检索结果-内蒙古大学图书馆

Settlement prediction of a high embankment based on non-linear regression and neural network algorithm

TRANSPORTATION GEOTECHNICS 2025年 50卷

作者： Yang, Enhui Wang, Kai He, Jinhong Liu, Kaiwen Wang, Junxin Zhang, Haopeng Qiu, Yanjun Southwest Jiaotong Univ Sch Civil Engn Chengdu 610031 Peoples R China Sichuan Zhenguang Expressway CO LTD Chengdu 610041 Peoples R China Highway Engn Lab Sichuan Prov Chengdu 610031 Sichuan Peoples R China Minist Educ Key Lab High Speed Railway Engn Chengdu 610031 Peoples R China

This study reports in-situ measured data from single-point settlement meters buried in layers at the road shoulders and the center of a high embankment respectively. Based on the measured data, the nonlinear fitting models and neural network algorithm were proposed to establish the method of settlement prediction, and the mean absolute percentage error (MAPE) and mean square errors (MSE) were used to evaluate the accuracy of the settlement prediction model. The results show that the MAPE value of the exponential curve prediction model is less than 10%, and the MAPE value of the hyperbolic model is between 10% and 20%, and the MAPE value of the logarithmic model is greater than 20%, which shows that the exponential curve model of the three nonlinear fitting models has the highest accuracy. After establishing the back propagation (BP) neural network model, the settlement data of the four monitoring points were learned and trained, and the prediction accuracy of the two BP neural network prediction models and the exponential curve prediction model were compared. The model fitting coefficient of R 2 of BP neural network were both greater than 0.99, and the MSE and MAPE were less than 1%. In addition, the multi-step rolling BP neural network prediction model has the highest prediction accuracy, followed by the BP neural network prediction model based on influencing factors while the exponential curve prediction model has the worst performance and weak practicability. This research can provide new inspiration for embankment settlement prediction and give technical support to monitor the disaster of high embankment.

关键词： High embankment Settlement prediction On-site measurement non-linear regression Neural network algorithm

来源：评论

学校读者我要写书评

暂无评论

Prediction of the undrained shear strength of remolded soil with non-linear regression,fuzzy logic,and artificial neural network

引用

Journal of Mountain Science 2024年第9期21卷 3108-3122页

作者： YÜNKÜL Kaan KARAÇOR Fatih GÜRBÜZ Ayhan BUDAK TahsinÖmür Department of Civil Engineering Faculty of Engineering and ArchitectureTokat Gaziosmanpaşa UniversityTokat 60100Türkiye Department of Civil Engineering Faculty of Engineering and ArchitectureKafkas UniversityKars 36000Türkiye Department of Civil Engineering Faculty of EngineeringGazi UniversityAnkara 06570Türkiye Ministry of Youth and Sports Ankara 06090Türkiye

This study aims to predict the undrained shear strength of remolded soil samples using non-linear regression analyses,fuzzy logic,and artificial neural network modeling.A total of 1306 undrained shear strength results from 230 different remolded soil test settings reported in 21 publications were collected,utilizing six different measurement *** water content,plastic limit,and liquid limit were used as input parameters for fuzzy logic and artificial neural network modeling,liquidity index or water content ratio was considered as an input parameter for non-linear regression *** non-linear regression analyses,12 different regression equations were derived for the prediction of undrained shear strength of remolded ***-Forward backpropagation and the TANSIG transfer function were used for artificial neural network modeling,while the Mamdani inference system was preferred with trapezoidal and triangular membership functions for fuzzy logic *** experimental results of 914 tests were used for training of the artificial neural network models,196 for validation and 196 for *** was observed that the accuracy of the artificial neural network and fuzzy logic modeling was higher than that of the non-linear regression ***,a simple and reliable regression equation was proposed for assessments of undrained shear strength values with higher coefficients of determination.

关键词： Undrained shear strength Liquidity index Water content ratio non-linear regression Artificial neural networks Fuzzy logic

来源：评论

学校读者我要写书评

暂无评论

Towards non-linear regression-based prediction of use case point (UCP) metric

引用

APPLIED INTELLIGENCE 2023年第9期53卷 10326-10339页

作者： Shukla, Suyash Kumar, Sandeep Indian Inst Technol Roorkee Comp Sci & Engn Dept Roorkee 247667 Uttarakhand India

Software Effort Estimation (SEE) is a procedure to estimate the effort required to develop software. The researchers have been dealing with SEE issues for a long time. Several methods were developed until the formulation of Function Point (FP) and Constructive Cost Estimation (COCOMO) methods. However, these methods were useful only for procedurally developed software, not for modern object-oriented software. On the other hand, using the Use Case Point (UCP) metric acquired from the UML diagrams can be more suitable, as the use case is the fundamental unit of an object-oriented system. An ample amount of research has already been done for UCP prediction using linear regression-based models. However, various nonlinear regression models have not been explored for predicting UCP values from different UCP parameters. Although, some of the researchers have used nonlinear regression models for predicting effort, given the UCP value. Motivated by this, the current work investigates different nonlinear regression models such as a k-nearest neighbor, decision tree, random forest, support vector machine, and multilayer perceptron for UCP prediction. The experimental investigation has been conducted on two publicly available UCP estimation datasets. Further, we compared the performance of nonlinear regression models with the linear regression-based models using different performance measures. The results suggest that the nonlinear regression models perform better than the linear regression-based models.

关键词： Object-oriented software Use case point linear regression non-linear regression

来源：评论

学校读者我要写书评

暂无评论

Modeling hydraulic fracture fluid efficiency in tight gas reservoirs using non-linear regression and a back-propagation neural network

引用

GEOSYSTEM ENGINEERING 2023年第1期26卷 12-29页

作者： Khouli, Rami Garrouch, Ali. A. Alruhaimani, Feras Kuwait Univ Petr Engn Dept Shidadiya Kuwait Kuwait Univ Petr Engn Dept POB 5969 Safat 13060 Kuwait

This study introduces a back-propagation (BP) neural network model and a regression model for estimating the fracture fluid efficiency based on a data set consisting of 1261 staged and ramped simulation runs of tight gas reservoirs subjected to hydraulic fracturing treatment. Data were generated using a 3-D commercial simulator which is a versatile software portfolio that models many well configurations, proppant placement, and fracture geometries. The BP network inputs consist of shear rate/fracture conductivity ratio, the injection rate, reservoir permeability, formation closure stress, reservoir thickness, effective viscosity, and fracture height. The neural network model was able to generate satisfactory estimates of the fracture fluid efficiency for the training dataset, and for the blind testing data. An average error of approximately 2.5% was obtained for the training set, and an average error of 3% was obtained for the testing set. An empirical non-linear regression model has been constructed based on dimensionless groups derived by applying dimensional analysis to a set of variables consisting of the maximum fracture width, fracture length, fracture height, effective viscosity, shear rate/fracture conductivity ratio, reservoir thickness, injection rate, reservoir permeability, and formation closure stress. The average error for estimating the fluid efficiency using the non-linear regression empirical model was approximately 6.17%. Since the non-linear regression model has an explicit formulation, it is easier to apply than the neural network model. The empirical regression model estimates of the fluid efficiency appeared to be unbiased and were more precise than those estimates obtained using either the KGD or the PKN 2-D models. The introduced BP model and the non-linear regression model offer fast and inexpensive alternatives to the application of three-dimensional simulators for estimating the fluid efficiency.

关键词： Hydraulic fracture fluid efficiency dimensional analysis tight gas reservoirs artificial neural network back-propagation non-linear regression

来源：评论

学校读者我要写书评

暂无评论

linear and non-linear regression analysis for the sorption kinetics of Tropaeoline 000 onto Nano Talc

引用

INTERNATIONAL JOURNAL OF ENVIRONMENTAL ANALYTICAL CHEMISTRY 2022年第9期102卷 2152-2167页

作者： Sharma, Pooja Jain, Rajeev Premi, Apresh Kumar, Neeraj Parashar, Arvind Jiwaji Univ Dept Chem Gwalior India Pondicherry Univ Dept Chem Pondicherry India

A comparison of linear least-squares method and a trial and error non-linear method of three widely used isotherms, Langmuir, Freundlich, and Temkin, were examined. The batch sorption model, based on a pseudo-second-order mechanism, was applied to predict the rate constant of sorption, the equilibrium capacity and the initial sorption rate with the effects of the initial solution pH (4.5) and adsorbent dose. The calculated adsorption capacity (q(e)) was q(e) = 97.6 mg/g. Langmuir isotherm parameters obtained from the Langmuir linear equations were used to fit the adsorption equilibrium data. The adsorption capacity of the adsorbent, obtained from the Langmuir model, was up to 1.78 mol/g(-1). In order to optimise the adsorption-isotherm model, correlation coefficient (R-2) of 0.991 and 0.841 error functions were employed to facilitate the evaluation of fitting accuracy.

关键词： Tropoaeoline 000 linear regression non-linear regression pseudo-second-order kinetics nano talc

来源：评论

学校读者我要写书评

暂无评论

Distributed sparsity-based non-linear regression with multiple kernels in wireless sensor networks

引用

AD HOC NETWORKS 2022年 125卷

作者： Abbasabad, Reza Aghaie Azghani, Masoumeh Sahand Univ Technol Fac Elect Engn Lab Wireless Commun & Signal Proc WCSP Tabriz Iran

In this paper, we investigate the problem of non-linear time-varying regression in a wireless sensor network system. A field over an area is estimated using a number of fixed sensors distributed randomly in the region. Two regression schemes have been suggested: one for centralized regression and the other for distributed regression. The proposed method exploits the reweighted multiple kernel least squares as the data fidelity term. Moreover, using the idea that not all of the sensors affect the field value in a specific region point, we add the sparsity constraint on the weighting vector to obtain a better estimation of the field. Another TV-norm term is added to the total cost function to ensure the smoothness of the estimated field. The consensus-based term is adopted to develop a distributed estimation technique. In addition, the bandwidth of the kernels are defined to change adaptively over time using a least mean square (LMS) technique which enables the algorithm to track the time-variations of the under-lying field. The simulation results confirm the superiority of the proposed method over its counterparts in both synthetic and real data.

关键词： Sparsity Wireless sensor network non-linear regression Multiple kernels Time-varying regression

来源：评论

学校读者我要写书评

暂无评论

Uncertainty analysis of linear and non-linear regression models in the modeling of water quality in the Caspian Sea basin: Application of Monte-Carlo method

引用

ECOLOGICAL INDICATORS 2025年 170卷

作者： Masteali, Sahar Heidari Bayat, Mahmoud Bettinger, Pete Ghorbanpour, Mansour Univ Tehran Fac Nat Resources Dept Environm Tehran Iran Educ & Extens Org AREEO Res Inst Forests & Rangelands Agr Res Tehran Iran Univ Georgia Sch Forestry & Nat Resources Athens GA 30602 USA Arak Univ Fac Agr & Nat Resources Dept Med Plants Arak *** Iran

The assessment of uncertainty in water quality prediction models is crucial for understanding the reliability of these models. This research focuses on the southern basin area of the Caspian Sea in Iran, where there have been limited studies on uncertainty analysis in water quality models. First, a forest layer was extracted from land cover map and 25 large watersheds were selected. River water quality was then assessed from the perspective of 8 landscape connectivity metrics and 12 graph theory indices. We developed predictive models using stepwise linear regression, power, exponential, and logarithmic models to locate the best model form for each water quality parameter (dependent variable) we examined. The goal of this study was to analyze the uncertainty in both linear and non-linear water quality regression models for this region. The researchers used a Monte Carlo simulation method, which is a common approach to investigate uncertainty. First, they determined the best distribution of the independent variables in the regression models using the Kolmogorov-Smirnov goodness of fit test. Then, they generated 15,000 random data points based on the characteristics of the identified variable distributions. In the third stage, new regression models were created using the data from the previous stages. Probability density functions related to the output of the models were developed, and uncertainty analysis was conducted based on the behavior of the models. The results indicated that the CO3 model had the lowest uncertainty bandwidth and the highest R2 among other models. Additionally, the K models had less bandwidth than the other models developed. The research results showed that more than 70% of the models produced results within an acceptable range of uncertainty. Furthermore, the normalized RMSE (NRMSE) values associated with the models were all in the range of 0 to 30%, which is considered acceptable. These outcomes suggest that landscape indicators and graph the

关键词： Uncertainty linear regression non-linear regression Landscape Water quality

来源：评论

学校读者我要写书评

暂无评论

Application of hybrid ANFIS-based non-linear regression modeling to predict the %oil yield from grape peels: Effect of process parameters and FIS generation techniques

引用

CLEANER ENGINEERING AND TECHNOLOGY 2022年 6卷

作者： Olatunji, Ololade Moses Horsfall, Ibiba Taiwo Ukoha-Onuoha, Erewari Osa-Aria, Keavey Akwa Ibom State Univ Dept Agr Engn Ikot Akpen Akwa Ibom State Nigeria Michael Okpara Univ Agr Dept Agr Bioresources Engn PMB 7267 Umudike Abia State Nigeria IBIBATH MULTI Serv Dept Environm Engn Port Harcourt Nigeria Rivers State Univ Dept Agr & Environm Engn Port Harcourt Nigeria Michael Okpara Univ Agr Dept Mech Engn PMB 7267 Umudike Abia State Nigeria

The present study evaluates the extraction process parameters of grape peel oil using non-linear hybrid Adaptive Neuro-Fuzzy Inference System (ANFIS) model. The experiments were conducted at temperature (80-100 degrees C), and time (5-9 h) at 5 levels with output parameter as oil yield. The experimental result and factor interaction analysis shows that temperature had the most significant effect on the oil yield. The oil yield estimation performance indicators for the Fuzzy Inference (FIS) generation techniques are: Fuzzy C-means Clustering (FCM), [R-2 = 0.9102, MSE = 0.278, RMSE = 0.527], Subtractive Clustering (SC), [R-2 = 0.9090, MSE = 0.300, RMSE = 0.548] and Grid Partitioning (GP), [R-2 = 0.827, MSE = 0.668, RMSE = 0.817]. This indicates that Fuzzy C-means FIS generation fits better than Subtractive Clustering and Grid Partitioning techniques. For future research, a substantial volume of data is recommended for successful ANFIS training.

关键词： Hybrid ANFIS non-linear regression Maximum oil yield Soxhlet extraction Grape peels RSM design

来源：评论

学校读者我要写书评

暂无评论

A comparative study of 11 non-linear regression models highlighting autoencoder, DBN, and SVR, enhanced by SHAP importance analysis in soybean branching prediction

引用

SCIENTIFIC REPORTS 2024年第1期14卷 1-16页

作者： Zhou, Wei Yan, Zhengxiao Zhang, Liting Florida A&M Univ Tallahassee FL 32307 USA Florida State Univ Tallahassee FL 32306 USA

To explore a robust tool for advancing digital breeding practices through an artificial intelligence-driven phenotype prediction expert system, we undertook a thorough analysis of 11 non-linear regression models. Our investigation specifically emphasized the significance of Support Vector regression (SVR) and SHapley Additive exPlanations (SHAP) in predicting soybean branching. By using branching data (phenotype) of 1918 soybean accessions and 42 k SNP (Single Nucleotide Polymorphism) polymorphic data (genotype), this study systematically compared 11 non-linear regression AI models, including four deep learning models (DBN (deep belief network) regression, ANN (artificial neural network) regression, Autoencoders regression, and MLP (multilayer perceptron) regression) and seven machine learning models (e.g., SVR (support vector regression), XGBoost (eXtreme Gradient Boosting) regression, Random Forest regression, LightGBM regression, GPs (Gaussian processes) regression, Decision Tree regression, and Polynomial regression). After being evaluated by four valuation metrics: R2 (R-squared), MAE (Mean Absolute Error), MSE (Mean Squared Error), and MAPE (Mean Absolute Percentage Error), it was found that the SVR, Polynomial regression, DBN, and Autoencoder outperformed other models and could obtain a better prediction accuracy when they were used for phenotype prediction. In the assessment of deep learning approaches, we exemplified the SVR model, conducting analyses on feature importance and gene ontology (GO) enrichment to provide comprehensive support. After comprehensively comparing four feature importance algorithms, no notable distinction was observed in the feature importance ranking scores across the four algorithms, namely Variable Ranking, Permutation, SHAP, and Correlation Matrix, but the SHAP value could provide rich information on genes with negative contributions, and SHAP importance was chosen for feature selection. The results of this study offer valuable i

关键词： Phenotype prediction non-linear regression Artificial intelligence Feature importance

来源：评论

学校读者我要写书评

暂无评论

Genetic algorithm-based threshold voltage prediction of SOI JLT using multi-variable non-linear regression 4

Genetic algorithm-based threshold voltage prediction of SOI ...

引用

4th International Symposium on Devices, Circuits and Systems (ISDCS)

作者： Moparthi, Sandeep Tiwari, Pramod Kumar Saramekala, Gopi Krishna NIT Calicut Dept ECE Kozhikode 673601 Kerala India IIT Patna Dept EE Patna 801103 Bihar India

ISBN: (纸本)9781665414784

A machine learning approach of multivariable non-linear regression with six input variables is used for the first time to assess the process variability impact on the threshold voltage (Vth) of silicon-on-insulator (SOI) junctionless transistor (JLT). The impact of source/drain electrode spacing (L-sD) is considered as one of the input variables in this analysis. The genetic algorithm is implemented in MATLAB and used for the optimization of the hypothesis. A dataset of 2000 samples is used for training and testing. More than 75% of the test samples offered a prediction error in the range of +/- 0.0125V despite nonlinearity caused by six input parameter variations. Average prediction accuracy of over 96 percent is achieved with efficient algorithm training and well-organized dataset.

关键词： SOI JLT threshold voltage non-linear regression hypothesis genetic algorithm

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：