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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Shengfeng Wang Hongwei Wang Mingfang Ni Deqi Kou Xun Tong |
| Copyright Year | 2011 |
| Description | Author affiliation: Department of Technical Support Engineering, Academy of Armored Forces Engineering, Beijing, China (Shengfeng Wang; Hongwei Wang; Mingfang Ni; Deqi Kou; Xun Tong) |
| Abstract | According to the characteristics of evaluation of maintenance quality, in this paper partial least squares (PLS) is adopted to improve the common least squares (LS), and the maintenance quality evaluation model based on FCM-PLS-RBFNN is set up, and the learning and training algorithm is provided for FCM-PLS-RBFNN, and the improving effect of the model and its validity and precision in maintenance quality evaluation is tested by the living example of certain equipment maintenance quality comprehensive evaluation. The result shows that the FCM-PLS-RBFNN is faster than FCM-LS-RBFNN in learning, and its approaching ability and popularize performance are improved obviously. It is workable and effective to apply the FCM-PLS-RBFNN in modeling and evaluating for maintenance quality. It provides new ideas for researching on the more external and better popularizes maintenance quality evaluation method. |
| Starting Page | 770 |
| Ending Page | 773 |
| File Size | 1059399 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781457712296 |
| e-ISBN | 9781457712326 |
| DOI | 10.1109/ICQR2MSE.2011.5976723 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2011-06-17 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Training RBF neural networks Comprehensive evaluation Neural networks Maintenance engineering Maintenance quality Data models Fuzzy c-means clustering Indexes Mathematical model Partial least squares Testing |
| Content Type | Text |
| Resource Type | Article |
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