Rate of penetration prediction using hybrid Gaussian process regression model with ARD structure in geological drilling process-Reference-Cited by-同舟云学术

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Rate of penetration prediction using hybrid Gaussian process regression model with ARD structure in geological drilling process

Published:2022-05-04 Issue: Volume: Page:
ISSN:
Container-title:2022 13th Asian Control Conference (ASCC)
language:
Short-container-title:

Author:

Wu Xiao¹,Lai Xuzhi¹,Hu Jie¹,Chen Xin¹,Cao Weihua¹,Wu Min¹

Affiliation:

1. China University of Geosciences,School of Automation,Wuhan,China,430074

Funder

National Natural Science Foundation of China

Natural Science Foundation of Hubei Province

Fundamental Research Funds for the Central Universities

China University of Geosciences

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9828005/9828007/09828292.pdf?arnumber=9828292

Reference18 articles.

1. Computational intelligence based prediction of drilling rate of penetration: A comparative study

2. Gaussian Processes for Machine Learning;williams,2006

3. Battery State-of-Charge Estimation Based on Regular/Recurrent Gaussian Process Regression

4. Short-Term Solar Power Forecasting Based on Weighted Gaussian Process Regression

5. Gaussian Process Regression With Automatic Relevance Determination Kernel for Calendar Aging Prediction of Lithium-Ion Batteries

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Improvement of Rate of Penetration in Drilling Process Based on TCN-Vibration Recognition;IEEE Transactions on Instrumentation and Measurement;2024

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