Predicting the Loose Zone of Roadway Surrounding Rock Using Wavelet Relevance Vector Machine
| Autor: | Qihu Wang, Liu Xiaoyun, Liu Yang, Weiqi Wang, Yicheng Ye |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
wavelet kernel function
Difference of Gaussians 0211 other engineering and technologies wavelet relevance vector machine (WRVM) 02 engineering and technology lcsh:Technology relevant vector machine (RVM) Square (algebra) lcsh:Chemistry Root mean square Relevance vector machine symbols.namesake Wavelet Gaussian function General Materials Science loose zone of roadway surrounding rock lcsh:QH301-705.5 Instrumentation 021101 geological & geomatics engineering Mathematics Fluid Flow and Transfer Processes lcsh:T business.industry Process Chemistry and Technology General Engineering Pattern recognition Function (mathematics) 021001 nanoscience & nanotechnology lcsh:QC1-999 Computer Science Applications prediction model lcsh:Biology (General) lcsh:QD1-999 lcsh:TA1-2040 Kernel (statistics) symbols Artificial intelligence lcsh:Engineering (General). Civil engineering (General) 0210 nano-technology business lcsh:Physics |
| Zdroj: | Applied Sciences Volume 9 Issue 10 Applied Sciences, Vol 9, Iss 10, p 2064 (2019) |
| ISSN: | 2076-3417 |
| DOI: | 10.3390/app9102064 |
| Popis: | By applying the Wavelet Relevance Vector Machine (WRVM) method, this research proposes the loose zone of roadway surrounding rock prediction. Based on the theory of relevance vector machine (RVM), the wavelet function is introduced to replace the original Gauss function as the model kernel function to form the WRVM. Five factors affecting the loose zone of roadway surrounding rock are selected as the model input, and the prediction model of the loose zone of roadway surrounding rock based on WRVM is established. By using cross-validation method, the kernel parameters of three kinds of wavelet relevance vector machines (RVMs) are calculated. By comparing and analyzing the root mean square (RMS) error of the test results of each predictive model, the advantages and accuracy of the model are verified. In practical engineering applications, the average relative prediction errors of the Mexican relevance vector machine, the Morlet relevance vector machine and the difference of Gaussian (DOG) relevance vector machine models are accordingly 4.581%, 4.586% and 4.575%. The square correlation coefficient of the predicted samples is 0.95 > 0.9, which further verifies the accuracy and reliability of the proposed method. |
| Databáze: | OpenAIRE |
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