Model selection approaches for non-linear system identification: a review
International Journal of Systems Science
Statistical Monitoring of Injection Moulds
Proceedings of the 2008 conference on Artificial Intelligence Research and Development: Proceedings of the 11th International Conference of the Catalan Association for Artificial Intelligence
Dual features functional support vector machines for fault detection of rechargeable batteries
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews - Special issue on information reuse and integration
Expert Systems with Applications: An International Journal
Distributed text classification with an ensemble kernel-based learning approach
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Electric load forecasting based on locally weighted support vector regression
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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Support vector machines (SVMs) are receiving increased attention in different application domains for which neural networks (NNs) have had a prominent role. However, in quality monitoring little attention has been given to this more recent development encompassing a technique with foundations in statistic learning theory. In this paper, we compare C-SVM and ν-SVM classifiers with radial basis function (RBF) NNs in data sets corresponding to product faults in an industrial environment concerning a plastics injection molding machine. The goal is to monitor in-process data as a means of indicating product quality and to be able to respond quickly to unexpected process disturbances. Our approach based on SVMs exploits the first part of this goal. Model selection which amounts to search in hyperparameter space is performed for study of suitable condition monitoring. In the multiclass problem formulation presented, classification accuracy is reported for both strategies. Experimental results obtained thus far indicate improved generalization with the large margin classifier as well as better performance enhancing the strength and efficacy of the chosen model for the practical case study.