Advertisement

Cluster Computing

, Volume 22, Supplement 2, pp 4719–4727 | Cite as

Fuzzy TOPSIS analytic hierarchy process based cooperative development of cultural soft power

  • Wang YanHongEmail author
Article
  • 78 Downloads

Abstract

In order to improve effectiveness of the problem about collaborative development and research on traditional sports of national minorities and cultural soft power of national fitness, a kind of collaborative development and research strategy for cultural soft power based on fuzzy TOPSIS analytic hierarchy process (AHP) was proposed. Firstly, index selection principle was considered; index weight principle, and qualitative and quantitative combination principle, and other principles were determined at the time of establishing comprehensive appraisal and analyzing model for traditional sports of national minorities and cultural soft power of national fitness. Based on that, theoretical model for comprehensive appraisal system of traditional sports of national minorities and cultural soft power of national fitness was established; then, AHP was used to determine weight of indexes in different levels; appraisal objects for traditional sports of all national minorities and cultural soft power of national fitness were endowed with different weights. FUSSY method was used to conduct quantitative treatment for qualitative index; comprehensive ranking of appraisal result was realized based on TOPSIS method. Finally, effectiveness of algorithm is verified through simulation example.

Keywords

TOPSIS appraisal Analytic hierarchy process Cultural soft power National minorities 

References

  1. 1.
    Arunkumar, N., Balaji, V.S., Ramesh, S., Natarajan, S., Likhita, V.R., Sundari, S.: Automatic detection of epileptic seizures using independent component analysis algorithm. In: IEEE-International Conference on Advances in Engineering, Science and Management, ICAESM-2012, art. no. 6215903, pp. 542–544 (2012)Google Scholar
  2. 2.
    Du, Y., Chen, Y., Zhuang, Y., Zhu, C., Tang, F., Huang, J.: probing nanostrain via a mechanically designed optical fiber interferometer. IEEE Photon. Technol. Lett. 29, 1348–1351 (2017)Google Scholar
  3. 3.
    Lv, Z., Halawani, A., Feng, S., Li, H., Réhman, S.U.: Multimodal hand and foot gesture interaction for handheld devices. ACM Trans. Multimed. Comput. Commun. Appl. 11(1s), 10 (2014)Google Scholar
  4. 4.
    Chen, Y., Tang, F., Bao, Y., Tang, Y., Chen, G.: A Fe-C coated long period fiber grating sensor for corrosion induced mass loss measurement. Opt. Lett. 41, 2306–2309 (2016)Google Scholar
  5. 5.
    Arunkumar, N., Jayalalitha, S., Dinesh, S., Venugopal, A., Sekar, D.: Sample entropy based ayurvedic pulse diagnosis for diabetics. In: IEEE-International Conference on Advances in Engineering, Science and Management, ICAESM-2012, art. no. 6215973, pp. 61–62 (2012)Google Scholar
  6. 6.
    Yijiu Zhao, Yu., Hen, Hu, Liu, Jingjing: Random triggering-based sub-nyquist sampling system for sparse multiband signal. IEEE Trans. Instrum. Meas. 66(7), 1789–1797 (2017)Google Scholar

Copyright information

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

Authors and Affiliations

  1. 1.Sanya Aviation & Tourism CollegeHainanChina

Personalised recommendations