By Topic

An Evolving Fuzzy Model for Embedded Applications

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)
de Barros, J.-C. ; Dept. of Eng. Sci., Oxford Univ. ; Dexter, A.L.

This paper describes an evolving fuzzy model (efM) approach to modelling non-linear dynamic systems in which an incremental learning method is used to build up the rule-base. The rule-base evolves when "new" information becomes available by creating a new rule, merging an existing rule or deleting an old rule, depended upon the proximity and potential of the rules, and the maximum number of rules to be used in the rule-base. The efM, which is based on a T-S fuzzy model with constant consequents, is a very good candidate for modelling complex non-linear systems, when the period of time required to collect a complete set of training data is too long for the model to be identified off-line and the learning scheme must be computationally undemanding, e.g. use in model-based self-learning controllers. The results presented in the paper demonstrate the ability of the efM to evolve the rule-base efficiently so as to account for the behaviour of the system in new regions of the operating space. The proposed approach generates an accurate model with relatively few rules in a computationally undemanding manner, even if the data are incomplete

Published in:

Evolving Fuzzy Systems, 2006 International Symposium on

Date of Conference:

7-9 Sept. 2006