By Topic

Parameter estimation for nonlinear stochastic model using generalized entropy optimization principle

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Liu Yunlong ; Sch. of Instrum. Sci. & Opto-Electron. Eng., Beihang Univ., Beijing, China ; Guo Lei ; Zhang Yumin

A new type of parameter estimation method has been proposed for a class of nonlinear stochastic model with non-Gaussian disturbance The Parzen window method was first used to estimate the density function of the sampled data and then the generalized entropy optimization principle was used to estimate the unknown parameters. No matter what distribution the noise obeys to, Gaussian or non-Gaussian, unbiased parameter estimated values can be obtained. The simulation results show the effectiveness of the proposed approaches.

Published in:

Control Conference (CCC), 2012 31st Chinese

Date of Conference:

25-27 July 2012