Improved diagnostics for the incipient faults in analog circuits using LSSVM based on PSO algorithm with Mahalanobis distance

Neurocomputing

Volumn 133, Issue , 2014, Pages 237-248

  Long, Bing ^a   Xian, Weiming ^a   Li, Min ^a   Wang, Houjun ^a  

^a UNIVERSITY OF ELECTRONIC SCIENCE AND TECHNOLOGY OF CHINA   (China)

Author keywords

Analog circuits; Diagnostics; Feature vector selection; Least squares support vector machine; Mahalanobis distance; Particle swarm optimization

Indexed keywords

ANALOG CIRCUITS; HIDDEN MARKOV MODELS; HIGHER ORDER STATISTICS; PARTICLE SWARM OPTIMIZATION (PSO); PLASMA DIAGNOSTICS; SUPPORT VECTOR MACHINES;

CLASSIFICATION ACCURACY; COMPONENT PARAMETERS; FEATURE VECTOR SELECTION; LEAST SQUARES SUPPORT VECTOR MACHINES; MAHALANOBIS DISTANCES; MULTI-FAULT DIAGNOSIS; STANDARD DEVIATION; STATISTICAL PROPERTIES;

VECTORS;

ACCURACY; ANALOG CIRCUIT; ARTICLE; CLASSIFICATION ALGORITHM; CLASSIFIER; COMPUTER SIMULATION; CONTROLLED STUDY; DIAGNOSTIC PROCEDURE; HUMAN; INFORMATION PROCESSING; INTERMETHOD COMPARISON; MAHALONOBIS DISTANCE; MATHEMATICAL ANALYSIS; MATHEMATICAL MODEL; PARTICLE SWARM OPTIMIZATION; PHYSICAL PHENOMENA; PRIORITY JOURNAL; PROCESS DEVELOPMENT; PROCESS MODEL; PROCESS OPTIMIZATION; REGRESSION ANALYSIS; SUPPORT VECTOR MACHINE; VELOCITY;

EID: 84894570624     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2013.11.012     Document Type: Article

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