Two-stage maximum likelihood weighted recursive least squares algorithm for nonlinear systems and an application in
Dachuan Yu1, Yan Ji1
1College of Automation and Electronic Engineering, Qingdao University of Science and Technology, Qingdao 266061, China.
Abstract:
The purpose of this paper is to address the parameter identification problem of Hammerstein systems with dead-zone nonlinearity interfered with by colored noise. By introducing a switching function, the linear parameter expression of the piecewise nonlinear subsystem is obtained. On this basis, combined with the maximum likelihood principle, the maximum likelihood weighted recursive least squares (ML-WRLS) identification algorithm is constructed. A two-stage recursive least squares algorithm is proposed to improve estimation accuracy while reducing the computational burden. Compared with the ML-WRLS algorithm, the two-stage maximum likelihood weighted recursive least squares algorithm can provide better steady-state performance. The effectiveness of the proposed algorithms is demonstrated via examples.
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