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

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

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...

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書誌詳細
主要な著者:	Yap K.S., Lim C.P., Au M.T.
その他の著者:	24448864400
フォーマット:	論文
出版事項:	2023
主題:	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
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Improved GART neural network model for pattern classification and rule extraction with application to power systems

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