Artificial Intelligence Algorithm Research for Inverse Problems in Environmental Hydraulics

被引:0
|
作者
Peng, Ya-mian [1 ]
Liu, Chun-feng [1 ]
机构
[1] Hebei United Univ, Coll Sci, Tangshan, Peoples R China
关键词
Artificial intelligence; GA; Environmental hydraulics; Inverse problem;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The control equation of many systems can described by the ODE or PDE with fitting initialization conditions and boundary conditions in the filed of environmental hydraulics. A new approach based on Artificial Intelligence Algorithm to inverse problem in environmental hydraulics is introduced in this paper. Firstly, the Genetic Algorithms (GA) transforms the inverse problem into an optimization problem and it has artificial intelligence search global function. Subsequently, the original iterated values are obtained through across gene and variance gene of GA, and put the original iterated values in the beginning of the best disturbed iteration method for the inverse problem of partial differential equation parameter identified. Then we can get the steady numerical solution of the parameter that need seek for. The results shows that the approximate solution have good astringency and high precision, and can apply broadly.
引用
收藏
页码:292 / 296
页数:5
相关论文
共 50 条
  • [21] Research on adaptive artificial intelligence algorithm in signal denoising and enhancement
    Mao, Zhequn
    [J]. Applied Mathematics and Nonlinear Sciences, 2024, 9 (01)
  • [22] Research on Artificial Intelligence Recommendation Model Based on Genetic Algorithm
    Cheng, Xuelong
    Qiu, Wenhui
    Lu, Chun
    [J]. TENTH INTERNATIONAL CONFERENCE ON DIGITAL IMAGE PROCESSING (ICDIP 2018), 2018, 10806
  • [23] Research on Financial Investment Decision Based on Artificial Intelligence Algorithm
    Ren, Jiaping
    [J]. IEEE SENSORS JOURNAL, 2021, 21 (22) : 25190 - 25197
  • [24] Artificial intelligence in research
    Musib, Mrinal
    [J]. SCIENCE, 2017, 357 (6346) : 28 - 28
  • [25] Trust and trustworthy artificial intelligence: A research agenda for AI in the environmental sciences
    Bostrom, Ann
    Demuth, Julie L.
    Wirz, Christopher D.
    Cains, Mariana G.
    Schumacher, Andrea
    Madlambayan, Deianna
    Bansal, Akansha Singh
    Bearth, Angela
    Chase, Randy
    Crosman, Katherine M.
    Ebert-Uphoff, Imme
    Gagne, David John
    Guikema, Seth
    Hoffman, Robert
    Johnson, Branden B.
    Kumler-Bonfanti, Christina
    Lee, John D.
    Lowe, Anna
    McGovern, Amy
    Przybylo, Vanessa
    Radford, Jacob T.
    Roth, Emilie
    Sutter, Carly
    Tissot, Philippe
    Roebber, Paul
    Stewart, Jebb Q.
    White, Miranda
    Williams, John K.
    [J]. RISK ANALYSIS, 2024, 44 (06) : 1498 - 1513
  • [26] New operations research and artificial intelligence approaches to traffic engineering problems
    Bielli, M
    Reverberi, P
    [J]. EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 1996, 92 (03) : 550 - 572
  • [27] Environmental sciences and artificial intelligence
    Sánchez-Marrè, M
    Cortés, U
    Comas, J
    [J]. ENVIRONMENTAL MODELLING & SOFTWARE, 2004, 19 (09) : 761 - 762
  • [28] Research on Human Thinking Engineering Model to Solve Problems in Artificial Intelligence
    Ping, Zhu
    [J]. INTERNATIONAL CONFERENCE ON ELECTRICAL AND CONTROL ENGINEERING (ICECE 2015), 2015, : 768 - 773
  • [29] Artificial intelligence and environmental ethics
    Thomson, AJ
    [J]. AI APPLICATIONS, 1997, 11 (01): : 69 - 73
  • [30] The Environmental Impact of Artificial Intelligence
    Kshetri, Nir
    [J]. IT PROFESSIONAL, 2024, 26 (03) : 9 - 13