Sparse least square support vector machines based on random entropy

Ma, Wenlu; Liu, Ran

doi:10.23919/chicc.2017.8027365

Cited by 1 publication

(1 citation statement)

References 23 publications

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“…For linearly inseparable problems, the appropriate kernel function is selected to solve. The type of kernel function and the relevant parameters used by every layer of the DSVM are selected according to the specific situation, to reach the optimal classification results [20].…”

Section: Dsvm a Svmmentioning

confidence: 99%

A New Rotation Machinery Fault Diagnosis Method Based on Deep Structure and Sparse Least Squares Support Vector Machine

Zhang

et al. 2019

IEEE Access

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In this paper, a fault diagnosis method that is based on the deep structure and the sparse least squares support vector machine (SLSSVM) is proposed. This method constructs the structure of a multi-layer support vector machine (SVM). First, the SVM on the first layer is trained by using the training samples, and it learns the shallow features of the data. Then, the ''feature extraction formula'' is used to generate a new expression of the sample, which is used as input of the next layer. The new layer of the SVM trains on the new sample, and it extracts and learns the deep features of the signal layer by layer; eventually, after multiple feature mapping, it outputs the diagnostic results on the last layer. Because of the deep structure, the algorithm complexity and operation time increase. Therefore, in this paper, the least squares support vector machine (LSSVM) is combined with the sparse theory. By constructing the approximate maximal linearly independent vector set in the feature space, we conduct the sparse expression of samples and obtain the discriminant function for classification, which effectively solves the problem of sparsity deficiency for the LSSVM. Last, the method is used to diagnose centrifugal pump faults and rolling bearing faults and compares with the several methods of the SVM, the SLSSVM, deep SVM, and convolutional neural networks. The diagnostic results indicate that the method in this paper has good performance. INDEX TERMS Fault diagnosis, deep structure, support vector machine, sparsity.

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Section: Dsvm a Svmmentioning

confidence: 99%

A New Rotation Machinery Fault Diagnosis Method Based on Deep Structure and Sparse Least Squares Support Vector Machine

Zhang

et al. 2019

IEEE Access

View full text Add to dashboard Cite

show abstract

Sparse least square support vector machines based on random entropy

Cited by 1 publication

References 23 publications

A New Rotation Machinery Fault Diagnosis Method Based on Deep Structure and Sparse Least Squares Support Vector Machine

A New Rotation Machinery Fault Diagnosis Method Based on Deep Structure and Sparse Least Squares Support Vector Machine

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