دورية أكاديمية

Predicting the Loose Zone of Roadway Surrounding Rock Using Wavelet Relevance Vector Machine

التفاصيل البيبلوغرافية
العنوان: Predicting the Loose Zone of Roadway Surrounding Rock Using Wavelet Relevance Vector Machine
المؤلفون: Yang Liu, Yicheng Ye, Qihu Wang, Xiaoyun Liu, Weiqi Wang
المصدر: Applied Sciences, Vol 9, Iss 10, p 2064 (2019)
بيانات النشر: MDPI AG, 2019.
سنة النشر: 2019
المجموعة: LCC:Technology
LCC:Engineering (General). Civil engineering (General)
LCC:Biology (General)
LCC:Physics
LCC:Chemistry
مصطلحات موضوعية: relevant vector machine (RVM), wavelet relevance vector machine (WRVM), wavelet kernel function, loose zone of roadway surrounding rock, prediction model, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999
الوصف: 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.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 2076-3417
العلاقة: https://www.mdpi.com/2076-3417/9/10/2064Test; https://doaj.org/toc/2076-3417Test
DOI: 10.3390/app9102064
الوصول الحر: https://doaj.org/article/5c91947dc73d446ba7979988cb2df974Test
رقم الانضمام: edsdoj.5c91947dc73d446ba7979988cb2df974
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:20763417
DOI:10.3390/app9102064