We conduct an empirical analysis to investigate the relationship between thirty seven different source code metrics with fifteen different Web Service QoS (Quality of Service) parameters. The source code metrics used in our experiments consists of nineteen Object-Oriented metrics, six Baski and Misra metrics, and twelve Harry M. Sneed metrics. We apply Principal Component Analysis (PCA) and Rou...

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...

Aimed at the submarine uncertainties and model’s impreciseness, an integrated RS-Chaos-LSSVM model is put forward because of nonlinearity and time-variability of AUV heading control system. The LSSVM is a typical nonlinear regression modeling based on small sample. Its parameters are optimized by Chaos algorithm. It is to gain the optimal model and get the higher accurate. By using Rough theory...

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...

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...

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...

This paper proposes an effective model based on the least squares support vector machines (LSSVM) and the particle swarm optimization (PSO), termed PSO-LSSVM, for prediction of natural gas consumption, as an important energy resource. The salient feature of mapping nonlinear data into high dimension feature space, distinguishes LS-SVM as a powerful approach for forecasting and estimation. Optim...

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 ...