Application of Artificial Neural Network Model based on Improved PSO in Water Supply Systems

被引:1
|
作者
Wang, Hongxiang [1 ]
Guo, Wenxian [1 ]
机构
[1] N China Univ Water Resources & Elect Power, Zhengzhou, Peoples R China
关键词
Artificial Neural Network; Water Distribution Ssystem; Microscopic Model; Macroscopic Model;
D O I
10.4028/www.scientific.net/AMR.267.609
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Parameter calibration, data collection and simulation to control element were used to improve the accuracy of microscopic model. In order to overcome the shortage of macroscopic model, theoretical and empirical equation was adopted. The artificial neural network based on PSO method was introduced to improve simulation ability of water distribution system model from microscopic model and macroscopic model. There are two hidden layers with a maximum of 64 nodes per layer in the model. The Particle Swarm Optimization (PSO) algorithm is implemented to optimize the node numbers of the hidden layers in the model. The study indicates that the artificial neural network connecting with improved PSO method is an attractive alternative to the conventional regression analysis method in modeling water distribution systems.
引用
收藏
页码:609 / 613
页数:5
相关论文
共 50 条
  • [1] Improved PSO in Water Supply Systems Based on AHP-RS and RBF Neural Network
    Wang Aojie
    Liu Chaolue
    [J]. ARCHITECTURE AND BUILDING MATERIALS, PTS 1 AND 2, 2011, 99-100 : 199 - 202
  • [2] The water resource supply distribution model for EIPs based on artificial neural network
    Qin, SunTao
    [J]. 2006 CHINESE CONTROL CONFERENCE, VOLS 1-5, 2006, : 115 - 117
  • [3] An artificial neural network model for water table management systems
    Yang, CC
    Prasher, SO
    Tan, CS
    [J]. DRAINAGE IN THE 21ST CENTURY: FOOD PRODUCTION AND THE ENVIRONMENT: PROCEEDINGS OF THE 7TH INTERNATIONAL DRAINAGE SYMPOSIUM, 1998, : 250 - 257
  • [4] An Improved Tropospheric Tomographic Model Based on Artificial Neural Network
    Zhang, Minghao
    Zhang, Kefei
    Wu, Suqin
    Li, Longjiang
    Zhu, Dantong
    Wan, Moufeng
    Sun, Peng
    Shi, Jiaqi
    Liu, Shangyi
    Hu, Andong
    [J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2023, 16 : 4801 - 4819
  • [5] APPLICATION OF ARTIFICIAL NEURAL NETWORKS TO THE TECHNICAL CONDITION ASSESSMENT OF WATER SUPPLY SYSTEMS
    Kaminski, Kamil
    Kaminski, Wladyslaw
    Mizerski, Tomasz
    [J]. ECOLOGICAL CHEMISTRY AND ENGINEERING S-CHEMIA I INZYNIERIA EKOLOGICZNA S, 2017, 24 (01): : 31 - 40
  • [6] Supply Chain Network Design Based on Fuzzy Neural Network and PSO
    Huang, Yuansheng
    Qiu, Zilong
    Liu, Qingchao
    [J]. 2008 IEEE INTERNATIONAL CONFERENCE ON AUTOMATION AND LOGISTICS, VOLS 1-6, 2008, : 2189 - 2193
  • [7] Research on the neural network based on an improved PSO algorithm
    Liu, Jiang
    [J]. GREEN BUILDING, ENVIRONMENT, ENERGY AND CIVIL ENGINEERING, 2017, : 49 - 53
  • [8] The application of power plant construction investment estimation based on improved neural network by PSO
    Jia Zheng-yuan
    Tian Li
    Liu Qingchao
    [J]. 2008 4TH INTERNATIONAL CONFERENCE ON WIRELESS COMMUNICATIONS, NETWORKING AND MOBILE COMPUTING, VOLS 1-31, 2008, : 7452 - 7455
  • [9] Research and application of unified model based on Artificial neural network
    Hao, Jishan
    Jin, Changlin
    Hu, Zhuofei
    Zhang, Qingfeng
    [J]. 2017 IEEE ELECTRICAL DESIGN OF ADVANCED PACKAGING AND SYSTEMS SYMPOSIUM (EDAPS), 2017,
  • [10] The Analysis and Application of the Monitor Model of Gasifier Temperature Based on the PSO Neural Network
    Jia, Qun
    Li, Yongxin
    [J]. 2013 THIRD INTERNATIONAL CONFERENCE ON INSTRUMENTATION & MEASUREMENT, COMPUTER, COMMUNICATION AND CONTROL (IMCCC), 2013, : 335 - 338