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  • 标题:A New Class of “Growth Functions” with Polynomial Variable Transfer Generated by Real Reaction Networks
  • 本地全文:下载
  • 作者:Nikolay Kyurkchiev
  • 期刊名称:Cybernetics and Information Technologies
  • 印刷版ISSN:1311-9702
  • 电子版ISSN:1314-4081
  • 出版年度:2020
  • 卷号:20
  • 期号:6
  • 页码:74-81
  • DOI:10.2478/cait-2020-0062
  • 语种:English
  • 出版社:Bulgarian Academy of Science
  • 摘要:In [4, 5], two classes of growth models with “exponentially variable transfer” and “correcting amendments of Bateman-Gompertz-Makeham-type” based on a specific extended reaction network have been studied [1]. In this article we will look at the new scheme with “polynomial variable transfer”. The consideration of such a dynamic model in the present article is dictated by our passionate desire to offer an adequate model with which to well approximate specific data in the field of computer viruses propagation, characterized by rapid growth in the initial time interval. Some numerical examples, using CAS Mathematica illustrating our results are given.
  • 关键词:Reaction networks; Generalized growth model; Exponentially and polynomial variable transfers.
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