By Topic

A data-driven multiple-model modeling algorithm

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

5 Author(s)
Jikun Ye ; Dept. of Control Sci. & Eng., Missile Inistitude of Air Force Eng. Univ., Sanyuan, China ; Humin Lei ; Fei Wang ; Jiong Li
more authors

Based on the input-output data , a multiple-model modeling method is suggested for the complex nonlinear system. Firstly, fuzzy partition is employed to on-line clustering for the input-output data; and then, the least-squares (LS) algorithm is employed to construct the local model for each clustering, and the parameter of each local model is updated by the new data. The proposed algorithm takes advantage of the TSK model, combines the fuzzy partition and multiple-model modeling, and updates on-line the number of the local model and the parameter of each by the input-output data, so as to realize the on-line modeling for the complex nonlinear system. Simulation result shows the effectiveness of the proposed method.

Published in:

Advanced Computer Control (ICACC), 2010 2nd International Conference on  (Volume:5 )

Date of Conference:

27-29 March 2010