Application of cellular neural networks in stress analysis of prismatic bars subjected to torsion

被引:0
|
作者
Krstic, I [1 ]
Reljin, B [1 ]
Kostic, P [1 ]
Kandic, D [1 ]
机构
[1] Fed Inst Standardizat, YU-11000 Belgrade, Yugoslavia
关键词
cellular neural network; modeling; prismatic bar; twist; shear stress;
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
In the most general case-the finding of the shear stress distribution on the cross section of prismatic bar subjected to torsion is a specific problem that can be solved in two steps. The first of them consists in finding the so-called. and the second one in finding the shear stress function, stresses on the basis of the formerly found stress function., The stress function is the solution of Poisson's partial differential equation for given conditions of unambiguity that in the elasticity theory describes the torsion of prismatic bars in terms of stresses.. Modeling by means of electrical networks is one of-a few possible ways to find the stress function. This paper describes how Chua and Yang's cellular neural networks can be used-. as an analogous model-to find the stress function of a twisted prismatic bar, which serves to calculate the shear stress distribution. Effectiveness of the presented method is illustrated by the solutions of two problems. The method can be applied in mechanical and civil engineering.
引用
收藏
页码:129 / 134
页数:6
相关论文
共 50 条
  • [31] Transverse Shear Capacity Predictions of GFRP Bars Subjected to Accelerated Aging Using Artificial Neural Networks
    Fasil, Mohammed
    Al-Zahrani, Mesfer M. M.
    JOURNAL OF MATERIALS IN CIVIL ENGINEERING, 2023, 35 (04)
  • [32] Predicting apparent shear stress in prismatic compound open channels using artificial neural networks
    Huai, Wenxin
    Chen, Gang
    Zeng, Yuhong
    JOURNAL OF HYDROINFORMATICS, 2013, 15 (01) : 138 - 146
  • [33] Neural networks application for image analysis
    Swiatnicki, Z
    PROCEEDINGS OF THE FIFTH JOINT CONFERENCE ON INFORMATION SCIENCES, VOLS 1 AND 2, 2000, : 828 - 829
  • [34] Application of neural networks in cluster analysis
    Su, MC
    DeClaris, N
    Liu, TK
    SMC '97 CONFERENCE PROCEEDINGS - 1997 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS, VOLS 1-5: CONFERENCE THEME: COMPUTATIONAL CYBERNETICS AND SIMULATION, 1997, : 1 - 6
  • [35] The Application of Cellular Neural Networks for Solving Partial Differential Equations
    Sun Lijuan (Department of Computer Engineering
    TheJournalofChinaUniversitiesofPostsandTelecommunications, 1997, (01) : 47 - 52
  • [36] Application of fuzzy cellular neural networks to Euclidean Distance Transformation
    Yang, T
    Yang, LB
    IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-FUNDAMENTAL THEORY AND APPLICATIONS, 1997, 44 (03): : 242 - 246
  • [37] APPLICATION OF CELLULAR NEURAL NETWORKS TO MODEL POPULATION-DYNAMICS
    CRUZ, JM
    CHUA, LO
    IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-FUNDAMENTAL THEORY AND APPLICATIONS, 1995, 42 (10): : 715 - 720
  • [38] Application of back propagation artificial neural networks in cellular manufacturing
    Mechanical Engineering Department, Indian Institute of Technology, New Delhi 110 016, India
    J Inst Eng India Part PR, 2 (43-46):
  • [39] Application of polynomial cellular neural networks in diagnosis of astrometric chromaticity
    Cancelliere, R.
    Gai, M.
    Slavova, A.
    APPLIED MATHEMATICAL MODELLING, 2010, 34 (12) : 4243 - 4252
  • [40] Cellular neural networks:: A unified analysis of the stability issue
    Bénédic, Y
    Mercklé, J
    PROCEEDINGS OF THE IASTED INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND APPLICATIONS, 2006, : 240 - +