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The correct identification of two-phase flow regime is the basis for the accurate measurement of other flow parameters in two-phase flow measurement. A PSO-SVM(Particle Swarm Optimization and Support Vector Machine) model, which can overcome selecting parameters needed in SVM model, was developed to identify the flow regime. The application of PSO-SVM improves the accuracy of flow regime recognition...
Reservoir inflow forecasting plays an essential role in reservoir management to ensure efficient water supply and more high accuracy inflow forecasting can lead to more effective use of water resources. In this study, support vector machine (SVM) with particle swarm optimization (PSO) for reservoir annual inflow forecasting is presented, among which PSO is used to find out the best parameter value...
This study applies a novel neural network technique, support vector regression (SVR), to rainfall forecasting. To build an effective SVR model, SVR's parameters must be set carefully. This study proposes a novel approach, known as particle swarm optimization algorithm (SVR-PSO), which searches for SVR's optimal parameters, and then adopts the optimal parameters to construct the SVR models. The monthly...
Precise forecasting for shareprice is very important to investment and financing. Support vector regression, called as SVR, is a novel learning algorithm based on statistical learning theory, which has greater generalization ability than traditional neural networks. In order to select the appropriate parameters of SVR, particle swarm optimization is introduced to choose the user-determined parameters...
This paper deals with the application of least squares support vector regression (LS-SVR) with radial basis function (RBF) kernel in dam crack forecasting. In the process of LS-SVR, we performed the standard grid search and particle swarm optimization (PSO) to tune hyperparameters of LS-SVR. The results demonstrate that our PSO approach can identify optimal or near optimal parameters faster than the...
Load forecasting is very essential to the operations of electric companies. This paper presents a rapid electric load forecasting algorithm based on Particle Swarm Optimization (PSO) and Core Vector Regression (CVR), called PSO-CVR algorithm. PSO is applied to determine the parameters of CVR, then CVR manages the issues of forecasting and training. In order to compare the results among different size...
Traffic accident forecasting is important for altering and planning of road. Recently time series analysis is an important direction in traffic accident forecasting. Support vector regression (SVR) a kind of SVM used in regression and has better nonlinear forecasting performance than BP neural network. In the paper, the combination method based on particle swarm optimization and support vector regression...
To accurately predict the non-stationary time series, an approach based on integration of wavelet transform, PSO (Particle Swarm Optimization) and SVM (Support Vector Machine) is proposed. Wavelet decomposition is used to reduce the complexity of time series. Different components are predicted by their corresponding SVM forecasters, respectively, after wavelet transform. The final forecasting result...
Economic growth forecasting is important to make the policy on national economic development. Support vector machine (SVM) is a new machine learning method, which seeks to minimize an upper bound of the generalization error instead of the empirical error as in conventional neural networks. In the study, support vector machine and particle swarm optimization is applied in economic growth forecasting,...
Forecasting agriculture water consumption is significant to optimize confiration of water resources. In the paper, we have combined particle swarm optimization (PSO) and support vector machines (SVM) for agriculture water consumption forecasting. Compared to GA, the advantages of PSO are that PSO is easy to implement and there are few parameters to adjust. Thus, PSO is very suitable to determine training...
Stock return forecast has been an important issue and difficult task for both shareholders and financial professionals. To tackle this problem, we introduce least square support vector machine (LS-SVM), an improved algorithm that regresses faster than standard SVM, and dynamic inertia weight particle swarm optimization (W-PSO), that outperform standard PSO in parameter selection. The work of this...
A novel model was proposed for short-term electricity price forecasting based on rough set approach and improved support vector machines (SVM). Firstly, we can get reduced information table with no information loss by rough set approach. And then, this reduced information is used to develop classification rules and train SVM, at the same time, we make use of the particle swarm optimization to optimize...
Accurate forecasting of power system short -term load has been one of the most important issues in the electricity industry. And the forecasting accuracy is influenced by many unpredicted factors. Because of the non-linear features of short-term power load, the paper uses support vector machines (SVM) technology for the short-term electricity load forecast. The method can better solve such practical...
A new method for load forecasting based on LS-SVM, PSO and wavelet transform is proposed. The wavelet transform is adopted to decompose the historical data, so the approximate part and several detail parts are obtained. The results of wavelet transform are predicted by a separate LS-SVM predictor. PSO is employed to determine these parameters of SVM model. The novel forecast model integrates the advantage...
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