检索结果-内蒙古大学图书馆

A-Brain: a general system for solving data analysis problems

JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE 2007年第4期19卷 333-353页

作者： Oltean, Mihai Univ Babes Bolyai Dept Comp Sci Fac Math & Comp Sci R-3400 Cluj Napoca Romania

An intelligent system should be able to solve a wide range of problems from different domains. In this paper we propose a complex and adaptive system capable of solving various data analysis problems without needing human help for parameter settings. The system, called A- Brain, consists of several interconnected components ( a decision- maker, a trainer, and several problem solvers) which provide a base for building complex problem solvers. The parameters of the trainer's algorithm are problem independent. This fact is a requirement for intelligent systems which cannot rely on human intervention while operating. The A- Brain system is used to solve some well- known problems in the. field of symbolic regression and classification. Numerical experiments show that the A- Brain system is able to perform very well on the considered test problems.

关键词： A-Brain intelligent systems problem solving data analysis multi expression programming

来源：评论

学校读者我要写书评

暂无评论

Straightforward prediction for air-entry value of compacted soils using machine learning algorithms

引用

ENGINEERING GEOLOGY 2020年 279卷 105911-105911页

作者： Wang, Han-Lin Yin, Zhen-Yu Zhang, Pin Jin, Yin-Fu Hong Kong Polytech Univ Dept Civil & Environm Engn Kowloon Hung Hom Hong Kong Peoples R China

The straightforward prediction for the air-entry value of compacted soils is practically useful, but the investigation on this issue is scarce. This study presents three alternative straightforward prediction models for the air-entry value of compacted soils using the representative machine learning algorithms of multi expression programming (MEP), evolutionary polynomial regression (EPR) and random forest (RF). Five known soil properties (i.e. sand content, fines content, plasticity index, initial water content and initial void ratio) are used as input variables. All models are developed based on a large database, covering a wide range of soil classifications. The results show that all the three proposed models are appropriate to predict the air-entry values of different compacted soils, with high prediction accuracies for both the training and the testing data. The monotonicity, the sensitivity and the robustness of the three prediction models are evaluated, showing consistency among different models with a slight difference and providing a strong support for the model feasibility. On the whole, the MEP and the EPR models are recommended for more convenient applications with explicit expression, while higher prediction accuracy may require the RF model although no explicit expression can be derived.

关键词： Machine learning Air-entry value multi expression programming Evolutionary polynomial regression Random forest Compacted soils

来源：评论

学校读者我要写书评

暂无评论

Evaluating the dynamic response and phase angle behavior of SBS-modified asphalt mixtures for enhanced pavement performance

引用

SCIENTIFIC REPORTS 2024年第1期14卷 1-26页

作者： Khan, Inamullah Bilal, Muhammad Khaliq, Wasim Khan, Nasir Khahro, Shabir Hussain Memon, Zubair Ahmed Malik, Mohammad Abdul Natl Univ Sci & Technol NUST Natl Inst Transportat NIT Islamabad Pakistan Univ Teknol PETRONAS Dept Civil & Environm Engn Seri Iskandar 32610 Malaysia Prince Sultan Univ Coll Engn Riyadh 11586 Saudi Arabia

Bitumen exhibits viscoelastic properties, showcasing both viscous and elastic behaviors, which are characterized by the phase angle and dynamic modulus. Issues like early fatigue fractures, rutting, and permanent deformations in bituminous asphalt pavements arise due to moisture susceptibility, high-temperature deformation, low-temperature cracking, and overloading. These distresses result in potholes, alligator cracks, and specific deformations that lead to early pavement failure, increasing rehabilitation and maintenance costs. To address these issues, this study examines the dynamic modulus and phase angle behavior of Styrene Butadiene Styrene (SBS) modified and unmodified asphalt mixtures. SBS was incorporated in various proportions, ranging from 2 to 7% by the weight of bitumen. The asphalt mixture performance tester (AMPT) was utilized to measure the dynamic modulus at temperatures of 4.4, 21.1, 37.8, and 54.4 degrees C, and frequencies of 0.1, 0.5, 1, 5, 10, and 25 Hz. The study found significant correlations between dynamic modulus, temperature, loading frequency, and SBS content. Additionally, multi expression programming (MEPX) and regression modeling were employed to estimate the dynamic modulus of SBS-modified HMA. Results indicated that increasing SBS content up to 7% decreased penetration and ductility values by up to 46% and 56%, respectively, while raising the softening point by 63% due to increased stiffness. The blend with 6% SBS by weight of bitumen exhibited superior performance compared to other mixtures. Phase angle initially increased with rising temperature, peaking at 37.8 degrees C at lower frequencies, and continued to increase at higher frequencies. Isothermal and isochronal plots showed that the 0% SBS mix had a higher phase angle due to increased bitumen content. Overall, the HMA mix with 6% SBS provided the best outcomes.

关键词： Styrene butadiene styrene (SBS) Dynamic modulus Phase angle multi expression programming Sensitivity analysis

来源：评论

学校读者我要写书评

暂无评论

Evolving evolutionary algorithms with patterns

引用

SOFT COMPUTING 2007年第6期11卷 503-518页

作者： Oltean, Mihai Univ Babes Bolyai Fac Math & Comp Sci Dept Comp Sci R-3400 Cluj Napoca Romania

A new model for evolving evolutionary algorithms (EAs) is proposed in this paper. The model is based on the multi expression programming (MEP) technique. Each MEP chromosome encodes an evolutionary pattern which is repeatedly used for generating the individuals of a new generation. The evolved pattern is embedded into a standard evolutionary scheme which is used for solving a particular problem. Several evolutionary algorithms for function optimization are evolved by using the considered model. The evolved evolutionary algorithms are compared with a human-designed genetic algorithm. Numerical experiments show that the evolved evolutionary algorithms can compete with standard approaches for several well-known benchmarking problems.

关键词： genetic algorithms genetic programming evolving evolutionary algorithms multi expression programming linear genetic programming

来源：评论

学校读者我要写书评

暂无评论

Energy-based numerical models for assessment of soil liquefaction

引用

Geoscience Frontiers 2012年第4期3卷 541-555页

作者： Amir Hossein Alavi Amir Hossein Gandomi School of Civil Engineering lran University of Science and TechnologyTehranIran College of Civil Engineering Tafresh UniversityTafreshlran

This study presents promising variants of genetic programming （GP）, namely linear genetic programming （LGP） and multi expression programming （MEP） to evaluate the liquefaction resistance of san- dy soils. Generalized LGP and MEP-based relationships were developed between the strain energy density required to trigger liquefaction （capacity energy） and the factors affecting the liquefaction characteristics of sands. The correlations were established based on well established and widely dispersed experimental results obtained from the literature. To verify the applicability of the derived models, they were employed to estimate the capacity energy values of parts of the test results that were not included in the analysis. The external validation of the models was verified using statistical criteria recommended by researchers. Sensitivity and parametric analyses were performed for further verification of the correlations. The results indicate that the proposed correlations are effectively capable of capturing the liquefaction resistance of a number of sandy soils. The developed correlations provide a significantly better prediction performance than the models found in the literature. Furthermore, the best LGP and MEP models perform superior than the optimal traditional GP model. The verification phases confirm the efficiency of the derived correlations for their general application to the assessment of the strain energy at the onset of liquefaction.

关键词： Soil liquefaction Capacity energy Linear genetic programming multi expression programming Sand Formulation

来源：评论

学校读者我要写书评

暂无评论

A Unified Fitness Calculation Method for Automatic Modeling Algorithms

A Unified Fitness Calculation Method for Automatic Modeling ...

引用

8th World Congress on Intelligent Control and Automation (WCICA)

作者： Jiang, Dazhi Wang, Zhifei Sun, Haojun Du, Yulin Univ Shantou Dept Comp Sci Shantou Guangdong Peoples R China

ISBN: (纸本)9781424467129

As a branch of Evolutionary Computation (EC), Gene expression programming (GEP) and multi expression programming (MEP) have been applied widely in Automatic Modeling. Based on the Gene Reading and Computing Machine, a unified and improved method named Improved Reading and Computing Machine (IGRCM) is presented to calculate the fitness of chromosome for two linear automatic modeling algorithms, which are GEP and MEP. The experimental results indicate that the unified IGRCM could save the CPU running time effectively.

关键词： Automatic Modeling Evolutionary Computation Gene expression programming multi expression programming

来源：评论

学校读者我要写书评

暂无评论

Inference of Differential Equations for Modeling Chemical Reactions

Inference of Differential Equations for Modeling Chemical Re...

引用

6th International Symposium on Neural Networks

作者： Yang, Bin Chen, Yuchui Meng, Qingfang Univ Jinan Computat Intelligence Lab Sch Informat Sci & Engn Jinan 250022 Peoples R China

ISBN: (纸本)9783642015069

This paper presents an evolutionary method for identifying a system of ordinary differential equations (ODEs) from the observed time series data. The structure of ODE is inferred by the multi expression programming (MEP) and the ODE's parameters are optimized by Using particle swarm optimization (PSO). The experimental results on chemical reaction modeling problems show effectiveness of the proposed method.

关键词： multi expression programming Ordinary differential equations Particle swarm optimization Chemical reaction

来源：评论

学校读者我要写书评

暂无评论

MENN Method Applications for Stock Market Forecasting

引用

5th International Symposium on Neural Networks

作者： Jia, Guangfeng Chen, Yuehui Wu, Peng Univ Jinan Sch Informat Sci & Engn Jinan 250022 Peoples R China

ISBN: (纸本)9783540877318

A new approach for forecasting stock index based oil multi expression Neural Network (MENN) is proposed in this paper. The approach employs the multi expression programming (MEP) to evolve the architecture of the MENN and the particle swarm optimization (PSO) to optimize the parameters encoded in the MENN. This framework allows input variables selection, over-layer connections for the various nodes involved. The performance and effectiveness of the proposed method are. evaluated using stock market forecasting problems and compared with the related methods.

关键词： multi expression programming Artificial Neural Network Stock Market Forecasting

来源：评论

学校读者我要写书评

暂无评论

Predicting compressive strength of hollow concrete prisms using machine learning techniques and explainable artificial intelligence (XAI)

引用

HELIYON 2024年第17期10卷 e36841页

作者： Bin Inqiad, Waleed Dumitrascu, Elena Valentina Dobre, Robert Alexandru Khan, Naseer Muhammad Hammood, Abbas Hussein Henedy, Sadiq N. Khan, Rana Muhammad Asad Natl Univ Sci & Technol NUST Mil Coll Engn MCE Islamabad 44000 Pakistan Natl Univ Sci & Technol Politehn Bucharest Fac Automat Control & Comp Sci Comp Dept Bucharest Romania Natl Univ Sci & Technol Politehn Bucharest Elect Technol & Reliabil Dept Bucharest 061071 Romania Natl Univ Sci & Technol NUST Mil Coll Engn MCE Dept Sustainable Adv Geomech Engn Islamabad 44000 Pakistan Middle East Univ MEU Res Unit Amman 11831 Jordan Al Mamoon Univ Coll Dept Comp Tech Engn Baghdad Iraq Mazaya Univ Coll Dept Civil Engn Nasiriya 64001 Iraq Pak Austria Fachhochschule Inst Appl Sci & Technol Haripur Pakistan

The design of masonry structures requires accurate estimation of compressive strength (CS) of hollow concrete masonry prisms. Generally, the CS of masonry prisms is determined by destructive laboratory testing which results in time and resource wastage. Thus, this study aims to provide machine learning-based predictive models for CS of hollow concrete masonry blocks using different algorithms including multi expression programming (MEP), Random Forest Regression (RFR), and Extreme Gradient Boosting (XGB) etc. A dataset of 159 experimental results was collected from published literature for this purpose. The collected dataset consisted of four input parameters including strength of masonry units (f(b)), height-to-thickness ratio (h/t), strength of mortar (f(m)), and ratio of f(m)/f(b) and only one output parameter i.e., CS. Out of all the algorithms employed in current study, only MEP and GEP expressed their output in the form of an empirical equation. The accuracy of developed models was assessed using root mean squared error (RMSE), objective function (OF), and R(2 )etc. Among all algorithms assessed, XGB turned out to be the most accurate having R-2 = 0.99 and least OF value of 0.0063 followed by AdaBoost, RFR, and other algorithms. The developed XGB model was also used to conduct different explainable artificial intelligence (XAI) analysis including sensitivity and shapley analysis and the results showed that strength of masonry unit (f(b)) is the most significant variable in predicting CS. Thus, the ML-based predictive models presented in this study can be utilized practically for determining CS of hollow concrete masonry prisms without requiring expensive and time-consuming laboratory testing.

关键词： Concrete masonry prisms Machine learning multi expression programming Compressive strength Explainable artificial intelligence (XAI)

来源：评论

学校读者我要写书评

暂无评论

Predicting 28-day compressive strength of fibre-reinforced self-compacting concrete (FR-SCC) using MEP and GEP

引用

SCIENTIFIC REPORTS 2024年第1期14卷 1-21页

作者： Inqiad, Waleed Bin Siddique, Muhammad Shahid Ali, Mujahid Najeh, Taoufik Natl Univ Sci & Technol NUST Mil Coll Engn MCE Islamabad 44000 Pakistan Silesian Univ Tech Fac Transport & Aviat Engn Dept Transport Syst Traff Engn & Logist Krasinskiego 8 St PL-40019 Katowice Poland Lulea Univ Technol Dept Civil Environm & Nat Resources Engn Operat & Maintenance Operat Maintenance & Acoust Lulea Sweden

The utilization of Self-compacting Concrete (SCC) has escalated worldwide due to its superior properties in comparison to normal concrete such as compaction without vibration, increased flowability and segregation resistance. Various other desirable properties like ductile behaviour, increased strain capacity and tensile strength etc. can be imparted to SCC by incorporation of fibres. Thus, this study presents a novel approach to predict 28-day compressive strength (C-S) of FR-SCC using Gene expression programming (GEP) and multi expression programming (MEP) for fostering its widespread use in the industry. For this purpose, a dataset had been compiled from internationally published literature having six input parameters including water-to-cement ratio, silica fume, fine aggregate, coarse aggregate, fibre, and superplasticizer. The predictive abilities of developed algorithms were assessed using error metrices like mean absolute error (MAE), a20-index, and objective function (OF) etc. The comparison of MEP and GEP models indicated that GEP gave a simple equation having lesser errors than MEP. The OF value of GEP was 0.029 compared to 0.031 of MEP. Thus, sensitivity analysis was performed on GEP model. The models were also checked using some external validation checks which also verified that MEP and GEP equations can be used to forecast the strength of FR-SCC for practical uses.

关键词： Self-compacting concrete Genetic programming Fiber-reinforced self-compacting concrete multi expression programming Gene expression programming

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：