By Topic

Time series models discovery with similarity-based neuro-fuzzy networks and evolutionary algorithms

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)
Valdes, J.J. ; Inst. for Inf. Technol., Nat. Res. Council of Canada, Montreal, Ont., Canada

The discovery of patterns of dependency in heterogeneous multivariate dynamic systems is approached with similarity-based neuro-fuzzy networks and evolutionary algorithms. The search space contains general autoregressive non-linear models representing the dependency structure of the process. Examples show that the proposed approach gives better results than the classical statistical one

Published in:

Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on  (Volume:3 )

Date of Conference:

2002