In view of some shortcomings of support vector machine, for instance, it is difficult to select the regularization parameter and the kernel function must satisfy Mercer’s condition, relevance vector machine (RVM) is developed and applied to the field of trend prediction. The performance of RVM, to a large extent, depends on the kernel function. However, a single kernel function is generally selected artificially and subjectively in current studies on RVM, which increases its dependency of the RVM to parameters and decreases the robustness in prediction process. To solve the problem, a new adaptive multi-kernel RVM is proposed for prediction. In the method, multiple kernel functions are selected originally and their weights are generated by the particle filter (PF) algorithm to construct multi-kernel RVM models. Then the optimal multi-kernel RVM model is obtained by iterative processes, i.e., predicting, weights updating and resampling. The effectiveness of the proposed method is validated by a simulation study and a case study of remaining useful life prediction of machinery. The results demonstrate that the proposed method obtains higher prediction accuracies compared with the single kernel RVM models.
LEI Yaguo
,
CHEN Wu
,
LI Naipeng
,
LIN Jing
. A Relevance Vector Machine Prediction Method Based on Adaptive Multi-kernel Combination and Its Application to Remaining Useful Life Prediction of Machinery[J]. Journal of Mechanical Engineering, 2016
, 52(1)
: 87
-93
.
DOI: 10.3901/JME.2016.01.087
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