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 valve is a key control component in the oil and gas transportation system, which, due to environment, transmission medium, other factors, susceptible internal leakage, resulting failure. Conventional testing methods cannot judge service life of valves. Therefore, it important carry out prediction research for safety. In this work, method based on PCA-PSO-LSSVM algorithm proposed. main facto...

Accurate forecast of water demand is one of the main problems in developing management strategy for the optimal control of water supply system. In this paper, a hybrid model which combines empirical mode decomposition (EMD) and least square support vector machine (LSSVM) model is proposed to forecast water demand. This hybrid is formulated specifically to address in modelling water demand that ...

As energy fuels play a significant role in many parts of human life, it is of great importance to have an effective price predictive analysis. In this chapter, the hybridization of Least Squares Support Vector Machines (LSSVM) with an enhanced Artificial Bee Colony (eABC) is proposed to meet the challenge. The eABC, which serves as an optimization tool for LSSVM, is enhanced by two types of mut...

Feature selection and classifier hyper-parameter optimization are important stages of any computer-aided diagnosis (CADx) system for mammography. The optimal selection for shape features, kernel parameter, and classifier regularization constant is crucial to achieve a good generalization and performance of least-squares support vector machines (LSSVMs). This paper presents a morphology-based CA...

How to analyze the features of stock price accurately and master the regularity of stock price changing with time quickly and effectively is of great theoretical and realistic significance and is an important research direction in financial field. For complicated non-linear and periodic variations of stock prices, a parallel computing model is proposed in this paper based on stock prediction al...

Credit risk evaluation has been the major focus of financial and banking industry due to recent financial crises and regulatory concern of Basel II. Recent studies have revealed that emerging artificial intelligent techniques are advantageous to statistical models for credit risk evaluation. In this study, we discuss the use of least square support vector machine (LSSVM) technique to design a c...

In this research, a comparison of the relevance vector machine (RVM), least square support vector machine (LSSVM) and the radial basis function neural network (RBFNN) for evaporation duct estimation are presented. The parabolic equation model is adopted as the forward propagation model, and which is used to establish the training database between the radar sea clutter power and the evaporation ...

Aiming at the problems that existing single time series models are not accurate and robust enough when it comes to forecasting iron ore prices parameters of traditional LSSVM model difficult determine, we propose a combined based on Phase Space Reconstruction (PSR), wavelet transform (PSR-WA-LSSVM) tackle these issues. ARIMA model, LSTM PSR-LSSVM PSR-WA-LSSVM were used for contrast simulation f...