Fuzzy-XCS: A michigan genetic fuzzy system

Casillas, J., Carse, B. and Bull, L. (2007) Fuzzy-XCS: A michigan genetic fuzzy system. IEEE Transactions on Fuzzy Systems, 15 (4). pp. 536-550. ISSN 10636706 Available from: http://eprints.uwe.ac.uk/5891

Full text not available from this repository

Publisher's URL: http://dx.doi.org/10.1109/TFUZZ.2007.900904

Item Type:Article
Additional Information:The main proposal of this paper is to extend the accuracy-based XCS learning classifier system to the fuzzy case, enabling the new fuzzy classifier system to operate using reinforcement learning with continuous valued inputs and outputs. This is a significant contribution since extending the discrete-valued, accuracy-based XCS to the fuzzy domain has long been recognized as a challenging problem. The representation and operators proposed in Fuzzy-XCS are designed to encourage optimal generalization in the evolved fuzzy rule base, providing easier scalability to higher dimensional spaces, faster inference and better linguistic interpretability.
Uncontrolled Keywords:Continuous action, genetic fuzzy systems, Michigan-style learning classifier systems, reinforcement learning
Faculty/Department:Faculty of Environment and Technology
ID Code:5891
Deposited By: R. Upload account
Deposited On:22 Jan 2010 15:10
Last Modified:12 Apr 2016 13:07

Request a change to this item