A sequential least squares algorithm for ARMAX dynamic network identification

作     者：Weerts H.H.M. Galrinho M. Bottegal G. Hjalmarsson H. den Hof P.M.J.V. 

作者机构：Eindhoven University of Technology Eindhoven Netherlands Department of Automatic Control School of Electrical Engineering KTH Royal Institute of Technology Stockholm Sweden 

出 版 物：《IFAC-PapersOnLine》 

年 卷 期：2018年第51卷第15期

页      面：844-849页

主　　题：dynamic networks identification algorithm least squares System identification 

摘      要：Identification of dynamic networks in prediction error setting often requires the solution of a non-convex optimization problem, which can be difficult to solve especially for large-scale systems. Focusing on ARMAX models of dynamic networks, we instead employ a method based on a sequence of least-squares steps. For single-input single-output models, we show that the method is equivalent to the recently developed Weighted Null Space Fitting, and, drawing from the analysis of that method, we conjecture that the proposed method is both consistent as well as asymptotically efficient under suitable assumptions. Simulations indicate that the sequential least squares estimates can be of high quality even for short data sets. © 2018