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Multiple regression models for energy consumption of office buildings in different climates in China
Siyu ZHOU, Neng ZHU
《能源前沿(英文)》 2013年 第7卷 第1期 页码 103-110 doi: 10.1007/s11708-012-0220-z
关键词: regression model energy consumption building envelope office building different climates
Dynamic response surface methodology using Lasso regression for organic pharmaceutical synthesis
《化学科学与工程前沿(英文)》 2022年 第16卷 第2期 页码 221-236 doi: 10.1007/s11705-021-2061-y
关键词: data-driven modeling pharmaceutical organic synthesis Lasso regression dynamic response surface methodology
《结构与土木工程前沿(英文)》 2021年 第15卷 第5期 页码 1181-1198 doi: 10.1007/s11709-021-0744-6
关键词: interaction load sharing ratio piled raft nonlinear regression artificial neural network
Multivariable regression model for Fox depth correction factor
Ravi Kant MITTAL, Sanket RAWAT, Piyush BANSAL
《结构与土木工程前沿(英文)》 2019年 第13卷 第1期 页码 103-109 doi: 10.1007/s11709-018-0474-6
关键词: settlement embedment Fox depth correction factor regression multivariable
《环境科学与工程前沿(英文)》 2023年 第17卷 第6期 doi: 10.1007/s11783-023-1676-2
● A novel framework integrating quantile regression with machine learning is proposed.
关键词: Driver-response Upper boundary of relationship Interpretable machine learning Quantile regression Total phosphorus Chlorophyll a
Ali Reza GHANIZADEH, Morteza RAHROVAN
《结构与土木工程前沿(英文)》 2019年 第13卷 第4期 页码 787-799 doi: 10.1007/s11709-019-0516-8
关键词: full-depth reclamation soil-reclaimed asphalt pavement blend Portland cement unconfined compressive strength multivariate adaptive regression spline
Pijush Samui, Jagan J
《结构与土木工程前沿(英文)》 2013年 第7卷 第2期 页码 133-136 doi: 10.1007/s11709-013-0202-1
关键词: unsaturated soil effective stress parameter Gaussian process regression (GPR) artificial neural network (ANN) variance
Faezehossadat KHADEMI,Mahmoud AKBARI,Sayed Mohammadmehdi JAMAL,Mehdi NIKOO
《结构与土木工程前沿(英文)》 2017年 第11卷 第1期 页码 90-99 doi: 10.1007/s11709-016-0363-9
关键词: concrete 28 days compressive strength multiple linear regression artificial neural network ANFIS sensitivity analysis (SA)
SPT based determination of undrained shear strength: Regression models and machine learning
Walid Khalid MBARAK, Esma Nur CINICIOGLU, Ozer CINICIOGLU
《结构与土木工程前沿(英文)》 2020年 第14卷 第1期 页码 185-198 doi: 10.1007/s11709-019-0591-x
关键词: undrained shear strength linear regression random forest gradient boosting machine learning standard penetration test
Presentation of regression analysis, GP and GMDH models to predict the pedestrian density in various
Iraj BARGEGOL; Seyed Mohsen HOSSEINIAN; Vahid NAJAFI MOGHADDAM GILANI; Mohammad NIKOOKAR; Alireza OROUEI
《结构与土木工程前沿(英文)》 2022年 第16卷 第2期 页码 250-265 doi: 10.1007/s11709-021-0785-x
关键词: pedestrian density regression analysis GP model GMDH model
Jun Yang, Jingyun Wang, Pengwei Qiao, Yuanming Zheng, Junxing Yang, Tongbin Chen, Mei Lei, Xiaoming Wan, Xiaoyong Zhou
《环境科学与工程前沿(英文)》 2020年 第14卷 第3期 doi: 10.1007/s11783-019-1216-2
关键词: Soil Heavy metal Influencing factor Categorical regression Identification method
Nasser L. AZAD,Ahmad MOZAFFARI
《机械工程前沿(英文)》 2015年 第10卷 第4期 页码 405-412 doi: 10.1007/s11465-015-0354-x
The main scope of the current study is to develop a systematic stochastic model to capture the undesired uncertainty and random noises on the key parameters affecting the catalyst temperature over the coldstart operation of automotive engine systems. In the recent years, a number of articles have been published which aim at the modeling and analysis of automotive engines’ behavior during coldstart operations by using regression modeling methods. Regarding highly nonlinear and uncertain nature of the coldstart operation, calibration of the engine system’s variables, for instance the catalyst temperature, is deemed to be an intricate task, and it is unlikely to develop an exact physics-based nonlinear model. This encourages automotive engineers to take advantage of knowledge-based modeling tools and regression approaches. However, there exist rare reports which propose an efficient tool for coping with the uncertainty associated with the collected database. Here, the authors introduce a random noise to experimentally derived data and simulate an uncertain database as a representative of the engine system’s behavior over coldstart operations. Then, by using a Gaussian process regression machine (GPRM), a reliable model is used for the sake of analysis of the engine’s behavior. The simulation results attest the efficacy of GPRM for the considered case study. The research outcomes confirm that it is possible to develop a practical calibration tool which can be reliably used for modeling the catalyst temperature.
关键词: automotive engine calibration coldstart operation Gaussian process regression machine (GPRM) uncertainty and random noises
扩大多元回归方法在跨组学研究中的范围 Article
Xiaoxi Hu, Yue Ma, Yakun Xu, Peiyao Zhao, Jun Wang
《工程(英文)》 2021年 第7卷 第12期 页码 1725-1731 doi: 10.1016/j.eng.2020.05.028
近年来科技的进步和发展使得高维数据急剧增加,研究人员对合适且有效的多元回归方法的需求也随之增长。许多传统的多元分析方法如主成分分析等已广泛应用于投资分析、图像识别和群体遗传结构分析等研究领域。然而,这些常见的方法存在其局限性,即忽略了响应之间的相关性和变量选择效率低的问题。因此,本文引入了降秩回归方法及其扩展形式——稀疏降秩回归和行稀疏的子空间辅助回归,这些方法有望满足上述需求,从而提高回归模型的可解释性。我们通过开展仿真研究来评估它们的效果,并将它们与其他几种变量选择方法进行比较。对于不同的应用场景,我们也提供了基于预测能力和变量选择精度的选择建议。最后,为了证明这些方法在微生物组研究领域的实用价值,我们将所选择的方法应用于实际种群水平的微生物组数据,结果验证了我们方法的有效性。该方法的扩展形式为未来的组学研究特别是多元回归研究提供了有价值的指导,并为微生物组学及其相关研究领域的新发现奠定了基础。
基于回归预测集成学习的交互式图像分割 Article
Jin ZHANG, Zhao-hui TANG, Wei-hua GUI, Qing CHEN, Jin-ping LIU
《信息与电子工程前沿(英文)》 2017年 第18卷 第7期 页码 1002-1020 doi: 10.1631/FITEE.1601401
Tanvi SINGH, Mahesh PAL, V. K. ARORA
《结构与土木工程前沿(英文)》 2019年 第13卷 第3期 页码 674-685 doi: 10.1007/s11709-018-0505-3
关键词: batter piles oblique load test neural network M5 model tree random forest regression ANOVA
标题 作者 时间 类型 操作
Multiple regression models for energy consumption of office buildings in different climates in China
Siyu ZHOU, Neng ZHU
期刊论文
Dynamic response surface methodology using Lasso regression for organic pharmaceutical synthesis
期刊论文
Interaction behavior and load sharing pattern of piled raft using nonlinear regression and LM algorithm-based
期刊论文
Multivariable regression model for Fox depth correction factor
Ravi Kant MITTAL, Sanket RAWAT, Piyush BANSAL
期刊论文
of driver-response relationships: identifying factors using a novel framework integrating quantile regression
期刊论文
compressive strength of soil-RAP blend stabilized with Portland cement using multivariate adaptive regression
Ali Reza GHANIZADEH, Morteza RAHROVAN
期刊论文
Determination of effective stress parameter of unsaturated soils: A Gaussian process regression approach
Pijush Samui, Jagan J
期刊论文
Multiple linear regression, artificial neural network, and fuzzy logic prediction of 28 days compressive
Faezehossadat KHADEMI,Mahmoud AKBARI,Sayed Mohammadmehdi JAMAL,Mehdi NIKOO
期刊论文
SPT based determination of undrained shear strength: Regression models and machine learning
Walid Khalid MBARAK, Esma Nur CINICIOGLU, Ozer CINICIOGLU
期刊论文
Presentation of regression analysis, GP and GMDH models to predict the pedestrian density in various
Iraj BARGEGOL; Seyed Mohsen HOSSEINIAN; Vahid NAJAFI MOGHADDAM GILANI; Mohammad NIKOOKAR; Alireza OROUEI
期刊论文
Identifying factors that influence soil heavy metals by using categorical regression analysis: A case
Jun Yang, Jingyun Wang, Pengwei Qiao, Yuanming Zheng, Junxing Yang, Tongbin Chen, Mei Lei, Xiaoming Wan, Xiaoyong Zhou
期刊论文
presence of different random noises and uncertainty: Implementation of generalized Gaussian process regression
Nasser L. AZAD,Ahmad MOZAFFARI
期刊论文