By Topic

Genetic-algorithms-based parameter and rule learning for fuzzy logic control systems

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 $31
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

2 Author(s)
Akec, J. ; Sch. of Manuf. & Mech. Eng., Birmingham Univ., UK ; Steiner, S.J.

Recently, genetic algorithms have been applied to the problem of automatic rule selection and parameter learning for fuzzy logic based control systems. But the question of formulating an effective cost function necessary for guiding the genetic search process, without external supervision or any training data, still presents many difficulties. This research paper presents a framework within which genetic algorithms can be complemented by ideas established in neural network-based reinforcement learning and classifier systems, for automatic rule generation and parameter learning for fuzzy logic based control systems. Initial results obtained from simulation studies on the control of a nonlinear and inherently unstable dynamic system, are encouraging

Published in:

Factory 2000 - The Technology Exploitation Process, Fifth International Conference on (Conf. Publ. No. 435)

Date of Conference:

2-4 Apr 1997