JACIII Vol.22 No.6 pp. 838-845
doi: 10.20965/jaciii.2018.p0838


Comparative Analysis of Risk Assessment for Technical Standards Alliance Based on BP Neural Network and Fuzzy AHP Methods

Jing Hu*, Lijun Zhou*, and Yilin Wang**

*College of Economics and Management, China Jiliang University
No.258 Xueyuan Street, Xiasha Higher Education District, Hangzhou 310018, China

**Zhongchao Ink Co., Ltd.
No.288 Xiuyan Road, Nanhui District, Shanghai 201315, China

May 19, 2017
December 25, 2017
October 20, 2018
technical standards alliances, risk assessment, BP neural network, fuzzy AHP
Comparative Analysis of Risk Assessment for Technical Standards Alliance Based on BP Neural Network and Fuzzy AHP Methods

BP networks model structure

Establishing unified industrial technical standards for a single enterprise in a highly global integrated market is becoming increasingly difficult. In recent years, leading enterprises have often built technical standards alliances around a key core technology to develop industrial standards cooperatively in order to learn from each other and optimize their resource allocation. Although such technical standards alliances result in huge gains to their members, their internal and external risks threaten both the alliances and their members. As compared to other forms of strategic alliances, the risk of such an alliance has fuzzy characteristics and is difficult to fully and accurately identify. This paper uses a fuzzy pattern-recognition method to evaluate and summarize the risks of technical standards alliances. A fuzzy analytic hierarchy process (AHP) evaluation and back propagation (BP) logic fuzzy neural network methods are used to construct a risk-evaluation model of technical standards alliances while considering an alliance around new-energy automobiles in Zhejiang as an empirical example. The two evaluation models are then contrastively analyzed, and cross validation of the evaluation results is performed in order to provide theoretical guidance and support for the application of two fuzzy evaluation models in practice.

Cite this article as:
J. Hu, L. Zhou, and Y. Wang, “Comparative Analysis of Risk Assessment for Technical Standards Alliance Based on BP Neural Network and Fuzzy AHP Methods,” J. Adv. Comput. Intell. Intell. Inform., Vol.22, No.6, pp. 838-845, 2018.
Data files:
  1. [1] Y. Dai and P. Zhang, “Review of Basic Issues of Technical Standards Alliance,” Science and Technology Management Research, Vol.10, pp. 119-121, 2010.
  2. [2] D. Li and D. Zeng, “Research on governance structure and governance mechanism of High-tech Industry Technical Standards Alliance,” Science and Technology Management Research, Vol.10, pp. 78-81, 2012.
  3. [3] Z. Li and S. Yang, “National strategies and enterprise strategies of mobile communication technology standardization,” Research Management, Vol.4, pp. 45-51, 2005.
  4. [4] W. Yang and Z. Li, “Mechanism and Pattern of Technical Standards Strategic Alliance based on the Resource Dependence Theory,” China Youth Science and Technology, Vol.6, pp. 26-36, 2007.
  5. [5] S. Wu and S. Zhao, “Gray cluster analysis on partners choosing of the core enterprise technology alliance -based on the core competence perspective,” Science and Technology Management Research, Vol.8, pp. 128-130, 2016.
  6. [6] M. Zeng, D. Peng, and Y. Zhang, “Analysis on the Value of Technical Standards Alliances,” Soft Science, Vol.20, pp. 5-8, 2006.
  7. [7] K. Xie, P. Gui, and T. Zhao, “A life cycle analysis of high-tech enterprises’ strategic alliance,” Studies in Science of Science, Vol.19, pp. 32-36, 2015.
  8. [8] M. Grabowski and K. H. Roberts, “Risk Mitigation in Virtual Organizations,” Organization Science, Vol.10, pp. 704-720, 2011.
  9. [9] J. Chen and W. Dong, “Establishment and Management of Virtual Enterprise,” Tsinghua University Press, pp. 20-32, 2010.
  10. [10] P. Jia, “Operation Design and Operating Rules Design of Virtual Enterprise,” Technical and Economic, Vol.201, pp. 37-38, 2009.
  11. [11] F. Ye and D. Sun, “Research on Risk Management Virtual Enterprise-Lifecycle-oriented,” Science & Science and Technology Management, Vol.25, pp. 130-133, 2007.
  12. [12] X. Xu, “Risk Analysis and Research on Avoiding Strategy of Dynamic Alliance,” Modern Management Science, Vol.4, pp. 70-72, 2005.
  13. [13] Q. Zhang, “Risk Management Mechanism and Prevention System of Enterprise Dynamic Alliance,” China Economic Press, 2009.
  14. [14] W. Feng and J. Chen, “Risk Management and Control of Virtual Enterprise,” Management Science, Vol.4, pp. 1-8, 2009.
  15. [15] H. Cao and D. Wang, “Dynamic Alliance Project Risk Optimization Model,” Systems Engineering-Theory Methodology Applications, Vol.10, pp. 56-59, 2010.
  16. [16] K. Jian and J. Li, “Application of Fuzzy Comprehensive Evaluation Method in Virtual Enterprise Risk Management,” Industrial Engineering, Vol.3, pp. 40-43, 2014.
  17. [17] M. Huang and H. Yang, “Risk Evaluation of Virtual Enterprise-based on Fuzzy comprehensive evaluation,” Practice and Theory of Mathematics, Vol.6, pp. 45-51, 2004.
  18. [18] D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning Internal Representations by Error Propagation,” Parallel Distributed Processing: Explorations in the Microstructure of Cognition, MIT Press, Vol.1, pp. 318-362, 1986.
  19. [19] H. N. Ronert, “Theory of the Back-proportion Neural Network,” Proc. of the Int. Joint Conf. on Neural Networks, New York: IEEE Press, 1989.
  20. [20] Q. Zhu and Z. Xu, “Analysis and Comparison of Corporation Control Activities Assessment Based on Fuzzy Comprehensive Method and BP Neural Network Method,” Management Review, Vol.25, No.8, pp. 113-122, 2013.

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Last updated on Nov. 16, 2018