Title :
Parameter estimation for additive nonlinear systems
Author :
Jing Chen ; Rui Ding
Author_Institution :
Wuxi Prof. Coll. of Sci. & Technol., Wuxi, China
Abstract :
This paper proposes a stochastic gradient algorithm and a recursive least squares algorithm for additive nonlinear systems. By using the Weierstrass approximation theorem, the model of the additive nonlinear systems can be changed to an identification model, then based on the identification model, two algorithms are proposed to estimate all the unknown parameters of the systems. An example is provided to show the effectiveness of the proposed algorithms.
Keywords :
approximation theory; gradient methods; least squares approximations; nonlinear systems; parameter estimation; recursive estimation; stochastic processes; Weierstrass approximation theorem; additive nonlinear systems; identification model; parameter estimation; recursive least squares algorithm; stochastic gradient algorithm; Additives; Computational modeling; Least squares approximations; Mathematical model; Nonlinear systems; Parameter estimation; Least squares; Nonlinear system; Parameter estimation; Stochastic gradient; Weierstrass approximation theorem;
Conference_Titel :
Control Conference (CCC), 2013 32nd Chinese
Conference_Location :
Xi´an
