With the rapid development of the photovoltaic industry, fault monitoring is becoming an important issue in maintaining the safe and stable operation of a solar power station. In order to diagnose the fault types of photovoltaic array, a fault diagnosis method that is based on the Least Squares Support Vector Machine (LSSVM) in the Bayesian framework is put forward. First, based on the elaborat...

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...

A piezo-resistive pressure sensor is made of silicon, the nature of which is considerably influenced by ambient temperature. The effect of temperature should be eliminated during the working period in expectation of linear output. To deal with this issue, an approach consists of a hybrid kernel Least Squares Support Vector Machine (LSSVM) optimized by a chaotic ions motion algorithm presented. ...

As enjoying the closed form solution, least squares support vector machine (LSSVM) has been widely used for classification and regression problems having the comparable performance with other types of SVMs. However, LSSVM has two drawbacks: sensitive to outliers and lacking sparseness. Robust LSSVM (R-LSSVM) overcomes the first partly via nonconvex truncated loss function, but the current algor...

Real-time and accurate short-term traffic flow prediction is the premise and key of intelligent traffic control and guidance system. According to this problem, this paper put forward a prediction model based on multivariable phase space reconstruction and least squares support vector machine (LSSVM). First, the model confirms embedding dimension and delay time of the traffic flow, occupancy and...

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...

A fault diagnosis method for sensor fault based on ensemble empirical mode decomposition (EEMD) energy entropy and optimized structural parameters least squares support vector machine (LSSVM) is put forward in this paper. Firstly, the original output fault signals are pretreatment with EEMD, and then the EEMD energy entropy is extracted as the fault feature vector. Then the radial basis functio...

To reduce the influence of random fluctuation on wind power prediction, a new ultra-short-term prediction model, based wavelet decomposition (WD), variational mode (VMD), and least-squares support vector machine (LSSVM), is proposed in this paper. The method double LSSVM, where sequence decomposed by WD into low- high-frequency components, which are further VMD to obtain many modal components w...

In this work some quantitative structure activity relationship models were developed for prediction of three bioenvironmental parameters of 28 volatile organic compounds, which are used in assessing the behavior of pollutants in soil. These parameters are; half-life, non dimensional effective degradation rate constant and effective Péclet number in two type of soil. The most effective descripto...

In this paper, two classifiers are proposed to distinguish between bulking and nonbulking situations in an activated sludge wastewater treatment plant, based on available image analysis information. The first classifier consists of a simple linear classification function, while the second classifier uses a highly nonlinear least squares support vector machine (LS-SVM) to distinguish between bot...