A new framework for the complex system's simulation and analysis

Y. Gao, Qing Li

Research output: Journal article publicationJournal articleAcademic researchpeer-review

1 Citation (Scopus)


© 2018 Springer Science+Business Media, LLC, part of Springer NatureStatistics (or probabilistic theory) and machine learning are currently the main methods of complex system researching. In order to solve the problems of the statistics and machine learning the CUP algorithm in this paper is proposed. The paper gave the basic definition and the measure of the CUP meanwhile it provided the theoretical support for application. Fitting algorithm based on CUP system was introduced. The algorithm can output more information from the fitting process. Contract of fitting to Lorenz system between CUP algorithm and artificial neural net was displayed in the following part. The different fitting effect by different candidate coefficient set is discussed. A typical example of realistic social application and other usages are put up. The CUP algorithm provides the probability and numerical fitting conclusion CUP algorithm the same time while numerical calculation accuracy is optional for different problems.
Original languageEnglish
Pages (from-to)1-8
Number of pages8
JournalCluster Computing
Publication statusAccepted/In press - 26 Feb 2018
Externally publishedYes


  • Complex system
  • CUP fitting algorithm
  • Machine learning
  • Numerical calculation
  • Statistical

ASJC Scopus subject areas

  • Software
  • Computer Networks and Communications


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