By Topic

SOGARG: A self-organized genetic algorithm-based rule generation scheme for fuzzy controllers

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

2 Author(s)
Pal, T. ; Electron. & Commun. Sci. Unit, Indian Stat. Inst., Calcutta, India ; Pal, N.R.

This paper presents a self-organized genetic algorithm-based rule generation (SOGARG) method for fuzzy logic controllers. It is a three-stage hierarchical scheme that does not require any expert knowledge and input-output data. The first stage selects rules required to control the system in the vicinity of the set point. The second stage extends this to the entire input space, giving a rulebase that can bring the system to its set point from almost all initial states. The third stage refines the rulebase and reduces the number of rules. The first two stages use the same fitness function whose aim is only to acquire the controllability, but the last stage uses a different one, which attempts to optimize both the settling time and number of rules. The effectiveness of SOGARG is demonstrated using an inverted pendulum and the truck reversing.

Published in:

Evolutionary Computation, IEEE Transactions on  (Volume:7 ,  Issue: 4 )