Open AccessGradient‐based iterative parameter estimation for bilinear‐in‐parameter systems using the model decomposition technique
Author(s) -
Chen Mengting,
Ding Feng,
Yang Erfu
Publication year - 2018
Publication title -
iet control theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.059
H-Index - 108
eISSN - 1751-8652
pISSN - 1751-8644
DOI - 10.1049/iet-cta.2018.5254
Subject(s) - bilinear interpolation , estimation theory , mathematics , iterative method , mathematical optimization , gradient method , linear system , block (permutation group theory) , bilinear transform , algorithm , control theory (sociology) , computer science , statistics , artificial intelligence , mathematical analysis , digital filter , geometry , filter (signal processing) , control (management) , computer vision
The parameter estimation issues of a block‐oriented non‐linear system that is bilinear in the parameters are studied, i.e. the bilinear‐in‐parameter system. Using the model decomposition technique, the bilinear‐in‐parameter model is decomposed into two fictitious submodels: one containing the unknown parameters in the non‐linear block and the other containing the unknown parameters in the linear dynamic one and the noise model. Then a gradient‐based iterative algorithm is proposed to estimate all the unknown parameters by formulating and minimising two criterion functions. The stochastic gradient algorithms are provided for comparison. The simulation results indicate that the proposed iterative algorithm can give higher parameter estimation accuracy than the stochastic gradient algorithms.