Advertisement

Operation Monitoring System Model of Small and Medium-sized Enterprises in Sichuan Province in 2012

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 241)

Abstract

It is a long-term strategic task for Sichuan province to vigorously develop small and medium enterprises. This paper uses the monitoring data of small and medium enterprises in Sichuan province in 2012 to establish a system model consisted of two models by employing support vector machine and group method of data handling. Research results show that the pre-warning level of the operating status of each month which obtained from the system model is almost the same with the official information, so we can use this model to effective early warn the operating status about small and medium-sized enterprises in Sichuan province. Meanwhile, the system model can find key factors which influence and restrict the healthy development of small and medium-sized enterprises in Sichuan province; there are some positive effects to deal with the survival and development crisis which small and medium-sized enterprises in Sichuan province are going through and to develop accurate support policies.

Keywords

Small and medium-sized enterprises Operation monitoring Pre-warning Influence factors System model Data mining 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Altman EI, Haldeman R (1997) Zata analysis: A new model to identify bankruptcy risk of corporations. Journal of Banking and Finance 9:29–54Google Scholar
  2. 2.
    Zhou SH, Yang JH, Wang P (1996) On the pre-warning analysis of financial distress-failure score model. Accountant Research 8:8–11Google Scholar
  3. 3.
    Jiang XH, Ren Q, Sun Z (2002) A forecasting model of financial distress for listed companies. Forecasting 21:56–61Google Scholar
  4. 4.
    Yang SE, XY WG (2003) Financial affairs in early warning model for listed companies-an empirical study on Y market’s model. China Soft Science 1:56–60Google Scholar
  5. 5.
    Tian GL, Wang XQ, Zhao HJ (2002) On corporate financial early warning techniques. Forecasting 21:23–27 (In Chinese)Google Scholar
  6. 6.
    Yang B, Ji H (2001) A study of commercial bank loans risk early warning based on BP neural network. System Engineering-Theory & Practice 5:70–74Google Scholar
  7. 7.
    Fayyad UM, Piatetsky-Shapiro G, Smyth P (1996) From data mining to knowledge discovery: An overview. In: Fayyad UM (ed.), Advances in Knowledge Discovery and Data Mining. AAAI Press/The MIT Press. Menlo Park 1–36Google Scholar
  8. 8.
    Ivakhnenko AG (1971) Polynomial theory of Complex Systems. IEEE Transactions on Systems, Man, and Cybernetics SMC-1(4):364–378Google Scholar
  9. 9.
    Mueller JA, Lemke F (1991) Self-organising data mining: An intelligent approach to extracting knowledge from data. Dresden, BerlinGoogle Scholar
  10. 10.
    Mehra RK (1997) Group method of data handling: Review and experience. In: Decision and Control including the 16th Symposium on Adaptive Processes and A Special Symposium on Fuzzy Set Theory and Applications, 1977 IEEE Conference on 16:29–34Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Business SchoolSichuan UniversityChengduPeople’s Republic ofChina
  2. 2.Small and Medium-sized Enterprise Development CenterChengduPeople’s Republic ofChina

Personalised recommendations