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

• Main Authors: Yap K.S., Lim C.P., Au M.T.
• Other Authors: 24448864400
• Format: Article
• Published: 2023
• Subjects: Fuzzy inference systems; generalized adaptive resonance theory; pattern classification; rule extraction; Data Mining; Databases, Factual; Electric Power Supplies; Electricity; Feedback; Models, Theoretical; Neural Networks (Computer); Pattern Recognition, Automated; Fuzzy inference; Online systems; Pattern recognition; Power transmission; Adaptive resonance theory; Data sets; If-then rules; Laplacians; Likelihood functions; Neural network model; Online learning; Ordering algorithms; Rule extraction; Training data; article; artificial neural network; automated pattern recognition; data mining; electricity; factual database; feedback system; methodology; power supply; theoretical model; Neural networks

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. � 2006 IEEE.