Sparse multikernel support vector regression machines trained by active learning
Expert Systems with Applications: An International Journal
Recurrent sparse support vector regression machines trained by active learning in the time-domain
Expert Systems with Applications: An International Journal
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Support vector machine (SVM) is a new machine learning method based on statistical learning theory, which is a powerful tool for solving the problem with small sample, nonlinear and high dimension. However, the practicability of SVM is affected due to the difficulty of selecting appropriate SVM parameters. Particle swarm optimization (PSO) is a new optimization method, which is motivated by social behavior of bird flocking or fish schooling. The optimization method not only has strong global search capability, but also is very easy to implement. Thus, in the study, the proposed PSO-SVM model is applied to fault diagnosis of turbo-generator, among which PSO is used to determine free parameters of support vector machine. Finally, the effectiveness and correctness of this method are validated by the results of fault diagnosis examples. Consequently, PSO-SVM is a proper method in fault diagnosis of turbo-generator.