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Lipschitz非线性系统；动态输出反馈控制；事件触发控制；全局渐近稳定 1

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Interaction behavior and load sharing pattern of piled raft using nonlinear regression and LM algorithm-based

《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

摘要： In the recent era, piled raft foundation (PRF) has been considered an emergent technology for offshore and onshore structures. In previous studies, there is a lack of illustration regarding the load sharing and interaction behavior which are considered the main intents in the present study. Finite element (FE) models are prepared with various design variables in a double-layer soil system, and the load sharing and interaction factors of piled rafts are estimated. The obtained results are then checked statistically with nonlinear multiple regression (NMR) and artificial neural network (ANN) modeling, and some prediction models are proposed. ANN models are prepared with Levenberg–Marquardt (LM) algorithm for load sharing and interaction factors through backpropagation technique. The factor of safety (FS) of PRF is also estimated using the proposed NMR and ANN models, which can be used for developing the design strategy of PRF.

关键词： interaction     load sharing ratio     piled raft     nonlinear regression     artificial neural network    

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非线性计数系统的关键因子辨识方法 Research Article

张新民,王静波,魏驰航,宋执环

《信息与电子工程前沿（英文）》 2022年 第23卷 第1期   页码 123-133 doi: 10.1631/FITEE.2000324

摘要： 从数据中识别对系统输出产生较大影响的关键因子是科学和工程领域最具挑战性的任务之一。本文针对非线性计数系统，提出基于敏感性分析的广义高斯过程回归（SA-GGPR）建模方法，以识别影响系统输出的关键因子。SA-GGPR采用具有泊松似然的GGPR模型描述非线性计数系统。GGPR模型继承了非参数核学习和泊松分布的优点，可处理复杂非线性计数系统。然而，由于GGPR模型的非参数核学习架构，难以理解GGPR模型中输入和输出之间的关系。SA-GGPR方法通过定量评估不同输入对系统输出的影响来辨识影响系统输出的关键因子。在模拟非线性计数系统和实际钢铁轧制过程的应用结果表明，SA-GGPR方法在识别精度方面优于几种先进方法。

关键词： 关键因子；非线性计数系统；广义高斯过程回归；敏感性分析；钢铁轧制过程    

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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

摘要： The energy consumption of office buildings in China has been growing significantly in recent years. Obviously, there are significant relationships between building envelope and the energy consumption of office buildings. The 8 key building envelope influencing factors were found in this paper to evaluate their effects on the energy consumption of the air-conditioning system. The typical combinations of the key influencing factors were performed in Trnsy simulation. Then on the basis of the simulated results, the multiple regression models were developed respectively for the four climates of China—hot summer and warm winter, hot summer and cold winter, cold, and severely cold. According to the analysis of regression coefficients, the appropriate building envelope design schemes were discussed in different climates. At last, the regression model evaluations consisting of the simulation evaluations and the actual case evaluations were performed to verify the feasibility and accuracy of the regression models. The error rates are within±5% in the simulation evaluations and within±15% in the actual case evaluations. It is believed that the regression models developed in this paper can be used to estimate the energy consumption of office buildings in different climates when various building envelope designs are considered.

关键词： regression model     energy consumption     building envelope     office building     different climates    

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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

摘要： To study the dynamic behavior of a process, time-resolved data are collected at different time instants during each of a series of experiments, which are usually designed with the design of experiments or the design of dynamic experiments methodologies. For utilizing such time-resolved data to model the dynamic behavior, dynamic response surface methodology (DRSM), a data-driven modeling method, has been proposed. Two approaches can be adopted in the estimation of the model parameters: stepwise regression, used in several of previous publications, and Lasso regression, which is newly incorporated in this paper for the estimation of DRSM models. Here, we show that both approaches yield similarly accurate models, while the computational time of Lasso is on average two magnitude smaller. Two case studies are performed to show the advantages of the proposed method. In the first case study, where the concentrations of different species are modeled directly, DRSM method provides more accurate models compared to the models in the literature. The second case study, where the reaction extents are modeled instead of the species concentrations, illustrates the versatility of the DRSM methodology. Therefore, DRSM with Lasso regression can provide faster and more accurate data-driven models for a variety of organic synthesis datasets.

关键词： data-driven modeling     pharmaceutical organic synthesis     Lasso regression     dynamic response surface methodology    

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基于核空间非线性特征提取的图像质量评价方法 Article

Yong DING,Nan LI,Yang ZHAO,Kai HUANG

《信息与电子工程前沿（英文）》 2016年 第17卷 第10期   页码 1008-1017 doi: 10.1631/FITEE.1500439

摘要： 概要：在实现对与人类视觉感知相一致的图像质量的客观评价中，如何提取图像的视觉感知特征至关重要。不同于传统方法中通过线性变换或模型表达图像的方式，本文采用高维空间的一种数学表达来揭示图像的统计特性，通过引入核独立分量分析（kernel independent component analysis, KICA）方法实现非线性转化和图像的高维特征提取。从而提出一种基于非线性特征提取的全参考图像质量评价方法。在LIVE、TID2008和CSIQ等图像质量评价数据库上的实验结果表明，图像的非线性特征更有利于图像内在质量的描述，并且本文提出的方法性能良好，与主观评价较为一致。

关键词： 图像质量评价；全参考方法；特征提取；核空间；支持向量回归    

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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

摘要： This paper presents a simple and efficient equation for calculating the Fox depth correction factor used in computation of settlement reduction due to foundation embedment. Classical solution of Boussinesq theory was used originally to develop the Fox depth correction factor equations which were rather complex in nature. The equations were later simplified in the form of graphs and tables and referred in various international code of practices and standard texts for an unsophisticated and quick analysis. However, these tables and graphs provide the factor only for limited values of the input variables and hence again complicates the process of automation of analysis. Therefore, this paper presents a non-linear regression model for the analysis of effect of embedment developed using “IBM Statistical Package for the Social Sciences” software. Through multiple iterations, the value of coefficient of determination is found to reach 0.987. The equation is straightforward, competent and easy to use for both manual and automated calculation of the Fox depth correction factor for wide range of input values. Using the developed equation, parametric study is also conducted in the later part of the paper to analyse the extent of effect of a particular variable on the Fox depth factor.

关键词： settlement     embedment     Fox depth correction factor     regression     multivariable    

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of driver-response relationships: identifying factors using a novel framework integrating quantile regression

《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

摘要：

● 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    

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compressive strength of soil-RAP blend stabilized with Portland cement using multivariate adaptive regression

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

摘要： The recycled layer in full-depth reclamation (FDR) method is a mixture of coarse aggregates and reclaimed asphalt pavement (RAP) which is stabilized by a stabilizer agent. For design and quality control of the final product in FDR method, the unconfined compressive strength of stabilized material should be known. This paper aims to develop a mathematical model for predicting the unconfined compressive strength (UCS) of soil-RAP blend stabilized with Portland cement based on multivariate adaptive regression spline (MARS). To this end, two different aggregate materials were mixed with different percentages of RAP and then stabilized by different percentages of Portland cement. For training and testing of MARS model, total of 64 experimental UCS data were employed. Predictors or independent variables in the developed model are percentage of RAP, percentage of cement, optimum moisture content, percent passing of #200 sieve, and curing time. The results demonstrate that MARS has a great ability for prediction of the UCS in case of soil-RAP blend stabilized with Portland cement ( is more than 0.97). Sensitivity analysis of the proposed model showed that the cement, optimum moisture content, and percent passing of #200 sieve are the most influential parameters on the UCS of FDR layer.

关键词： full-depth reclamation     soil-reclaimed asphalt pavement blend     Portland cement     unconfined compressive strength     multivariate adaptive regression spline    

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Determination of effective stress parameter of unsaturated soils: A Gaussian process regression approach

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

摘要： This article examines the capability of Gaussian process regression (GPR) for prediction of effective stress parameter ( ) of unsaturated soil. GPR method proceeds by parameterising a covariance function, and then infers the parameters given the data set. Input variables of GPR are net confining pressure ( ), saturated volumetric water content ( ), residual water content ( ), bubbling pressure ( ), suction ( ) and fitting parameter ( ). A comparative study has been carried out between the developed GPR and Artificial Neural Network (ANN) models. A sensitivity analysis has been done to determine the effect of each input parameter on . The developed GPR gives the variance of predicted . The results show that the developed GPR is reliable model for prediction of of unsaturated soil.

关键词： unsaturated soil     effective stress parameter     Gaussian process regression (GPR)     artificial neural network (ANN)     variance    

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Multiple linear regression, artificial neural network, and fuzzy logic prediction of 28 days compressive

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

摘要： Evaluating the in situ concrete compressive strength by means of cores cut from hardened concrete is acknowledged as the most ordinary method, however, it is very difficult to predict the compressive strength of concrete since it is affected by many factors such as different mix designs, methods of mixing, curing conditions, compaction, etc. In this paper, considering the experimental results, three different models of multiple linear regression model (MLR), artificial neural network (ANN), and adaptive neuro-fuzzy inference system (ANFIS) are established, trained, and tested within the Matlab programming environment for predicting the 28 days compressive strength of concrete with 173 different mix designs. Finally, these three models are compared with each other and resulted in the fact that ANN and ANFIS models enables us to reliably evaluate the compressive strength of concrete with different mix designs, however, multiple linear regression model is not feasible enough in this area because of nonlinear relationship between the concrete mix parameters. Finally, the sensitivity analysis (SA) for two different sets of parameters on the concrete compressive strength prediction are carried out.

关键词： concrete     28 days compressive strength     multiple linear regression     artificial neural network     ANFIS     sensitivity analysis (SA)    

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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

摘要： The purpose of this study is the accurate prediction of undrained shear strength using Standard Penetration Test results and soil consistency indices, such as water content and Atterberg limits. With this study, along with the conventional methods of simple and multiple linear regression models, three machine learning algorithms, random forest, gradient boosting and stacked models, are developed for prediction of undrained shear strength. These models are employed on a relatively large data set from different projects around Turkey covering 230 observations. As an improvement over the available studies in literature, this study utilizes correct statistical analyses techniques on a relatively large database, such as using a train/test split on the data set to avoid overfitting of the developed models. Furthermore, the validity and consistency of the prediction results are ensured with the correct use of statistical measures like -value and cross-validation which were missing in previous studies. To compare the performances of the models developed in this study with the prior ones existing in literature, all models were applied on the test data set and their performances are evaluated in terms of the resulting root mean squared error ( ) values and coefficient of determination ( ). Accordingly, the models developed in this study demonstrate superior prediction capabilities compared to all of the prior studies. Moreover, to facilitate the use of machine learning algorithms for prediction purposes, entire source code prepared for this study and the collected data set are provided as supplements of this study.

关键词： undrained shear strength     linear regression     random forest     gradient boosting     machine learning     standard penetration test    

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New nonlinear stiffness actuator with predefined torque‒deflection profile

《机械工程前沿（英文）》 2023年 第18卷 第1期 doi: 10.1007/s11465-022-0721-3

摘要： A nonlinear stiffness actuator (NSA) could achieve high torque/force resolution in low stiffness range and high bandwidth in high stiffness range, both of which are beneficial for physical interaction between a robot and the environment. Currently, most of NSAs are complex and hardly used for engineering. In this paper, oriented to engineering applications, a new simple NSA was proposed, mainly including leaf springs and especially designed cams, which could perform a predefined relationship between torque and deflection. The new NSA has a compact structure, and it is lightweight, both of which are also beneficial for its practical application. An analytical methodology that maps the predefined relationship between torque and deflection to the profile of the cam was developed. The optimal parameters of the structure were given by analyzing the weight of the NSA and the mechanic characteristic of the leaf spring. Though sliding friction force is inevitable because no rollers were used in the cam-based mechanism, the sliding displacement between the cam and the leaf spring is very small, and consumption of sliding friction force is very low. Simulations of different torque‒deflection profiles were carried out to verify the accuracy and applicability of performing predefined torque‒deflection profiles. Three kinds of prototype experiments, including verification experiment of the predefined torque‒deflection profile, torque tracking experiment, and position tracking experiment under different loads, were conducted. The results prove the accuracy of performing the predefined torque‒deflection profile, the tracking performance, and the interactive performance of the new NSA.

关键词： compliant actuator     nonlinear stiffness actuator     nonlinear spring     predefined torque−deflection profile    

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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

摘要： In this study, the relationship between space mean speed (SMS), flow rate and density of pedestrians was investigated in different pedestrian facilities, including 1 walkway, 2 sidewalks, 2 signalized crosswalks and 2 mid-block crosswalks. First, statistical analysis was performed to investigate the normality of data and correlation of variables. Regression analysis was then applied to determine the relationship between SMS, flow rate, and density of pedestrians. Finally, two prediction models of density were obtained using genetic programming (GP) and group method of data handling (GMDH) models, and k-fold and holdout cross-validation methods were used to evaluate the models. By the use of regression analysis, the mathematical relationships between variables in all facilities were calculated and plotted, and the best relationships were observed in flow rate-density diagrams. Results also indicated that GP had a higher R2 than GMDH in the prediction of pedestrian density in terms of flow rate and SMS, suggesting that GP was better able to model SMS and pedestrian density. Moreover, the application of k-fold cross-validation method in the models led to better performances compared to the holdout cross-validation method, which shows that the prediction models using k-fold were more reliable. Finally, density relationships in all facilities were obtained in terms of SMS and flow rate.

关键词： pedestrian density     regression analysis     GP model     GMDH model    

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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

《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

摘要： A method was proposed to identify the main influence factors of soil heavy metals. The influence degree of different environmental factors was ranked. Parent material, soil type, land use and industrial activity were main factors. Interactions between some factors obviously affected soil heavy metal distribution. Identifying the factors that influence the heavy metal contents of soil could reveal the sources of soil heavy metal pollution. In this study, a categorical regression was used to identify the factors that influence soil heavy metals. First, environmental factors were associated with soil heavy metal data, and then, the degree of influence of different factors on the soil heavy metal contents in Beijing was analyzed using a categorical regression. The results showed that the soil parent material, soil type, land use type, and industrial activity were the main influencing factors, which suggested that these four factors were important sources of soil heavy metals in Beijing. In addition, population density had a certain influence on the soil Pb and Zn contents. The distribution of soil As, Cd, Pb, and Zn was markedly influenced by interactions, such as traffic activity and land use type, industrial activity and population density. The spatial distribution of soil heavy metal hotspots corresponded well with the influencing factors, such as industrial activity, population density, and soil parent material. In this study, the main factors affecting soil heavy metals were identified, and the degree of their influence was ranked. A categorical regression represents a suitable method for identifying the factors that influence soil heavy metal contents and could be used to study the genetic process of regional soil heavy metal pollution.

关键词： Soil     Heavy metal     Influencing factor     Categorical regression     Identification method    

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presence of different random noises and uncertainty: Implementation of generalized Gaussian process regression

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

摘要：

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    

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标题 作者 时间 类型 操作

Interaction behavior and load sharing pattern of piled raft using nonlinear regression and LM algorithm-based

期刊论文

非线性计数系统的关键因子辨识方法

张新民,王静波,魏驰航,宋执环

期刊论文

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

基于核空间非线性特征提取的图像质量评价方法

Yong DING,Nan LI,Yang ZHAO,Kai HUANG

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

New nonlinear stiffness actuator with predefined torque‒deflection profile

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