Committee machine with over 95% classification accuracy for combustible gas identification

Proceedings of the IEEE International Conference on Electronics, Circuits, and Systems

Volumn , Issue , 2006, Pages 862-865

  Minghua, Shi ^a   Bermak, Amine ^a  

^a HONG KONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Hong Kong)

Author keywords

Committee machine; Gas identification; Pattern recognition; Tin oxide gas sensor

Indexed keywords

ALGORITHMS; CHEMICAL SENSORS; CLASSIFICATION (OF INFORMATION); CLASSIFIERS; COMPUTER VISION; DATA ACQUISITION; DETECTORS; FEATURE EXTRACTION; FEEDFORWARD NEURAL NETWORKS; IMAGE SEGMENTATION; LEARNING SYSTEMS; NEURAL NETWORKS; PATTERN RECOGNITION; PATTERN RECOGNITION SYSTEMS; RADIAL BASIS FUNCTION NETWORKS; SENSORS; TIN; TITANIUM COMPOUNDS;

CLASSIFICATION ACCURACIES; COMBINATION RULES; COMBUSTIBLE GASES; COMMITTEE MACHINES; DATA ACQUISITION (DAQ) SYSTEMS; DATA SETS; GAS IDENTIFICATION; GAUSSIAN MIXTURE MODEL (GMM); INDIVIDUAL CLASSIFIERS; INTERNATIONAL CONFERENCES; K-NEAREST NEIGHBORS (KNN); MULTI LAYER PERCEPTRON (MLP); OXIDE GAS; RADIAL-BASIS FUNCTION (RBF); REAL GASES; RECOGNITION SYSTEMS;

GASES;

EID: 36849058936     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICECS.2006.379925     Document Type: Conference Paper

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