Nonlinear Dynamics

, Volume 73, Issue 1, pp 583–592

Multistage least squares based iterative estimation for feedback nonlinear systems with moving average noises using the hierarchical identification principle

Original Paper

DOI: 10.1007/s11071-013-0812-0

Cite this article as:
Hu, P. & Ding, F. Nonlinear Dyn (2013) 73: 583. doi:10.1007/s11071-013-0812-0


This paper develops a multistage least squares based iterative algorithm to estimate the parameters of feedback nonlinear systems with moving average noise from input–output data. Since that the identification model is bilinear on the unknown parameter space, the solution is to decompose a system into several subsystems with each of which is linear about its parameter vector, then to replace the unknown noise terms in the information vectors with their corresponding estimates at the previous iteration of each subsystem, and estimate each subsystem, respectively. The simulation results show that the proposed algorithm can work well.


Parameter estimationIterative identificationLeast squares algorithmHierarchical identification principleNonlinear systemFeedback system

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education)Jiangnan UniversityWuxiP.R. China
  2. 2.Control Science and Engineering Research CenterJiangnan UniversityWuxiP.R. China