Advertisement

Cluster Computing

, Volume 22, Supplement 4, pp 9097–9104 | Cite as

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

  • Yuan GaoEmail author
  • Qing Li
Article
  • 71 Downloads

Abstract

Statistics (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.

Keywords

Complex system CUP fitting algorithm Statistical Machine learning Numerical calculation 

References

  1. 1.
    Jalalifar, H., Mojedifar, S., Sahebi, A.A.: Prediction of rock mass rating using fuzzy logic and multi-variable rmr regression model. Int. J. Min. Sci. Technol. 24(2), 237–244 (2014)Google Scholar
  2. 2.
    Dumitru, C., Maria, V.: Advantages and disadvantages of using neural networks for predictions. Ovidius Univ. Ann. Econ. Sci. Ser. 13(1), 444–449 (2013)Google Scholar
  3. 3.
    Moghtadaei, M., Golpayegani, M.R.: Complex dynamic behaviors of the complex Lorenz system. Scientia Iranica 19(3), 733–738 (2012)Google Scholar
  4. 4.
    Bengio, Y.: Learning deep architectures for AI. Foundations & Trends®. Mach. Learn. 2(1), 1–127 (2009)MathSciNetzbMATHGoogle Scholar
  5. 5.
    Deutsch: Supervised sequence labelling with recurrent neural networks. Springer, Berlin (2012)Google Scholar
  6. 6.
    Holland, J.H.: Studying complex adaptive systems. J. Syst. Sci. Complex 19(1), 1–8 (2006)MathSciNetzbMATHGoogle Scholar
  7. 7.
    Kareem, R.A., Adeoti, O.A.: A Study of Discriminant Analysis and Artificial Neural Network in Prediction of Stock Market in Nigeria. J. Stat. Econ. Methods 5(1), 29–47 (2016)Google Scholar
  8. 8.
    Badrulalammiah, M., Zakir Hossain, M., Amjad Hossain, M., et al.: Price prediction of stock market using hybrid model of artificial intelligence. Int. J. Comput. Appl. 111(3), 5–9 (2015)Google Scholar
  9. 9.
    Tian, Q., Shang, P., Feng, G.: Financial time series analysis based on information categorization method. Phys. A Stat. Mech. Appl. 416, 183–191 (2014)MathSciNetzbMATHGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Computer Engineering and ScienceShanghai UniversityShanghaiChina

Personalised recommendations