Scheduled System Maintenance:
On Monday, April 27th, IEEE Xplore will undergo scheduled maintenance from 1:00 PM - 3:00 PM ET (17:00 - 19:00 UTC). No interruption in service is anticipated.
By Topic

A recurrent neuro-fuzzy network structure and learning procedure

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)
Ballini, R. ; DCA-FEEC, Univ. Estadual de Campinas, Sao Paulo, Brazil ; Soares, S. ; Gomide, F.

A novel recurrent neurofuzzy network is proposed in this paper. This model is constructed from fuzzy set models of neurons. The network has a multilayer, recurrent structure whose units are modeled through triangular norms and conorms, and weights are defined within the unit interval. The learning procedure developed is based on two main paradigms, the gradient search and associative reinforcement learning, that is, the output layer weights are adjusted via an error gradient method whereas a reward and punishment scheme updates the hidden layer weights. The recurrent neuro-fuzzy network is used to develop a model of a nonlinear process. Numerical results show that the neuro-fuzzy network proposed provides an accurate process model after a short period of learning time

Published in:

Fuzzy Systems, 2001. The 10th IEEE International Conference on  (Volume:3 )

Date of Conference:

2001