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In this paper a new, generalized PSO (GPSO) algorithm is presented and analyzed, both theoretically and empirically. The new optimizer enables direct control over the properties of the search process. In addition, PSO is addressed in conceptually different manner, revealing further aspects of the algorithm behavior. GPSO is applied for training radial basis(More)
In this paper, support vector machines (SVMs) are applied in predicting fuel consumption in the first phase of oil refining at oil refinery ldquoNIS Rafinerija Nafte Novi Sadrdquo in Novi Sad, Serbia. During cross-validation process of the SVM training particle swarm optimization (PSO) algorithm was utilized in selection of free SVM parameters. In(More)
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