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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...
This article aims to discuss the application of computational intelligence (CI) techniques in combination "with classical concepts in physics in devising investment strategies. In the analysis of investment strategies, many CI techniques are employed to predict market trends, such as the neural network (NN), the support vector machine (SVM), and particle swarm optimization (PSO) techniques. Other...
In this paper, the application of support vector machine (SVM) approach based on the statistics-learning theory of structural risk minimization in heart disease diagnosis. Aiming at the blindness of man made choice of parameter and kernel function of SVM, a chaotic adaptive particle swarm optimization (CAPSO) method is applied to select parameters of SVM in the paper and genetic characteristics of...
Support vector machine (SVM) plays an important role in the data mining and knowledge discovery by constructing a non-linear optimal classifier. The key problem of training support vector machines is how to solve quadratic programming problem, which results in calculation difficulty while learning samples gets larger. The intelligent search techniques, such as genetic algorithm and particle swarm...
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