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Composites: Mechanics, Computations, Applications: An International Journal

年間 4 号発行

ISSN 印刷: 2152-2057

ISSN オンライン: 2152-2073

The Impact Factor measures the average number of citations received in a particular year by papers published in the journal during the two preceding years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) IF: 0.2 To calculate the five year Impact Factor, citations are counted in 2017 to the previous five years and divided by the source items published in the previous five years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) 5-Year IF: 0.3 The Eigenfactor score, developed by Jevin West and Carl Bergstrom at the University of Washington, is a rating of the total importance of a scientific journal. Journals are rated according to the number of incoming citations, with citations from highly ranked journals weighted to make a larger contribution to the eigenfactor than those from poorly ranked journals. Eigenfactor: 0.00004 The Journal Citation Indicator (JCI) is a single measurement of the field-normalized citation impact of journals in the Web of Science Core Collection across disciplines. The key words here are that the metric is normalized and cross-disciplinary. JCI: 0.08 SJR: 0.153 SNIP: 0.178 CiteScore™:: 1 H-Index: 12

Indexed in

LINEAR DYNAMIC NEURAL NETWORK MODEL OF A VISCOELASTIC MEDIUM AND ITS IDENTIFICATION

巻 1, 発行 1, 2010, pp. 1-23
DOI: 10.1615/CompMechComputApplIntJ.v1.i1.10
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要約

For identification of the behavior of viscoelastic media with small deformations the linear dynamic neural network model is suggested. The model realizes the principle of an adaptively hierarchical superstructure. In order to reach the specified level of the identification error (10−12) the model changes its structure automatically from the 3rd to the 24th order of complexity. The neural network model, compared to other known phenomenological models of viscoelastic media, possesses a higher operation speed, allows use of parallel computational procedures, and realizes an adaptively hierarchical principle of construction. A small error of training the linear nonstationary dynamic model without feedback can be reached only in the presence of a huge initial massif of experimental data.

参考
  1. Joseph, D. D., Fluid Dynamics of Viscoelastic Liquids.

  2. Basistov, Yu. A. and Yanovsky, Yu. G., Ill-posed problems under identification of non-linear rheological models of state.

  3. Vainberg, M. M., Variatsionnyi metod i metod monotonnykh operatorov v teorii nelineinykh uravnenii (Variational Method and Method of Monotonic Operators in the Theory of Nonlinear Equations).

  4. Yanovsky, Yu. G., Basistov, Yu. A., and Filipenkov, P. A., Problem of identification of rheological behavior of heterogeneous polymeric media under finite deformation.

  5. Basistov, Yu. A. and Yanovsky, Yu. G., Identification of mathematical models of viscoelastic media in rheology and electrorheology.

  6. Yanovsky, Yu. G., Polymer Rheology: Theory and Practice.

  7. Hagan, M. T., Demuth, H. B., and Beale, M. H., Neural Network Design.

によって引用された
  1. Chokshi Sagar, Gohil Piyush, Patel Darshan, Experimental investigations of bamboo, cotton and viscose rayon fiber reinforced Unidirectional composites, Materials Today: Proceedings, 28, 2020. Crossref

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