检索结果-内蒙古大学图书馆

Short-Term Traffic Flow Prediction Model of Wavelet Neural Network Based on mind evolutionary algorithm

INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2018年第12期32卷

作者： Xu, Liqiang Du, Xuedong Wang, Binguo Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao 266590 Shandong Peoples R China Shandong Univ Sci & Technol Coll Comp Sci & Engn Prov Comp Expt Teaching Demonstrating Ctr Shandon Qingdao 266590 Shandong Peoples R China

This paper introduces mind evolutionary algorithm (MEA) into the application of short-term traffic flow prediction, and proposes a short-term traffic flow prediction model of wavelet neural network based on mind evolutionary algorithm (MEA-WNN). The optimal connection weight and wavelet parameters of wavelet neural network (WNN) are searched globally by MEA, and the convergence capacity of wavelet neural network is improved. The experimental data show that, compared with the prediction model of the traditional WNN and the WNN based on genetic algorithm (GA-WNN), the prediction model of MEA-WNN has higher global prediction accuracy.

关键词： mind evolutionary algorithm wavelet neural network short-term traffic flow prediction model global prediction accuracy

来源：评论

学校读者我要写书评

暂无评论

Phase Prediction of High Carbon Pearlitic Steel: An Improved Model Combining mind evolutionary algorithm and Neural Networks

引用

ADVANCED ENGINEERING MATERIALS 2021年第8期23卷 2100204-2100204页

作者： Qiao, Ling Liu, Yong Zhu, Jingchuan Wang, Yuan Harbin Inst Technol Sch Mat Sci & Engn Harbin 150001 Heilongjiang Peoples R China Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Liaoning Peoples R China

Accurate phase constitution prediction is crucial for guiding the new steel design with desirable properties. This article uses three machine learning (ML) algorithms, backpropagation neural network (BPNN), radial basis function neural network (RBFNN), and fuzzy neural network (FNN), to compare the accuracy of predictive model. Toward the collected data, statistical measure of correlation is taken in the present work by determining Pearson correlation coefficient (PCC). The results show that the testing accuracy using BPNN is higher than the corresponding testing accuracy using RBFNN and FNN. With these understandings, the well-known optimization algorithms, namely, mind evolutionary algorithm (MEA), are implemented to find optimal hyperparameters set that is able to induce a higher predictive capability. The resulting MEA-BPNN further enhances the prediction results in the present dataset and efficiently explores the relationship between alloying elements and phase constitution. Finally, the reliability and practicability of the model are verified by experimental measurement on the ferrite content for prepared steels, and their mechanical properties are tested for engineering applications. As such, the work proposes a useful MEA-BPNN model for predicting the phases of high carbon pearlite steel and provides an alternative route of designing new steels in a given system.

关键词： alloying elements machine learning mind evolutionary algorithm phases steels

来源：评论

学校读者我要写书评

暂无评论

CNC Thermal Compensation Based on mind evolutionary algorithm Optimized BP Neural Network

引用

World Journal of Engineering and Technology 2016年第1期4卷 38-44页

作者： Yuefang Zhao Xiaohong Ren Yang Hu Jin Wang Xuemei Bao Sichuan University of Science & Engineering Zigong China

Thermal deformation error is one of the most important factors affecting the CNCs’ accuracy, so research is conducted on the temperature errors affecting CNCs’ machining accuracy;on the basis of analyzing the unpredictability and pre-maturing of the results of the genetic algorithm, as well as the slow speed of the training speed of the particle algorithm, a kind of mind evolutionary algorithm optimized BP neural network featuring extremely strong global search capacity was proposed;type KVC850MA/2 five-axis CNC of Changzheng Lathe Factory was used as the research subject, and the mind evolutionary algorithm optimized BP neural network algorithm was used for the establishment of the compensation model between temperature changes and the CNCs’ thermal deformation errors, as well as the realization method on hardware. The simulation results indicated that this method featured extremely high practical value.

关键词： Thermal Errors Thermal Error Compensation Genetic algorithm mind evolutionary algorithm BP Neural Network

来源：评论

学校读者我要写书评

暂无评论

Convergence Analysis of mind evolutionary algorithm based on Functional Analysis

Convergence Analysis of Mind Evolutionary Algorithm based on...

引用

Firth IEEE International Conference on Cognitive Informatics(第五届认知信息国际会议)

作者： Keming Xie Yuxia Qiu Gang Xie The college of Information Engineering Tatyuan University of Technology Taiyuan 030024 P.R.China

ISBN: (纸本)9781424404759

As one of new type evolutionary computing methods, mind evolutionary algorithm processes evolutionary operations by using 'similartax' and 'dissimilation' operator. This paper studies the variance of population during the evolution from the view of the functional analysis. Interval sheath theorem is used to prove the global convergence of the algorithm. The conclusion is validated again by the numerical experiment results.

关键词： mind evolutionary algorithm Sequence Convergence Interval sheath theorem.

来源：评论

学校读者我要写书评

暂无评论

The Intelligence mind evolutionary algorithm Based on Difference

The Intelligence Mind Evolutionary Algorithm Based on Differ...

引用

The 24th Chinese Control and Decision Conference (第24届中国控制与决策学术年会 2012 CCDC)

作者： Hongge Guo Keming Xie College of Information Engineering Taiyuan University of Technology Taiyuan 030024 China. Coll College of Information Engineering Taiyuan University of Technology Taiyuan 030024 China.

In order to add population diversity, The intelligence mind evolutionary algorithm (IMEA) based on difference is presented. It uses difference information of subpopulations to share social information and comparing fitness to consideration. The improved algorithm makes mind evolutionary algorithm(MEA) perfect in theory and achieves the best balance between the searching velocity and global convergence in practice. Optimum experiments by the testing functions show the effectiveness and feasibility of the IMEA.

关键词： mind evolutionary algorithm Sharing social information Difference

来源：评论

学校读者我要写书评

暂无评论

Application of BP neural network models and mind evolutionary algorithm in predicting stock composite indexes on Shanghai Stock Exchange

Application of BP neural network models and mind evolutionar...

引用

2015 5th International Conference on Computer Sciences and Automation Engineering(ICCSAE 2015)

作者： Guohao Lu Changping Xie Yingshu Zhang Shaomei Fang College of Mathematics and Informatics South China Agricultural University

Stock composite indexes prediction is an important issue in the financial world. A back propagation neural network(BPNN) with mind evolutionary algorithm(MEA) developed for the prediction of prices on Shanghai Stock Exchange is presented. The optimum weights and threshold values of BPNN are determined by MEA, which solves partial minimization of BPNN. Experiments are performed with Shanghai Stock Exchange stock to determine the effectiveness of the model. The results indicate that the accuracy rate of the proposed model is more than 70%.

关键词： mind evolutionary algorithm Back propagation neural network Shanghai Stock Exchange Composite Index Stock Prediction

来源：评论

学校读者我要写书评

暂无评论

Research on the state-of-charge fusion estimation of lithium-ion batteries by the Extract Segment Fusion method

引用

JOURNAL OF ENERGY STORAGE 2025年 117卷

作者： Zhao, Zhihui Kou, Farong Pan, Zhengniu Chen, Leiming Xian Univ Sci & Technol Sch Mech Engn Xian 710054 Shaanxi Peoples R China Guangxi Univ Sch Phys Sci & Technol Nanning 530004 Guangxi Peoples R China Zhengzhou Univ Aeronaut Sch Mat Sci & Engn Zhengzhou 450046 Henan Peoples R China

The fusion of model-based and data-driven techniques for state-of-charge (SOC) estimation of batteries has become a prominent research focus. Existing approaches often rely on data-driven techniques to compensate for model-based methods, which can result in significant overcompensation. In this paper, a novel fusion method for SOC estimation is proposed. Firstly, a variable forgetting factor recursive least squares method with a limiting factor (Lim-VFFRLS) is employed to identify the parameters of a dual polarization (DP) battery model. Then, SOC estimation based on the Extended Kalman Filter (EKF) is divided into three states: steady state, failure state, and transition state. Subsequently, an innovative Extract Segment Fusion (ESF) method is proposed to integrate the advantages of the DP model-based EKF and a back propagation neural network optimized by the mind evolutionary algorithm (MEA-BP). This method, referred to as EKF-MEA-BP-ESF (EMBE). In contrast, the fusion method using linear interpolation is called EMBL. The performance of EMBE is evaluated using driving cycle condition tests, with results showing that the average mean absolute error (MAE) of SOC estimation is 0.243 %, and the average root mean squared error (RMSE) is 0.298 %. Furthermore, compared to the EKF, MEA-BP, and EMBL methods, the EMBE method reduces the MAE by an average of 47.546 %, 60.034 %, and 50.860 %, respectively, and the RMSE by an average of 55.484 %, 65.175 %, and 53.237 %, respectively. These results demonstrate that the EMBE method significantly enhances both the accuracy and robustness of SOC estimation. This approach shows considerable promise for applications in electric vehicles and large-scale energy storage.

关键词： State of charge fusion estimation BP neural network EKF mind evolutionary algorithm

来源：评论

学校读者我要写书评

暂无评论

Prediction of the displacement in a foundation pit based on neural network model fusion error and variational modal decomposition methods

引用

MEASUREMENT 2025年 240卷

作者： Sun, Linna Liu, Shengchang Zhang, Liming He, Keqiang Yan, Xiuzheng Qingdao Univ Technol Dept Civil Engn Qingdao 266033 Peoples R China Minist Nat Resources Key Lab Geol Safety Coastal Urban Underground Spac Qingdao 266101 Peoples R China

Prediction of foundation pit displacements is an important part of pit monitoring and early warning system, and accurate prediction of pit displacements can reduce pit disaster accidents. Herein, the Elman neural network model optimized by the mind evolutionary algorithm (MEA) is combined with the variational modal decomposition (VMD) and the fusion error method to accurately predict pit displacements at the top of the slope. Firstly, VMD is used to decompose the time series of pit displacements into trend, period, and random terms, and analyzed the rationality of the decomposition of the sub-displacement sequences;Secondly, the MEA-Elman model is used to predict the trend, period, and random terms and decomposition error in the decomposition of pit displacements, and finally the prediction results of the sub-displacements are superimposed to obtain the total displacement;The measured data of a pit in Qingdao, China, are used to verify the reliability of the model, and the calculated results show that the predictions are consistent with the measured data.

关键词： Pit displacement prediction Variational modal decomposition mind evolutionary algorithm Elman neural network Fusion error

来源：评论

学校读者我要写书评

暂无评论

Finite element model updating of continuous girder bridge based on IMEA-BP neural network

引用

INTERNATIONAL JOURNAL OF STRUCTURAL INTEGRITY 2025年

作者： Xiong, Hui-Zhong Sui, Hongbiao Xiao, Yinbo Huang, Yong Zhang, Guohua Northeast Forestry Univ Sch Civil Engn & Transportat Harbin Peoples R China China Earthquake Adm Inst Engn Mech Harbin Peoples R China China Gezhouba Grp Municipal Engn Co Ltd Harbin Peoples R China

PurposeThis paper aims to update the finite element model of a pre-stressed concrete continuous girder bridge using a back propagation neural network optimized by the improved mind evolutionary algorithm (IMEA).Design/methodology/approachIn this paper, we introduce particle swarm optimization in the convergence process of the mind evolutionary algorithm (MEA) and then dynamically adjust the inertia weights in the moving speed of the particle swarm through a nonlinear decreasing strategy, which is used to solve the drawback of the algorithm's arbitrarily initialized population. Finally, the measured natural frequency of the Yunliang River Bridge is used as the input parameter, and the design parameters are obtained by updating in stages (pile foundation stage and bridge completion stage).FindingsThe relative error of the natural frequencies of the continuous girder bridge is significantly reduced from more than 7% before updating to less than 3%. Compared with the 2.31% average relative error of the natural frequency of the continuous beam bridge updated by the MEA-BP neural network, the error of the natural frequency after the update of the IMEA-BP neural network model is only 1.01%. The IMEA-BP neural network demonstrates stronger applicability and higher update precision. Compared to the bridge finite element models without phased updating, the phased updating presents a better ***/valueThis study provides an accurate and effective method of updating finite element models for continuous girder bridges, as well as a valuable reference for updating finite element models for other types of bridges.

关键词： Finite element model updating Continuous girder bridge Back propagation neural network mind evolutionary algorithm

来源：评论

学校读者我要写书评

暂无评论

An Improved Model Combining evolutionary algorithm and Neural Networks for PV Maximum Power Point Tracking

引用

IEEE ACCESS 2019年 7卷 2823-2827页

作者： Wang, Haixin Shen, Jianxin Nanjing Univ Aeronaut & Astronaut Coll Mech & Elect Engn Nanjing 210016 Jiangsu Peoples R China

Aiming at the large error of the traditional constant control method in predicting the maximum power of solar UAV, this paper proposed an improved mind evolutionary algorithm combined with BP neural network (BPNN), in which the improved mind evolutionary algorithm optimizes the BPNN. The optimized model is used to predict the voltage at the maximum power of the panel in the UAV. The constant voltage parameter based on the conventional constant pressure control algorithm is replaced by this value. At the same time, a new control simulation model of constant voltage solar panel maximum power tracking based on the improved mind evolution algorithm optimize BPNN was built. At last, the algorithm was simulated and validated in the MATLAB/Simulink environment. The simulation results show that this algorithm is better than simply using BPNN and genetic algorithm optimize BPNN stability and higher accuracy.

关键词： Simulation photovoltaic maximum power tracking artificial neural networks mind evolutionary 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：