In this paper, a hybrid particle swarm optimization based on the natural selection (NPSO) was presented and used to optimize the parameters of Least Square Support Vector Machine (LSSVM). The NPSO algorithm overcomes the shortcomings of premature convergence and poor local search capability of traditional Particle Swarm Optimization (PSO). Then a classification model of oil-gas-water three-phas...

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...

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...

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...

As the construction of energy internet progresses, proportion residential electricity consumption in end-use is increasing, peak load on grid growing year year, and seasonal regional power supply tensions, mainly for consumption, have become common problems across country. Accurate forecasting can provide strong data support operation demand response incentive setting response. For accuracy sta...