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An improved fuzzy ARTMAP and Q-learning agent model for pattern classification
journal contribution
posted on 2019-06-04, 00:00 authored by F Pourpanah, R Wang, Chee Peng LimChee Peng Lim, X Wang, M Seera, C J TanThe Fuzzy ARTMAP (FAM) network is an online supervised neural network that operates by computing the similarity level between the new sample and those prototype nodes stored in its network against a threshold. In our previous study, we have developed a multi-agent system consisting of an ensemble of FAM networks and Q-learning, known as QMACS, for data classification. In this paper, an Improved QMACS (IQMACS) model with trust measurement using a combination of Q-learning and Bayesian formalism is proposed. A number of benchmark and real-world problems, i.e., motor fault detection and human motion detection, are conducted to evaluate the effectiveness of IQMACS. Statistical features are extracted from real-world case studies and utilized for classification with IQMACS, QMACS, and their constituents. The experimental results indicate that IQMACS produces better classification performance by combining the outcomes of its constituents as compared with those of QMACS and other related methods.
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Journal
NeurocomputingPublisher
ElsevierLocation
Amsterdam, The NetherlandsPublisher DOI
ISSN
0925-2312eISSN
1872-8286Language
engNotes
In pressPublication classification
C1 Refereed article in a scholarly journalCopyright notice
2019, Elsevier B.V.Usage metrics
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Fuzzy ARTMAPMulti-agent systemQ-learningPattern classificationMotor fault detectionHuman motion detectionScience & TechnologyTechnologyComputer Science, Artificial IntelligenceComputer ScienceCONVOLUTIONAL NEURAL-NETWORKBRAIN STORM OPTIMIZATIONHYBRID MODELFEATURE-SELECTIONSYSTEMREPRESENTATIONRECOGNITIONFAULT
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