Parameter estimation for nonlinear systems by using the data filtering and the multi-innovation identification theory

Abstract

For Hammerstein output-error autoregressive systems, a decomposition based multi-innovation stochastic gradient (D-MISG) identification algorithm and a data filtering based multi-innovation stochastic gradient (F-MISG) identification algorithm are derived by means of the key-term separation principle and the multi-innovation identification theory. The D-MISG algorithm uses the decomposition technique to transform a Hammerstein system into two subsystems and requires less computational cost, and the F-MISG algorithm uses a linear filter to filter the input-output data and has a higher estimation accuracy for larger innovation lengths. The simulation results show that the proposed two algorithm can give satisfactory parameter estimates.

Keywords:

• parameter estimation
• recursive identification
• key-term separation principle
• multi-innovation identification theory
• decomposition technique
• filtering
• nonlinear system

2010 AMS Subject Classifications:

• 93B30
• 93A30

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [No. 61273194 and No. 61304138] and the PAPD of Jiangsu Higher Education Institutions.