Application of artificial neural networks to load identification

被引：113
|
作者：
Cao, X
Sugiyama, Y
Mitsui, Y
机构：
[1] Shinshu Univ, Coll Engn, Dept Civil Engn, Nagano 380, Japan
[2] China Flight Test Estab, Xian 710089, Peoples R China
[3] Osaka Prefecture Univ, Coll Engn, Sakai, Osaka 593, Japan
来源：
COMPUTERS & STRUCTURES | 1998年 / 69卷 / 01期
关键词：
artificial neural network; load identification; inverse problem;
D O I：
10.1016/S0045-7949(98)00085-6
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
The intended aim of the study is to develope an approach to the identification of the loads acting on aircraft wings, which uses an artificial neural network to model the load-strain relationship in structural analysis. As the first step of the study, this paper describes the application of an artificial neural network to identify the loads distributed across a cantilevered beam. The distributed loads are approximated by a set of concentrated loads. The paper demonstrates that using an artificial neural network to identify loads is feasible and a well trained artificial neural network reveals an extremely fast convergence and a high degree of accuracy in the process of load identification for a cantilevered beam model. (C) 1998 Elsevier Science Ltd. All rights reserved.
引用
收藏
页码：63 / 78
页数：16
相关论文
共 50 条
  • [41] Accurate electricity load forecasting with Artificial Neural Networks
    Ortiz-Arroyo, Daniel
    Skov, Morten K.
    Huynh, Quang
    [J]. INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE FOR MODELLING, CONTROL & AUTOMATION JOINTLY WITH INTERNATIONAL CONFERENCE ON INTELLIGENT AGENTS, WEB TECHNOLOGIES & INTERNET COMMERCE, VOL 1, PROCEEDINGS, 2006, : 94 - +
  • [42] Noisy fingerprint identification by artificial neural networks
    Curbelo, R
    [J]. NINTH WORKSHOP ON VIRTUAL INTELLIGENCE/DYNAMIC NEURAL NETWORKS: ACADEMIC/INDUSTRIAL/NASA/DEFENSE TECHNICAL INTERCHANGE AND TUTORIALS, 1999, 3728 : 432 - 449
  • [43] Artificial neural networks for species identification by taxonomists
    Clark, JY
    [J]. BIOSYSTEMS, 2003, 72 (1-2) : 131 - 147
  • [44] Nematode Identification using Artificial Neural Networks
    Uhlemann, Jason
    Cawley, Oisin
    Kakouli-Duarte, Thomais
    [J]. PROCEEDINGS OF THE 1ST INTERNATIONAL CONFERENCE ON DEEP LEARNING THEORY AND APPLICATIONS (DELTA), 2020, : 13 - 22
  • [45] Application of Semi-Bayesian Neural Networks in the Identification of Load Causing Beam Yielding
    Miller, Bartosz
    [J]. ARTIFICIAL NEURAL NETWORKS-ICANN 2010, PT I, 2010, 6352 : 97 - 100
  • [46] Application of Artificial Neural Networks for Power Load Prediction in Critical Infrastructure: A Comparative Case Study
    Aliyari, Mostafa
    Ayele, Yonas Zewdu
    [J]. APPLIED SYSTEM INNOVATION, 2023, 6 (06)
  • [47] Application and comparison of several artificial neural networks for forecasting the Hellenic daily electricity demand load
    Ekonomou, L.
    Oikonomou, D. S.
    [J]. ADVANCES ON ARTIFICIAL INTELLIGENCE, KNOWLEDGE ENGINEERING AND DATA BASES, PROCEEDINGS, 2008, : 67 - +
  • [48] IDENTIFICATION OF THE SPATIAL DISTRIBUTION OF CONDUCTIVITY BY EDDY CURRENT DEFECTOSCOPY WITH APPLICATION OF ARTIFICIAL NEURAL NETWORKS
    Chady, T.
    Spychalski, I.
    Sikora, R.
    [J]. REVIEW OF PROGRESS IN QUANTITATIVE NONDESTRUCTIVE EVALUATION, VOLS 31A AND 31B, 2012, 1430 : 449 - 456
  • [49] Application of artificial neural network to economic load dispatch
    Nanda, J
    Sachan, A
    Pradhan, L
    Lai, LL
    Prasad, M
    Kothari, ML
    Rao, AK
    [J]. FOURTH INTERNATIONAL CONFERENCE ON ADVANCES IN POWER SYSTEM CONTROL, OPERATION & MANAGEMENT, VOLS 1 AND 2, 1997, : 707 - 711
  • [50] Application of artificial neural networks in non-invasive identification of electric energy receivers
    Kwater, Tadeusz
    Bartman, Jacek
    [J]. 2017 PROGRESS IN APPLIED ELECTRICAL ENGINEERING (PAEE), 2017,
12345