检索结果-内蒙古大学图书馆

Hourly water demand forecasting using a hybrid model based on mind evolutionary algorithm

WATER SUPPLY 2022年第1期22卷 917-927页

作者： Huang, Haidong Zhang, Zhixiong Lin, Zhenliang Liu, Shitong Beibu Gulf Univ Coll Civil Engn & Architecture Qinzhou 535011 Guangxi Peoples R China

A hybrid model based on mind evolutionary algorithm is proposed to predict hourly water demand. In the hybrid model, hourly water demand data are first reconstructed to generate appropriate samples so as to represent the characteristics of time series effectively. Then, mind evolutionary algorithm is integrated into a back propagation neural network (BPNN) to improve prediction performance. To investigate the application potential of the proposed model in hourly water demand forecasting, real hourly water demand data were applied to evaluate its prediction performance. Besides, the performance of the proposed model was compared with a traditional BPNN model and another hybrid model where genetic algorithm (GA) is used as an optimization algorithm for BPNN. The results show that the proposed model has a satisfactory prediction performance in hourly water demand forecasting. On the whole, the proposed model outperforms all other models involved in the comparisons in both prediction accuracy and stability. These findings suggest that the proposed model can be a novel and effective tool for hourly water demand forecasting.

关键词： back propagation neural network genetic algorithm mind evolutionary algorithm water demand forecasting

来源：评论

学校读者我要写书评

暂无评论

Indoor Localization Using mind evolutionary algorithm-Based Geomagnetic Positioning and Smartphone IMU Sensors

引用

IEEE SENSORS JOURNAL 2022年第7期22卷 7130-7141页

作者： Sun, Meng Wang, Yunjia Joseph, Wout Plets, David China Univ Min & Technol Sch Environm & Spatial Informat MNR Key Lab Land Environm & Disaster Monitoring Xuzhou 221116 Jiangsu Peoples R China Univ Ghent Dept Informat Technol IMEC WAVES Grp B-9052 Ghent Belgium

With the pervasiveness and ubiquitous distribution of the magnetic field in indoor environments, indoor localization using magnetic positioning (MP) has attracted considerable attention. This work concentrates on the MP and pedestrian dead reckoning (PDR) method, and constructs a fusion system for smartphones using MP and PDR based on the extended Kalman filter (EKF). The mind evolutionary algorithm (MEA) is introduced to search for the optimal magnetic position based on a heuristic searching strategy, which uses the similartaxis and dissimilation for the evolutionary operation. In the PDR module, the acceleration characteristics of different walking patterns are analyzed and the corresponding features are extracted. The enhanced genetic algorithm-based extreme learning machine (EGA-ELM) is adopted to train these features and address the gait recognition problem of different walking patterns. Finally, to obtain a lightweight and high-precision fusion method, MEA-based MP is integrated with PDR based on the EKF. Extensive experiments are conducted to evaluate the proposed methods. The testing results showed that MEA-based MP can obtain a location error within 2.3 m and steps can be recognized with a mean accuracy of 95% when different users participate in testing. The positioning results after fusion with PDR reveal that the mean location error and root-mean-square error (RMSE) are 1.25 m and 1.53 m respectively, which outperforms the MP, PDR, MP and PDR fusion methods using improved particle filter (IPF) and genetic particle filter (GPF).

关键词： Legged locomotion Magnetometers Magnetic separation Magnetic sensors Location awareness Particle filters Complexity theory Indoor positioning sensor fusion pedestrian dead reckoning (PDR) magnetic positioning extended Kalman filter mind evolutionary algorithm magnetic field

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

Application of mind evolutionary algorithm and Artificial Neural Networks for Prediction of Profile and Flatness in Hot Strip Rolling Process

引用

NEURAL PROCESSING LETTERS 2019年第3期50卷 2455-2479页

作者： Wang, Zhenhua Ma, Gengsheng Gong, Dianyao Sun, Jie Zhang, Dianhua Northeastern Univ State Key Lab Rolling & Automat Shenyang 110819 Liaoning Peoples R China Collaborat Innovat Ctr Steel Technol Shenyang 110819 Liaoning Peoples R China

Strip shape prediction is one of the most important technical to improve the quality of products in hot strip rolling process. In this paper, three hybrid models, including GA-MLP, MEA-MLP and PCA-MEA-MLP, are proposed for profile and flatness predictions by combining genetic algorithm (GA), mind evolutionary algorithm (MEA), principal component analysis (PCA) and multi-layer perceptron (MLP) neural networks. Mean absolute error (MAE), mean absolute percentage error, root mean squared error are adapted to evaluate the performance of the models. The results show that the data-driven model based on intelligent algorithm optimization neural networks can achieve good prediction of profile and flatness. Comparing with the hybrid GA-MLP model, the training speed of the hybrid MEA-MLP model is faster and the training time is greatly reduced. The model establishing with the input data after dimensionality reduction by PCA can reduce training time and become simple. The innovation of this paper is to propose a data-driven fast response model based on intelligent algorithm optimization neural network to replace the traditional mechanism model based on mathematical formula analysis to study complex, non-linear strip shape control in hot rolling process.

关键词： Artificial neural network mind evolutionary algorithm Principal component analysis Genetic algorithm Profile and flatness prediction Hot strip rolling

来源：评论

学校读者我要写书评

暂无评论

A Prediction Approach on Energy Consumption for Public Buildings Using mind evolutionary algorithm and BP Neural Network 7

A Prediction Approach on Energy Consumption for Public Build...

引用

IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)

作者： Gao, Yang Liu, Xudong Li, Xiaoli Gu, Liu Cui, Jiarui Yang, Xu Beijing Inst Residential Bldg Design & Res Co LTD Res Ctr Beijing 100005 Peoples R China Univ Sci & Technol Beijing Sch Automat & Elect Engn Key Lab Knowledge Automat Ind Proc Minist Educ Beijing 100083 Peoples R China Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Minist Educ Engn Res Ctr Digital Community Beijing 100124 Peoples R China

ISBN: (纸本)9781538626184

This paper proposes a prediction approach on energy consumption for public buildings based on mind evolutionary algorithm and BP neural network. The actual real-time data of some layer in a public building can be obtained online by our implemented building monitoring system, then several key factors which affect building energy consumption can be analyzed and determined by correlation analysis method. By using the mind evolutionary algorithm, the ideal weight values and threshold values of BP neural network are calculated, which can solve its problems of low efficiency and slow convergence. Finally, the performance and effectiveness of the proposed forecasting model are demonstrated through a case study of a building energy consumption monitoring system from practical engineering.

关键词： Energy Consumption mind evolutionary algorithm BP Neural Network Building Monitoring System

来源：评论

学校读者我要写书评

暂无评论

The research of voltage prediction of solar UAV panel by Improved mind evolutionary algorithm 3

The research of voltage prediction of solar UAV panel by Imp...

引用

3rd International Conference on Computational Intelligence and Applications (ICCIA)

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

ISBN: (纸本)9781538695715

Solar energy is a new energy, which is not only perennial but also obtainable to every strata of the world. The use of solar photovoltaic systems (SPV) is the process of converting solar energy into electricity. Photovoltaic modules are mounted on the wings of solar unmanned aerial vehicles. In this paper, a new MPPT controller is proposed to predict the voltage to obtain the maximum power from the solar panel. The proposed MPPT controller is based on mind evolution algorithm (MEA) optimized back propagation neural network (BPNN). Firstly, the mind evolution algorithm model is constructed based on topology of BP Neural Network. Then, it is used to obtain the optimal solutions, which is regarded as initial weights and threshold value of BP Neural Network. Finally, the simulation experiment is carried out by using MATLAB software. The prediction results of the BP neural network optimized by the mind evolution algorithm are compared.

关键词： Solar cell module Solar unmanned aerial vehicle BP Neural Network mind evolutionary algorithm Maximum power point tracking

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

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

New wind speed forecasting approaches using fast ensemble empirical model decomposition, genetic algorithm, mind evolutionary algorithm and Artificial Neural Networks

引用

RENEWABLE ENERGY 2015年 83卷 1066-1075页

作者： Liu, Hui Tian, Hongqi Liang, Xifeng Li, Yanfei Cent S Univ Sch Traff & Transportat Engn Minist Educ Key Lab Traff Safety Track Changsha 410075 Hunan Peoples R China Univ Rostock Fac Comp Sci & Elect Engn Inst Automat D-18119 Rostock Mecklenburg Vor Germany

Wind speed high-precision prediction is one of the most important technical aspects to protect the safety of wind power utilization. In this study, two new hybrid methods [FEEMD-MEA-MLP/FEEMD-GA-MLP] are proposed for the wind speed accurate multi-step predictions by combining FEEMD (Fast Ensemble Empirical Mode Decomposition), MEA (mind evolutionary algorithm), GA (Genetic algorithm) and MLP (Multi Layer Perceptron) neural networks. In these two hybrid methods, the FEEMD algorithm is adopted to decompose the original wind speed series into a number of sub-layers and the MLP neural networks optimized by the MEA algorithm and the GA algorithm are built to predict the decomposed wind speed sub-layers, respectively. The innovation of the study is to investigate the promoted percentages of the MLP neural networks by the FEEMD decomposition and the MEA/GA optimization, respectively. The involved forecasting models in the performance comparison in the study include the hybrid FEEMD-MEA-MLP, the hybrid FEEMD -GA-MLP, the hybrid FEEMD-MLP, the hybrid MEA-MLP, the hybrid GA-MLP and the single MLP. Two experimental results show that: (a) among all the involved methods, the hybrid FEEMD-MEA-MLP model has the best forecasting performance;(b) the FEEMD algorithm promotes the performance of the MLP neural networks significantly while the MEA/GA algorithms do not improve the performance of the MLP neural networks significantly;and (c) the hybrid FEEMD-MEA-MLP method and the hybrid FEEMD-GA-MLP method are both effective in the wind speed high-precision predictions. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Wind energy Wind speed forecasting Decomposition mind evolutionary algorithm Genetic algorithm Artificial Neural Networks

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

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：