By Topic

A new method for identification of fuzzy models based on evolutionary algorithms and its application to the modeling of a wind turbine

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)
Moreno, G. ; Electr. Eng. Dept., Univ. de Chile, Santiago, Chile ; Saez, D. ; Orchard, M.E.

This paper presents a novel fuzzy model identification method, which is based on Genetic Algorithms and Particle Swarm Optimization. The proposed method is compared to other existing strategies for identification of fuzzy systems and equivalent linear models. A wind turbine system is used to verify and validate the proposed strategy. For purposes of this work, it is assumed that the simulator of the plant represents the actual system that needs to be identified. Simulations are carried out in continuous time and data are acquired with fixed sample time to generate a black box model of the system, using different techniques of identification.

Published in:

Control and Automation (ICCA), 2011 9th IEEE International Conference on

Date of Conference:

19-21 Dec. 2011