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

• المؤلفون الرئيسيون: Yap K.S., Lim C.P., Au M.T.
• مؤلفون آخرون: 24448864400
• التنسيق: مقال
• منشور في: 2023
• الموضوعات: 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 dynami...