Reinforced ART (ReART) for online neural control

Ediriweera, Damjee D. and Marshall, Ian W. (2008) Reinforced ART (ReART) for online neural control. Lecture Notes in Electrical Engineering, 14 (n/a). pp. 293-304. ISSN 1876-1100

Full text not available from this repository.


Fuzzy ART has been proposed for learning stable recognition categories for an arbitrary sequence of analogue input patterns. It uses a match based learning mechanism to categorise inputs based on similarities in their features. However, this approach does not work well for neural control, where inputs have to be categorised based on the classes which they represent, rather than by the features of the input. To address this we propose and investigate ReART, a novel extension to Fuzzy ART. ReART uses a feedback based categorisation mechanism supporting class based input categorisation, online learning, and immunity from the plasticity stability dilemma. ReART is used for online control by integrating it with a separate external function which maps each ReART category to a desired output action. We test the proposal in the context of a simulated wireless data reader intended to be carried by an autonomous mobile vehicle, and show that ReART training time and accuracy are significantly better than both Fuzzy ART and Back Propagation. ReART is also compared to a Naïve Bayesian Classifier. Naïve Bayesian Classification achieves faster learning, but is less accurate in testing compared to both ReART, and Bach Propagation.

Item Type: Journal Article
Journal or Publication Title: Lecture Notes in Electrical Engineering
Additional Information: The original publication is available at
Uncontrolled Keywords: /dk/atira/pure/researchoutput/libraryofcongress/qa75
Departments: Faculty of Science and Technology > School of Computing & Communications
Faculty of Science and Technology > Lancaster Environment Centre
ID Code: 27254
Deposited By: Users 810 not found.
Deposited On: 09 Oct 2009 12:15
Refereed?: Yes
Published?: Published
Last Modified: 22 Jun 2019 02:36

Actions (login required)

View Item View Item