Multi-class classification is one of the major challenges in machine learning and an ongoing research issue. Classification algorithms are generally binary, but they must be extended to multi-class problems for real-world application. more complex than binary classification. In classification, only decision boundaries class known, whereas multiclass several involved. The objective this investig...

The support vector machine (SVM) is a method for classification and for function approximation. This method commonly makes use of an /spl epsi/-insensitive cost function, meaning that errors smaller than /spl epsi/ remain unpunished. As an alternative, a least squares support vector machine (LSSVM) uses a quadratic cost function. When the LSSVM method is used for function approximation, a nonsp...

As a key hydrological parameter, daily reference evapotranspiration (ETo) determines the accuracy of the hydrological number of the crop, and, consequently, the regional optimization disposition of water resources. At present, the main methods for ETo estimation are the Penman-Monteith (PM) equation and its modified formula, both of which are based on climatic factors such as temperature, radia...

Accurate geomechanical parameters are critical in tunneling excavation, design, and supporting. In this paper, a displacements back analysis based on artificial bee colony (ABC) algorithm is proposed to identify geomechanical parameters from monitored displacements. ABC was used as global optimal algorithm to search the unknown geomechanical parameters for the problem with analytical solution. ...

Anomaly detection is the identification of abnormal system behavior, in which a model of normality is constructed, with deviations from the model identified as “abnormal”. Complex high-integrity systems typically operate normally for the majority of their service lives, and so examples of abnormal data may be rare in comparison to the amount of available normal data. Anomaly detection is partic...

The compressibility factor (Z-factor) of natural gases is necessary in many gas reservoir engineering calculations. Accurate determination of this parameter is of crucial need and challenges a large number of used simulators in petroleum engineering. Although numerous studies for prediction of gas compressibility factor have been reported in the literature, the accurate prediction of this param...

In this paper, a hybrid model integrating wavelet and least squares support machines (LSSVM) is proposed for crude oil price forecasting. In this model, Haar à trous wavelet transform is first selected to decompose an original time series into several sub-series with different scales. Then the LSSVM is used to predict each sub-series. And the final oil price forecasting is obtained by reconstru...

In the domain of industrial process modeling and control, Hammerstein model has been used widely to describe a class of nonlinear systems. Goethals et al. (2005) proposed a method based on Least Squares Support Vector Machines (LSSVM) to identify the input-output relationship of the Hammerstein model. Unfortunately, as the data points grow, this kernel learning approach costs much time correspo...