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International Journal of Frontiers in Sociology, 2021, 3(11); doi: 10.25236/IJFS.2021.031102.

Research on Life Cycle Evaluation of Industrial Technology Innovation Alliance Based on Support Vector Machine SVM

Author(s)

Lin Wang1, Abdrahamane Kone2

Corresponding Author:
Lin Wang
Affiliation(s)

1Jilin Jianzhu University, Changchun, China

2Social science University of Bamako, Mali, Bamako

Abstract

Different stages of alliance life cycle have different effects on the decision-making and development of enterprises, so the division of alliance life cycle plays an important role in the survival and development of member enterprises. This paper puts forward the index of dividing alliance life cycle, and constructs the evaluation system of support vector machine. BP neural network and support vector machine are used to establish the evaluation model, and the accuracy of the model is compared to verify the effectiveness of the alliance life cycle division index. The evaluation model based on support vector machine has high accuracy and applicability, which provides an efficient method for alliance life cycle division.

Keywords

Support Vector Machine SVM, Industrial Technology Innovation Alliance, Life Cycle

Cite This Paper

Lin Wang, Abdrahamane Kone. Research on Life Cycle Evaluation of Industrial Technology Innovation Alliance Based on Support Vector Machine SVM. International Journal of Frontiers in Sociology (2021), Vol. 3, Issue 11: 10-17. https://doi.org/10.25236/IJFS.2021.031102.

References

[1] Wu honghong. The life cycle and characteristics of strategic alliance of industrial technology innovation [J].Economic Research Guide, 2015, 21: 40-41.

[2] Zhao yongming. Research on the Impact of IT Capability and Organizational Learning on Project Agility [D]. Dalian University of Technology, 2019.

[3] Liu fengqiu.Research on theory and algorithm of support vector machine based on prior knowledge [D].Harbin Institute of Technology, 2011

[4] Zhang Jian.Life cycle and performance evaluation of Enterprise Technology Alliance [J].Research on science and technology management.2006, 10: 76-77.

[5] Lin Wang, Xinhua Bi.Risk assessment of knowledge fusion in an innovation ecosystem based on a GA-BP neural network [J].Cognitive Systems Research 66 (2021) 201–210.

[6] Cai, B., Pan, G. L., & Fu, F. (2020). Prediction of post-fifire flflexural capacity of RC beam using GA-BPNN machine learning. Journal of Performance of Constructed Facilities. 

[7] Cai, B., Xu, L. F., & Fu, F. Shear Resistance Prediction of Post-fifire Reinforced Concrete Beams Using Artifificial Neural Network. International Journal of Concrete Structures and Materials, 2019, 13(46), 1–13.