| Issue |
MATEC Web Conf.
Volume 179, 2018
2018 2nd International Conference on Mechanical, Material and Aerospace Engineering (2MAE 2018)
|
|
|---|---|---|
| Article Number | 01017 | |
| Number of page(s) | 7 | |
| Section | Mechanical | |
| DOI | https://doi.org/10.1051/matecconf/201817901017 | |
| Published online | 26 July 2018 | |
Prediction Method of Equipment Degradation State Based on Improved RVM
Coastal Defense College, NAAU Yantai, China
a Corresponding author: kvcelu@163.com
In order to improve the prediction accuracy of the relevance vector machine model, an improved method for equipment condition prediction is proposed. First of all, an improved kernel function of variance Gauss kernel (VGKF) is constructed to improve the global performance and generalization ability of the kernel function. Then, by using the method of selecting the number of adjacent points in the chaotic sequence local prediction method, the H-Q criterion was used to optimize the embedding dimension of the training space to avoid the blindness of subjective selection. Through the prediction example of terminal guidance radar equipment test parameters, the effectiveness and superiority of the improved RVM were verified.
© The Authors, published by EDP Sciences 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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