Open AccessPARAMETER ESTIMATION OF NONLINEAR OUTPUT ERROR SYSTEM UNDER VARIATIONAL BAYESIAN METHOD BASED ON PROBABILISTIC GRAPHICAL MODEL
Author(s) -
Yiping P. Du,
Iyad Katib
Publication year - 2022
Publication title -
fractals
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.654
H-Index - 44
eISSN - 1793-6543
pISSN - 0218-348X
DOI - 10.1142/s0218348x22400758
Subject(s) - nonlinear system , estimation theory , system identification , probabilistic logic , nonlinear system identification , computer science , control theory (sociology) , identification (biology) , mathematics , bayesian probability , algorithm , mathematical optimization , artificial intelligence , data mining , control (management) , measure (data warehouse) , physics , botany , quantum mechanics , biology
To estimate the parameters of the nonlinear output error system (nonlinear system), a variational Bayesian estimation method (VB method) is proposed based on the probabilistic graphical model (PGM). First, the related theories are introduced in this study such as the PGM and nonlinear systems. Then, the parameter estimation model of the nonlinear system is established. Finally, a VB method is proposed based on the PGM to estimate the parameters of the nonlinear system, which is tested and verified by numerical simulation experiments. It is found that the parameter estimation model of nonlinear system and the proposed method can estimate the parameters of relevant nonlinear system better, and the minimum error between the parameter estimated value and the actual value is only about 0.0001. It proves the feasibility of the VB method based on PGM in the identification of nonlinear systems. The results of this study provide an important reference for the control and identification of nonlinear systems.