A technique for summarizing data access and its use in parallelism enhancing transformations
PLDI '89 Proceedings of the ACM SIGPLAN 1989 Conference on Programming language design and implementation
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Asymptotic behaviors of support vector machines with Gaussian kernel
Neural Computation
Application of PSO-Based Neural Network in Quality Assessment of Construction Project
MMIT '08 Proceedings of the 2008 International Conference on MultiMedia and Information Technology
Bio-inspired computing: constituents and challenges
International Journal of Bio-Inspired Computation
International Journal of Bio-Inspired Computation
International Journal of Bio-Inspired Computation
Active AODE learning based on a novel sampling strategy and its application
International Journal of Computer Applications in Technology
International Journal of Computer Applications in Technology
Texture recognition by using a non-linear kernel
International Journal of Computer Applications in Technology
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In this paper, we present an application of support vector machines (SVMs) and particle swarm optimisation (PSO) to fault diagnosis. SVMs have been successfully employed to solve regression problem of nonlinearity and small sample. However, the practicability of SVM is affected due to the difficulty of selecting appropriate SVM parameters. PSO is a new optimisation method, which is motivated by social behaviour of organisms such as bird flocking and fish schooling. The method not only has strong global search capability but also is very easy to implement. Thus, the proposed PSO-SVM model is applied to diagnosis operation of rolling bearing failure in this paper, in which PSO is used to determine free parameters of SVM. The experimental results also indicate that the SVM method can achieve greater accuracy than grey model, artificial neural network under the condition of availability of small training data.