Study on identification method of tool wear based on singular value decomposition and least squares support vector machine

Advances in Intelligent and Soft Computing

Volumn 112, Issue , 2011, Pages 835-843

  Guan, Shan ^a   Wang, Long Shan ^a  

^a JILIN UNIVERSITY   (China)

Author keywords

Empirical Mode Decomposition; Least Squares Support Vector Machine; Singular Value Decomposition; Tool wear condition monitoring

Indexed keywords

ACOUSTIC EMISSION SIGNAL; BP NEURAL NETWORKS; CONVERGENCE RATES; EMPIRICAL MODE DECOMPOSITION; EMPIRICAL MODE DECOMPOSITION METHOD; FEATURE VECTOR MATRIX; FEATURE VECTORS; IDENTIFICATION METHOD; IDENTIFICATION RATES; INTRINSIC MODE FUNCTIONS; LEAST SQUARES SUPPORT VECTOR MACHINE; LEAST SQUARES SUPPORT VECTOR MACHINES; LOCAL MINIMUMS; NONSTATIONARY; SINGULAR SPECTRUM; SINGULAR VALUE DECOMPOSITION METHOD; SINGULAR VALUES; TOOL WEAR;

ACOUSTIC EMISSIONS; CONDITION MONITORING; IMAGE RETRIEVAL; LEARNING ALGORITHMS; LEAST SQUARES APPROXIMATIONS; NEURAL NETWORKS; SIGNAL PROCESSING; SPEECH RECOGNITION; SUPPORT VECTOR MACHINES; VECTORS; WEAR OF MATERIALS;

SINGULAR VALUE DECOMPOSITION;

EID: 84855225548     PISSN: 18675662     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-642-25194-8_98     Document Type: Conference Paper

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