By Topic

Generic GA-based meta-level parameter optimization for pattern recognition systems

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)
Lumanpauw, E. ; Nanyang Technol. Univ., Singapore ; Pasquier, M. ; Oentaryo, R.J.

This paper proposes a novel generic meta-level parameter optimization framework to address the problem of determining the optimal parameters of pattern recognition systems. The proposed framework is currently implemented to control the parameters of neuro-fuzzy system, a subclass of pattern recognition system, by employing a genetic algorithm (GA) as the core optimization technique. Two neuro-fuzzy systems i.e., generic self-organizing fuzzy neural network realizing Yager inference (GenSoFNN-Yager) and reduced fuzzy cerebellar model articulation computer realizing the Yager inference (RFCMAC-Yager), are employed as the test prototypes to evaluate the proposed framework. Experimental results on several classification and regression problems have shown the efficacy and robustness of the proposed approach.

Published in:

Evolutionary Computation, 2007. CEC 2007. IEEE Congress on

Date of Conference:

25-28 Sept. 2007