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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

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

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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

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

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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

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

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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

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Application of Chaos mind evolutionary algorithm in Antenna Arrays Synthesis

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JOURNAL OF COMPUTERS 2010年第5期5卷 717-724页

作者： Liu, Jianxia Li, Nan Xie, Keming Taiyuan Univ Technol Taiyuan Peoples R China

mind evolutionary algorithm (MEA) uses 'similartaxis' operation and 'dissimilation' operation to imitate the human mind evolution to processes optimization, overcoming the prematurity and improving searching efficiency. But it has several defects: the generation of the initial population is blind, random and redundant;the addition of naturally washed out temporary subpopulations is monotonous;existing searching modes easily to fall into local convergence. This paper proposed Chaos mind evolutionary algorithm. Two chaotic sequences produced in different ways bring adequate diversity to the population. As a result, the searching area is widened. Chaotic mind evolutionary algorithm is used in antenna array synthesis in this paper. Computer simulations show that Chaos mind evolutionary algorithm can be applied in optimization problems of uniformly-spaced linear array and the optimization result is better than that obtained from Genetic algorithm.

关键词： mind evolutionary algorithm chaotic optimization similartaxis operation dissimilation operation pattern synthesis

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Synthesis of Antenna Arrays Shaped-beam Using mind evolutionary algorithm

Synthesis of Antenna Arrays Shaped-beam Using Mind Evolution...

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1st International Workshop on Education Technology and Computer Science

作者： Liu, Jianxia Li, Nan Yang, Yongqing Xie, Keming Taiyuan Univ Technol Coll Informat Engn Taiyuan Peoples R China

ISBN: (纸本)9780769535579

mind evolutionary algorithm (AEA) uses 'similartaxis' operation and 'dissimilation' operation by imitating the human mind evolution to processes numerical optimization, overcoming the prematurity and improving searching efficiency. Given the determinate number and space between the antenna arrays units, the feeding-back amplitudes and phases are optimized and selected by this way to make the shaped-beam patterns satisfy the designing request. Computer simulations show that mind evolutionary algorithm can be applied in optimization problems of uniformly-spaced linear array and the optimization result is better than that obtained from Genetic algorithm.

关键词： shaped-beam mind evolutionary algorithm Genetic algorithm similartaxis operation dissimilation operation

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Optimization of Broadband Patch Antenna Based on mind evolutionary algorithm

Optimization of Broadband Patch Antenna Based on Mind Evolut...

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International Conference on Networks Security, Wireless Communications and Trusted Computing

作者： Liu, Jianxia Yang, Yongqing Li, Nan Xie, Keming Taiyuan Univ Technol Coll Informat Engn Taiyuan Peoples R China

ISBN: (纸本)9780769536101

Antenna designers should have enough experience in traditional antenna design, and its efficiency was low. evolutionary algorithm could improve the efficiency of designing antenna. The mind evolutionary algorithm (MEA) was combined with HFSS based on finite element method and applied in antenna optimization. Antenna parameters are optimized by MEA, with the simulation performance of HFSS as the fitness function of MEA to direct the evolutionary. Then an E- broadband microstrip antenna was designed and the efficiency of this method was proved. Simulation showed that the impedance bandwidth of the antenna optimized by MEA could increase of about 4% than traditional method, the return loss S-11 was lower in the passband and the gain in the center frequency was higher

关键词： microstrip antenna mind evolutionary algorithm similartax dissimilation return loss

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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...

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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

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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...

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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

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The Intelligence mind evolutionary algorithm Based on Difference

The Intelligence Mind Evolutionary Algorithm Based on Differ...

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24th Chinese Control and Decision Conference (CCDC)

作者： Guo, Hongge Xie, Keming Taiyuan Univ Technol Coll Informat Engn Taiyuan 030024 Peoples R China

ISBN: (纸本)9781457720727

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

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