Casillas, J., Carse, B. and Bull, L.
Fuzzy-XCS: A michigan genetic fuzzy system.
IEEE Transactions on Fuzzy Systems, 15 (4).
Available from: http://eprints.uwe.ac.uk/5891
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Publisher's URL: http://dx.doi.org/10.1109/TFUZZ.2007.900904
|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|
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|Deposited On:||22 Jan 2010 15:10|
|Last Modified:||12 Apr 2016 13:07|
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