Adaptive inference-based learning and rule generation algorithms in fuzzy neural network for failure prediction

Behbood, V; Lu, J; Zhang, G

Adaptive inference-based learning and rule generation algorithms in fuzzy neural network for failure prediction

Behbood, V Lu, J

Zhang, G

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Publication Type:: Conference Proceeding
Citation:: Proceedings of 2010 IEEE International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2010, 2010, pp. 33 - 38
Issue Date:: 2010-12-01

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Field	Value	Language
dc.contributor.author	Behbood, V	en_US
dc.contributor.author	Lu, J https://orcid.org/0000-0003-0690-4732	en_US
dc.contributor.author	Zhang, G https://orcid.org/0000-0003-3960-0583	en_US
dc.date.issued	2010-12-01	en_US
dc.identifier.citation	Proceedings of 2010 IEEE International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2010, 2010, pp. 33 - 38	en_US
dc.identifier.isbn	9781424467907	en_US
dc.identifier.uri	http://hdl.handle.net/10453/16100
dc.description.abstract	Creating an applicable and precise failure prediction system is highly desirable for decision makers and regulators in the finance industry. This study develops a new Failure Prediction (FP) approach which effectively integrates a fuzzy logic-based adaptive inference system with the learning ability of a neural network to generate knowledge in the form of a fuzzy rule base. This FP approach uses a preprocessing phase to deal with the imbalanced data-sets problem and develops a new Fuzzy Neural Network (FNN) including an adaptive inference system in the learning algorithm along with its network structure and rule generation algorithm as a means to reduce prediction error in the FP approach. © 2010 IEEE.	en_US
dc.relation.ispartof	Proceedings of 2010 IEEE International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2010	en_US
dc.relation.isbasedon	10.1109/ISKE.2010.5680789	en_US
dc.title	Adaptive inference-based learning and rule generation algorithms in fuzzy neural network for failure prediction	en_US
dc.type	Conference Proceeding
utslib.for	080108 Neural, Evolutionary and Fuzzy Computation	en_US
dc.location.activity	China	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

Creating an applicable and precise failure prediction system is highly desirable for decision makers and regulators in the finance industry. This study develops a new Failure Prediction (FP) approach which effectively integrates a fuzzy logic-based adaptive inference system with the learning ability of a neural network to generate knowledge in the form of a fuzzy rule base. This FP approach uses a preprocessing phase to deal with the imbalanced data-sets problem and develops a new Fuzzy Neural Network (FNN) including an adaptive inference system in the learning algorithm along with its network structure and rule generation algorithm as a means to reduce prediction error in the FP approach. © 2010 IEEE.

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http://hdl.handle.net/10453/16100