By Topic

Construction of dynamic fuzzy if-then rules through genetic reinforcement learning for temporal problems solving

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

1 Author(s)
Chia-Feng Juang ; Dept. of Electr. Eng., Chung-Chou Inst. of Technol.network, genetic algoritDept. of Electr. Eng., Chang-Hua, Taiwan

In this paper, a genetic algorithm (GA) based dynamic fuzzy network design approach is proposed. First, a dynamic fuzzy network (DyFN) constituted from a series of dynamic fuzzy if-then rules is introduced. One characteristic of DyFN is its ability to deal with temporal problems. Then, GA is adopted into the design process as a means of allowing the application of DyFN in situations where gradient information is costly to obtain or only a reinforcement signal is available. To promote the design performance, a modification to the traditional GA, the Relative-based Mutated Reproduction GA (RMRGA), is proposed. To show the efficiency of DyFN designed by GAs, including both traditional GA and RMRGA, two temporal problems, dynamic plant control and adaptive noise cancellation, are simulated. The simulated results have verified the efficiency of DyFN designed by GA

Published in:

IFSA World Congress and 20th NAFIPS International Conference, 2001. Joint 9th  (Volume:4 )

Date of Conference:

25-28 July 2001