A ship power equipments' fault monitoring signal usually provides few samples and the data's feature is non-linear in practical situation. This paper adopts the method of the least squares support vector machine (LSSVM) to deal with the problem of fault pattern identification in the case of small sample data. Meanwhile, in order to avoid involving a local extremum and poor convergence precision...

Forecasting exchange rate requires a model that can capture the non-stationary and non-linearity of the exchange rate data. In this paper, empirical mode decomposition (EMD) is combines with least squares support vector machine (LSSVM) model in order to forecast daily USD/TWD exchange rate. EMD is used to decompose exchange rate data behaviors which are non-linear and nonstationary. LSSVM has b...

Abstract Monthly runoff forecasting has always been a key problem in water resources management. As data-driven method, the least square support vector machine (LSSVM) method investigated by numerous studies forecasting. However, selecting appropriate parameters for LSSVM is to obtaining satisfactory model performance. In this study, we propose hybrid monthly forecasting, VMD-SSA-LSSVM short, w...

The half-bridge converter series Y-connection microgrid (HCSY-MG) is a new type of microgrid. In order to reduce the harmonic content in HCSY-MG grid-connected current and at same time simplify parameter design process LCL filter, this study proposed an filter method based on improved particle swarm optimization-least squares support vector machine (PSO-LSSVM) by analyzing characteristics curre...

The classification accuracy of the least squares support vector machine (LSSVM) models strongly depends on proper setting of its parameters. An optimal selection approach of LSSVM parameters is put forward based on multi-swarm cooperative chaos particle swarm optimization (MCCPSO) algorithm. Chaos particle swarm optimization (CPSO) can improve the ability of local search optimization with good ...

Least squares support vector machine (LSSVM) is a learning algorithm based on statistical theory. Itsadvantages include robustness and calculation simplicity, it has good performance in the data processingof small samples. The LSSVM model lacks sparsity unable to handle large-scale problem, this articleproposes an method mixture kernel sparse This reduces theinitial training set sub-dataset usi...

Background: In view of the low accuracy prognosis model esophageal squamous cell carcinoma (ESCC), this study aimed to optimize least squares support vector machine (LSSVM) algorithm determine uncertain prognostic factors using a Cloud model, and consequently, establish new high-precision ESCC.