Improved GART Neural Network Model for Pattern Classification and Rule Extraction With Application to Power Systems

  • Authors:

  • Keem Siah Yap;Chee Peng Lim;Mau Teng Au

  • Affiliations:

  • College of Graduate Studies, Universiti Tenaga Nasional, Kajang, Malaysia;School of Computer Sciences, University of Science Malaysia, Penang, Malaysia;Power Engineering Centre, Universiti Tenaga Nasional, Kajang, Malaysia

  • Venue:
  • IEEE Transactions on Neural Networks - Part 2
  • Year:
  • 2011

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Abstract

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.