Abstract
Support vector machines (SVMs) are used for condition trend prediction of mechanical equipment. The influence of cost functions, kernel functions and parameters on prediction performance of SVMs is studied. Cost functions play a very important role on prediction performance of SVMs. Experiments show that the prediction performance of ε insensitive cost function is superior to that of least square cost function. At the same time, analysis of the experimental results shows that four kernel functions have very close prediction performance in short term prediction, and radial basis function kernel has better prediction performance than other kernels in long term prediction. In comparison with traditional Back Propagation (BP) neural network, Radial Basis Function (RBF) network and Generalized Regression Neural Network (GRNN), experiments show that SVMs, which implement the structure risk minimization principle, obtain the best prediction performance.
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Yang, J., Zhang, Y. (2005). Application Research of Support Vector Machines in Condition Trend Prediction of Mechanical Equipment. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3498. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427469_136
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DOI: https://doi.org/10.1007/11427469_136
Publisher Name: Springer, Berlin, Heidelberg
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