived for the mean and meansquaredeviation (MSD) behavior of the adaptive weights as a function of the input cyclostationarity. The MSD behaviors of the RLS and LMS algorithms are compared for cyclostationary colored input. Monte Carlo simulations provide strong support for the theory. A separate analysis for white Gaussian and nonGaussian inputs is presented in support of the assumptions made for the mathematical model above.

Twodimensional recursive least squares identification based on local polynomial modeling for batch processes Abstract   In this paper, a twodimensional recursive least squares identification method based on local polynomial modeling for batch processes is proposed. Using local polynomial modeling method to parameterize the timevarying characteristics of batch processes, a twodimensional cost function along both time and batch directions is minimized to design the recursive least squares identification algorithm. Because this proposed method employs local polynomial modeling and utilizes twodimensional data information to estimate model parameters, it can effectively improve the estimation accuracy and accelerate the convergence rate. Furthermore, the convergence property of the proposed method is analyzed. Finally, the simulation results show the superiority of the proposed method.