Adaptive BP Network Prediction Method for Ground Surface Roughness with High-Dimensional Parameters
Ground surface roughness is difficult to predict through a physical model due to its complex influencing factors. BP neural networks (BPNNs), a promising method, have been widely applied in the prediction of surface roughness. This paper uses the concept of BPNN to predict ground surface roughness c...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-08-01
|
| Series: | Mathematics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7390/10/15/2788 |
| _version_ | 1797413067003265024 |
|---|---|
| author | Xubao Liu Yuhang Pan Ying Yan Yonghao Wang Ping Zhou |
| author_facet | Xubao Liu Yuhang Pan Ying Yan Yonghao Wang Ping Zhou |
| author_sort | Xubao Liu |
| collection | DOAJ |
| description | Ground surface roughness is difficult to predict through a physical model due to its complex influencing factors. BP neural networks (BPNNs), a promising method, have been widely applied in the prediction of surface roughness. This paper uses the concept of BPNN to predict ground surface roughness considering the state of the grinding wheel. However, as the number of input parameters increases, the local optimum solution of the model that arises is more serious. Therefore, “identify factors” are designed to judge the iterative state of the model, whilst “memory factors” are designed to store the best weights during network training. The iterative termination conditions of the model are improved, and the learning rate and update rules of the weights are adjusted to avoid the local optimal solution. The results show that the prediction accuracy of the presented model is higher and more stable than the traditional model. Under three types of iteration steps, the average prediction accuracy is improved from 0.071, 0.065, 0.066 to 0.049, 0.042, 0.039 and the standard deviation of prediction decreased from 0.0017, 0.0166, 0.0175 to 0.0017, 0.0070, 0.0076, respectively. Therefore, the proposed method provides guidance for improving the global optimization ability of BPNNs and developing more accurate models for predicting surface roughness. |
| first_indexed | 2024-03-09T05:12:12Z |
| format | Article |
| id | doaj.art-aa47ed29b7cc4d4eb15dc708b9c53218 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-09T05:12:12Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-aa47ed29b7cc4d4eb15dc708b9c532182023-12-03T12:48:37ZengMDPI AGMathematics2227-73902022-08-011015278810.3390/math10152788Adaptive BP Network Prediction Method for Ground Surface Roughness with High-Dimensional ParametersXubao Liu0Yuhang Pan1Ying Yan2Yonghao Wang3Ping Zhou4Key Laboratory for Precision and Non-Traditional Machining Technology of Ministry of Education, Dalian University of Technology, Dalian 116024, ChinaKey Laboratory for Precision and Non-Traditional Machining Technology of Ministry of Education, Dalian University of Technology, Dalian 116024, ChinaKey Laboratory for Precision and Non-Traditional Machining Technology of Ministry of Education, Dalian University of Technology, Dalian 116024, ChinaKey Laboratory for Precision and Non-Traditional Machining Technology of Ministry of Education, Dalian University of Technology, Dalian 116024, ChinaKey Laboratory for Precision and Non-Traditional Machining Technology of Ministry of Education, Dalian University of Technology, Dalian 116024, ChinaGround surface roughness is difficult to predict through a physical model due to its complex influencing factors. BP neural networks (BPNNs), a promising method, have been widely applied in the prediction of surface roughness. This paper uses the concept of BPNN to predict ground surface roughness considering the state of the grinding wheel. However, as the number of input parameters increases, the local optimum solution of the model that arises is more serious. Therefore, “identify factors” are designed to judge the iterative state of the model, whilst “memory factors” are designed to store the best weights during network training. The iterative termination conditions of the model are improved, and the learning rate and update rules of the weights are adjusted to avoid the local optimal solution. The results show that the prediction accuracy of the presented model is higher and more stable than the traditional model. Under three types of iteration steps, the average prediction accuracy is improved from 0.071, 0.065, 0.066 to 0.049, 0.042, 0.039 and the standard deviation of prediction decreased from 0.0017, 0.0166, 0.0175 to 0.0017, 0.0070, 0.0076, respectively. Therefore, the proposed method provides guidance for improving the global optimization ability of BPNNs and developing more accurate models for predicting surface roughness.https://www.mdpi.com/2227-7390/10/15/2788BP networklocal optimumground surface roughnesswheel wear |
| spellingShingle | Xubao Liu Yuhang Pan Ying Yan Yonghao Wang Ping Zhou Adaptive BP Network Prediction Method for Ground Surface Roughness with High-Dimensional Parameters Mathematics BP network local optimum ground surface roughness wheel wear |
| title | Adaptive BP Network Prediction Method for Ground Surface Roughness with High-Dimensional Parameters |
| title_full | Adaptive BP Network Prediction Method for Ground Surface Roughness with High-Dimensional Parameters |
| title_fullStr | Adaptive BP Network Prediction Method for Ground Surface Roughness with High-Dimensional Parameters |
| title_full_unstemmed | Adaptive BP Network Prediction Method for Ground Surface Roughness with High-Dimensional Parameters |
| title_short | Adaptive BP Network Prediction Method for Ground Surface Roughness with High-Dimensional Parameters |
| title_sort | adaptive bp network prediction method for ground surface roughness with high dimensional parameters |
| topic | BP network local optimum ground surface roughness wheel wear |
| url | https://www.mdpi.com/2227-7390/10/15/2788 |
| work_keys_str_mv | AT xubaoliu adaptivebpnetworkpredictionmethodforgroundsurfaceroughnesswithhighdimensionalparameters AT yuhangpan adaptivebpnetworkpredictionmethodforgroundsurfaceroughnesswithhighdimensionalparameters AT yingyan adaptivebpnetworkpredictionmethodforgroundsurfaceroughnesswithhighdimensionalparameters AT yonghaowang adaptivebpnetworkpredictionmethodforgroundsurfaceroughnesswithhighdimensionalparameters AT pingzhou adaptivebpnetworkpredictionmethodforgroundsurfaceroughnesswithhighdimensionalparameters |