Improved GART neural network model for pattern classification and rule extraction with application to power systems

Yap, Keem Siah, Lim, Chee Peng and Au, Mau Teng 2011, Improved GART neural network model for pattern classification and rule extraction with application to power systems, IEEE transactions on neural networks, vol. 22, no. 12, pp. 2310-2323.

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Title Improved GART neural network model for pattern classification and rule extraction with application to power systems
Author(s) Yap, Keem Siah
Lim, Chee Peng
Au, Mau Teng
Journal name IEEE transactions on neural networks
Volume number 22
Issue number 12
Start page 2310
End page 2323
Total pages 14
Publisher IEEE
Place of publication Piscataway, N. J.
Publication date 2011-12
ISSN 1045-9227
1941-0093
Keyword(s) fuzzy inference systems
generalized adaptive resonance theory
pattern classification
rule extraction
Summary Generalized adaptive resonance theory (GART) is a neural network model that is capable of online learning and is effective in tackling pattern classification tasks. In this paper, we propose an improved GART model (IGART), and demonstrate its applicability to power systems. IGART enhances the dynamics of GART in several aspects, which include the use of the Laplacian likelihood function, a new vigilance function, a new match-tracking mechanism, an ordering algorithm for determining the sequence of training data, and a rule extraction capability to elicit if-then rules from the network. To assess the effectiveness of IGART and to compare its performances with those from other methods, three datasets that are related to power systems are employed. The experimental results demonstrate the usefulness of IGART with the rule extraction capability in undertaking classification problems in power systems engineering.
Language eng
Field of Research 089999 Information and Computing Sciences not elsewhere classified
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1.1 Refereed article in a scholarly journal
Persistent URL http://hdl.handle.net/10536/DRO/DU:30048774

Document type: Journal Article
Collection: Institute for Frontier Materials
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