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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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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
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spelling	my.uniten.dspace-303802023-12-29T15:47:13Z Improved GART neural network model for pattern classification and rule extraction with application to power systems Yap K.S. Lim C.P. Au M.T. 24448864400 55666579300 9742020600 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 Fuzzy inference systems 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. Final 2023-12-29T07:47:13Z 2023-12-29T07:47:13Z 2011 Article 10.1109/TNN.2011.2173502 2-s2.0-83655167009 https://www.scopus.com/inward/record.uri?eid=2-s2.0-83655167009&doi=10.1109%2fTNN.2011.2173502&partnerID=40&md5=b9f90b52abbb9bfe3954a9abec6e01ca https://irepository.uniten.edu.my/handle/123456789/30380 22 12 PART 2 6069866 2310 2323 Scopus
institution	Universiti Tenaga Nasional
building	UNITEN Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Tenaga Nasional
content_source	UNITEN Institutional Repository
url_provider	http://dspace.uniten.edu.my/
topic	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 Fuzzy inference systems 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
spellingShingle	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 Fuzzy inference systems 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 Yap K.S. Lim C.P. Au M.T. Improved GART neural network model for pattern classification and rule extraction with application to power systems
description	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.
author2	24448864400
author_facet	24448864400 Yap K.S. Lim C.P. Au M.T.
format	Article
author	Yap K.S. Lim C.P. Au M.T.
author_sort	Yap K.S.
title	Improved GART neural network model for pattern classification and rule extraction with application to power systems
title_short	Improved GART neural network model for pattern classification and rule extraction with application to power systems
title_full	Improved GART neural network model for pattern classification and rule extraction with application to power systems
title_fullStr	Improved GART neural network model for pattern classification and rule extraction with application to power systems
title_full_unstemmed	Improved GART neural network model for pattern classification and rule extraction with application to power systems
title_sort	improved gart neural network model for pattern classification and rule extraction with application to power systems
publishDate	2023
_version_	1806423432129150976
score	13.222552

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

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