检索结果-内蒙古大学图书馆

An integrated model for predicting the size of silver nanoparticles in montmorillonite/chitosan bionanocomposites: A hybrid of data envelopment analysis and genetic programming approach

引用

SCIENTIA IRANICA 2021年第3期28卷 1871-1883页

作者： Sarvestani, E. Khayati, G. R. Shahid Bahonar Univ Kerman Dept Mat Sci & Engn POB 76135-188 Kerman Iran

Unique chemical and physical properties of silver nanoparticles (AgNPs) contribute to the broader scope of their applications in different fields including medical utilities. Considering the high dependency of AgNPs properties on their size, this study employed gene expression programming (GEP) to develop a quantitative model for estimating the size of AgNPs in montmorillonite/chitosan bionanocomposites prepared by the chemical approach. generalization capabilities, fault tolerance, noise tolerance, high parallelism, nonlinearity, and significant information processing characteristics are the main advantages of GEP. Accordingly, the practical parameters including reaction temperature, AgNO3 concentration, weight of montmorillonite in aqueous AgNO3/chitosan solution (WMMT), and percentage of chitosan were the input parameters selected through GEP modeling. The accuracy of the proposed models was investigated based on statistical indicators including Mean Absolute Percentage Error (MAPE), Root Relative Squared Error (RRSE), Root Mean Square Error (RMSE), and correlation coefficient (R-2). Finally, the best model was selected by R-2 = 0.987, RMSE = 0.100, RRSE = 0.146, and MAPE = 0.221. The sensitivity analysis confirmed that the percentage of chitosan, concentration of AgNO3, W-MMT. and reaction temperature were the most effective parameters for the size of AgNPs. (C) 2021 Sharif University of Technology. All rights reserved.

关键词： Silver nanopazticle gene expression programming Montmorillonite Chitosan Bionanocomposites Sensitivity analysis

来源：评论

学校读者我要写书评

暂无评论

A new model for prediction of soil thermal conductivity

引用

INTERNATIONAL COMMUNICATIONS IN HEAT AND MASS TRANSFER 2021年 129卷

作者： Zhang, Ruiliang Xue, Xinhua Sichuan Univ Coll Water Resource & Hydropower State Key Lab Hydraul & Mt River Engn Chengdu 610065 Peoples R China

This study presents a gene expression programming (GEP) method for determining soil thermal conductivity (k). A large and reliable database containing 444 thermal conductivity measurements was collected and employed to develop the GEP model. Four governing parameters (i.e., porosity, saturation degree, quartz content and temperature) were selected as inputs based on a Pearson correlation analysis. Through the determination of the optimal parameter setting of GEP, a predictive equation was first proposed. Then, the proposed GEP model was evaluated and compared with several previous models. The results indicate the proposed GEP model has higher prediction precision than the other models. In addition, a monotonicity analysis was performed to validate the rationality of the proposed GEP model. According to the monotonicity analysis results, the soil thermal conductivity increases with the increase of saturation degree, quartz content and temperature, and it decreases with the increasing of the porosity. Finally, a parametric analysis was conducted to investigate the significance of the inputs. The results show that the effect rank of these four input variables on soil thermal conductivity is saturation degree > porosity > temperature > quartz content, which can provide a reference for similar projects.

关键词： Thermal conductivity Unsaturated soil gene expression programming Heat transfer

来源：评论

学校读者我要写书评

暂无评论

GEP-based classifier for mining imbalanced data

引用

EXPERT SYSTEMS WITH APPLICATIONS 2021年 164卷 114058-114058页

作者： Jedrzejowicz, Joanna Jedrzejowicz, Piotr Univ Gdansk Inst Informat Fac Math Phys & Informat PL-80308 Gdansk Poland Gdynia Maritime Univ Dept Informat Syst Morska 83 PL-81225 Gdynia Poland

The paper proposes an incremental gene expression programming classifier for mining imbalanced datasets. Imbalanced datasets are commonly encountered in real-life applications. There exist numerous algorithms, techniques, and tools which are proposed as suitable for dealing with imbalanced class distribution. Yet, none of them seems to be able to outperform all others in all possible applications. We believe that our approach can extend the available range of learners that have proven good performance in mining imbalanced data and imbalanced streams. The idea is to adapt the GEP classifier to requirements of the imbalanced data environment with reuse of the minority class instances, and application of the incremental learning paradigm. The paper offers an overview of the related work and a detailed description of the proposed incremental learner. An extensive computational experiment, based on data from the KEEL dataset repository, proves that in numerous cases the approach is competitive to other state-of-the-art learners.

关键词： Imbalanced classification Incremental learning gene expression programming

来源：评论

学校读者我要写书评

暂无评论

Spectral acceleration prediction using genetic programming based approaches

引用

APPLIED SOFT COMPUTING 2021年 106卷 107326-107326页

作者： Gandomi, Mostafa Kashani, Ali R. Farhadi, Ali Akhani, Mohsen Gandomi, Amir H. Univ Tehran Dept Civil Engn Tehran Iran Univ Memphis Dept Civil Engn Memphis TN USA Univ Technol Sydney Fac Engn & IT Ultimo Australia

Evolutionary computation (EC) is a widely used computational intelligence that facilitates the formulation of a range of complex engineering problems. This study tackled two hybrid EC techniques based on genetic programming (GP) for ground motion prediction equations (GMPEs). The first method coupled regression analysis with multi-objective genetic programming. In this way, the strategy was maximizing the accuracy and minimizing the models' complexity simultaneously. The second approach incorporated mesh adaptive direct search (MADS) into gene expression programming to optimize the obtained coefficients. A big data set provided by the Pacific Earthquake Engineering Research Centre (PEER) was used for the model development. Two explicit formulations were developed during this effort. In those formulae, we correlated spectral acceleration to a set of seismological parameters, including the period of vibration, magnitude, the closest distance to the fault ruptured area, shear wave velocity averaged over the top 30 meters, and style of faulting. The GP-based models are verified by a comprehensive comparison with the most well-known methods for GMPEs. The results show that the proposed models are quite simple and straightforward. The high degrees of accuracy of the predictions are competitive with the NGA complex models. Correlations of the predicted data using GEP-MADs and MOGP-R models with the real observations seem to be better than those available in the literature. Three statistical measures for GMPEs, such as E (%), LLH, and EDR index, confirmed those observations. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Spectral acceleration Ground-motion models Multi-gene genetic programming gene expression programming Multi-objective genetic programming

来源：评论

学校读者我要写书评

暂无评论

Accurate performance prediction model for impact hammer developed using customized evolutionary algorithm

引用

TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY 2021年 109卷 103773-103773页

作者： Hojjati, Shahabedin Tumac, Deniz Jeon, Seokwon Seoul Natl Univ Dept Energy Syst Engn 1 Gwanak Ro Seoul 08826 South Korea Istanbul Tech Univ Dept Min Engn TR-34469 Istanbul Turkey

In past decades, impact hammers have played a key role in underground construction. Simply in Istanbul, impact hammers have been used to excavate more than 20 km of metro tunnels. Thus, determining the instantaneous breaking rate (IBR) of an impact hammer is attracting increasing attention. A number of IBR prediction models have been developed for impact hammers. However, there is still a demand for models that require a smaller number of easy-to-obtain rock properties as inputs and provide a reasonable level of accuracy, a wide range of applications, and high reliability. This study had the goal of developing such a prediction model based on an investigation of two subway tunnels built in Istanbul. In order to enhance the results generated by multiple linear regression analysis, a customized tool was developed for the non-linear analysis of a relatively large set of data collected for the present research. gene expression programming (GEP) and particle swarm optimization (PSO) were merged to create a non-linear analysis tool. The GEP-PSO algorithm was trained using 80% of the available data, with the remaining 20% reserved to validate the results. The output of the algorithm was presented in the form of a mathematical equation that predicted the IBR using the uniaxial compressive strength, rock quality designation, Schmidt hammer rebound value, and machine power as input parameters. The predicted IBR values were in remarkable agreement with the recorded values. In order to verify the efficiency of the proposed prediction model, it was successfully tested against previously developed models for which input parameters were available. In addition, the proposed model was investigated under hypothetical circumstances to ensure that it legitimately described the performance changes due to changes in the input parameters. The model developed in this research is proposed as an accurate and reliable tool for predicting the performance of impact hammers over a wide applicati

关键词： Tunnel construction Impact hammer Performance prediction gene expression programming Particle swarm optimization Rock properties

来源：评论

学校读者我要写书评

暂无评论

Application of Novel Machine Learning Techniques for Predicting the Surface Chloride Concentration in Concrete Containing Waste Material

引用

MATERIALS 2021年第9期14卷 2297页

作者： Ahmad, Ayaz Farooq, Furqan Ostrowski, Krzysztof Adam Sliwa-Wieczorek, Klaudia Czarnecki, Slawomir COMSATS Univ Islamabad Dept Civil Engn Abbottabad Campus Islamabad 22060 Pakistan Cracow Univ Technol Fac Civil Engn 24 Warszawska Str PL-31155 Krakow Poland Wroclaw Univ Sci & Technol Fac Civil Engn Wybrzeze Wyspianskiego 27 PL-50370 Wroclaw Poland

Structures located on the coast are subjected to the long-term influence of chloride ions, which cause the corrosion of steel reinforcements in concrete elements. This corrosion severely affects the performance of the elements and may shorten the lifespan of an entire structure. Even though experimental activities in laboratories might be a solution, they may also be problematic due to time and costs. Thus, the application of individual machine learning (ML) techniques has been investigated to predict surface chloride concentrations (C-c) in marine structures. For this purpose, the values of C-c in tidal, splash, and submerged zones were collected from an extensive literature survey and incorporated into the article. gene expression programming (GEP), the decision tree (DT), and an artificial neural network (ANN) were used to predict the surface chloride concentrations, and the most accurate algorithm was then selected. The GEP model was the most accurate when compared to ANN and DT, which was confirmed by the high accuracy level of the K-fold cross-validation and linear correlation coefficient (R2), mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE) parameters. As is shown in the article, the proposed method is an effective and accurate way to predict the surface chloride concentration without the inconveniences of laboratory tests.

关键词： surface chloride concentration individual algorithm aggressive ions environment gene expression programming concrete artificial neural networks

来源：评论

学校读者我要写书评

暂无评论

Prediction of discharge flow rate beneath sheet piles using scaled boundary finite element modeling database

引用

SCIENTIA IRANICA 2021年第2期28卷 645-655页

作者： Johari, A. Heydari, A. Talebi, A. Shiraz Univ Technol Dept Civil & Environm Engn POB 71557-13876 Shiraz Iran

Sheet piles are of general water-retaining structures. The rate of discharge flow beneath sheet plies is an important parameter in the design of these structures. In this study, gene expression programming (GEP) as an artificial intelligence method is used for developing a model to predict the discharge flow rate. The input parameters include the sheet pile height, upstream head, and hydraulic conductivity anisotropy ratio. In order to achieve better performance, the flow rate is normalized and selected as an output of the model. A database including 1000 cases created from the Scaled Boundary Finite Element Method (SBFEM) for the seepage beneath sheet plies is employed to develop the model. The GEP-based model predictions are shown in reasonable agreement with the simulated data, which indicates the efficiency of the developed model. The results of the sensitivity analysis indicate that the upstream head is the most influential parameter in the discharge flow rate beneath the sheet piles. Furthermore, the outputs of the parametric analysis show the reasonable performance of the model in the prediction of normalized discharge flow rate. (C) 2021 Sharif University of Technology. All rights reserved.

关键词： Seepage Scaled boundary finite-element method Flow rate discharge Sheet pile gene expression programming

来源：评论

学校读者我要写书评

暂无评论

Evolutionary artificial intelligence approach for performance prediction of bio-composites

引用

CONSTRUCTION AND BUILDING MATERIALS 2021年 290卷 123254-123254页

作者： Ahmad, Muhammad Riaz Chen, Bing Dai, Jian-Guo Kazmi, Syed Minhaj Saleem Munir, Muhammad Junaid Shanghai Jiao Tong Univ State Key Lab Ocean Engn Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Dept Civil Engn Shanghai 200240 Peoples R China Hong Kong Polytech Univ Dept Civil & Environm Engn Hong Kong Peoples R China RMIT Univ Sch Engn 376-392 Swanston St Melbourne Vic 3001 Australia

Giving the high amount of carbon and energy emission from the use of traditional building materials, the use of bio-composites made from industrial crops especially hemp has caught attention from researchers in recent years. These bio-composites not only enhance the thermal performance of buildings but also promote sustainable development due to their eco-friendly nature. Due to their highly heterogeneous nature, however, most of the existing studies on the bio-composites have only focused on experimental investigations, while mathematical modeling of physical, thermal and mechanical properties of biocomposite remains a challenge for the researchers. In this paper, an artificial intelligence (AI) based gene expression programming (GEP) technique is used to develop the mathematical models for predicting the dry density, compressive strength and thermal conductivity of hemp-based bio-composites. A large amount of database was established based on past studies and the most influential parameters were identified by several trial analyses. The proposed mathematical models showed a high correlation with the experimental results. All the models passed the statistical and performance index checks showing strong predictability, generalization capability and high accuracy of GEP-AI models. Comparison of results with the regression analysis techniques further proved the superiority of GEP-AI models over the traditional methods. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： gene expression programming Bio-composite Thermal conductivity Compressive strength Mathematical modeling

来源：评论

学校读者我要写书评

暂无评论

Hybrid Methods of GMDH-Neural Networks Synthesis and Training for Solving Problems of Time Series Forecasting 15th

Hybrid Methods of GMDH-Neural Networks Synthesis and Trainin...

引用

15th International Scientific Conference on Intellectual Systems of Decision Making and Problems of Computational Intelligence (ISDMCI)

作者： Lytvynenko, Volodymyr Wojcik, Waldemar Fefelov, Andrey Lurie, Iryna Savina, Nataliia Voronenko, Mariia Boskin, Oleg Smailova, Saule Kherson Natl Tech Univ Kherson Ukraine Lublin Univ Technol Lublin Poland Natl Univ Water & Environm Engn Rivne Ukraine D Serikbayev East Kazakhstan State Tech Univ Ust Kamenogorsk Kazakhstan

ISBN: (纸本)9783030264741

In this paper, for solving the problem of forecasting non-stationary time series, hybrid learning methods for GMDH-neural networks are proposed. Training methods combine artificial immune systems with members of the evolutionary algorithm family, in particular, gene expression programming systems. The following hybrid computational methods for the synthesis and training of GMDH-neural networks have been developed: a method in which candidate models are represented as gene expression, and training is performed by clonal selection;the method of two-phase structural-parametric synthesis, in which the structural component is formed by programming the expression of genes, and the parameterization is performed by clonal selection;a method based on cooperative-competitive processes of interaction of the elements of the immune system, in which the structure and parameters of the GMDH-neural network are represented by the entire population element-wise. Comparative experimental studies of the quality of the proposed computational methods for solving forecasting problems were carried out.

关键词： Group method of data handling Clonal selection method gene expression programming

来源：评论

学校读者我要写书评

暂无评论

A dynamic scheduling method for self-organized AGVs in production logistics systems

引用

Procedia CIRP 2021年 104卷 381-386页

作者： Lixiang Zhang Yan Yan Yaoguang Hu Weibo Ren School of Mechanical Engineering Beijing Institute of Technology Beijing China

Automated guided vehicles (AGV) with different carrying capacities are required for complex material handling in smart factories, which causes resource waste. To minimize the delay and reduce the cost of logistics systems, this paper proposes a dynamic scheduling method for self-organized AGVs (SAGV) in production logistics systems, where multiple identical SAGVs can communicate and freely combine with others as one vehicle to perform one task. Using an improved gene expression programming to learning dynamic dispatching rules, experimental results show that dispatching rules learned are efficient and the cost of logistic systems by using SAGVs is significantly reduced.

关键词： dynamic scheduling automated guided vehicles gene expression programming production logistics system

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：