By Topic

Learning of a backpropagation neural network to tune a fuzzy control of a thermal system

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)

This regular paper describes three important aspects: design, simulation and implementation of neuro-fuzzy control applied to the temperature variable of a thermal system with a range of 25°C to 75°C and resolution of 0.01%. In the design were found the membership functions and the fuzzy rules base already optimized of the fuzzy controller by means of a backpropagation neural network trained to 120 learning cycles. The simulation presents basic tables of the fuzzy controller obtained by the neural network. The implementation of the program of the fuzzy controller was effected using a 486 PC with conversion card A/D and of 8-bit port output

Published in:

Mixed-Signal Design, 2000. SSMSD. 2000 Southwest Symposium on

Date of Conference:

2000