Industry-University-Research Knowledge Innovation Alliance Risk Assessment Based on BP Neural Network Algorithm

Lingnuo, Xiao; Shi, Jianfeng; Yuzhong, Sun; Mei-rong, Yang

doi:10.1109/iciii.2011.384

Cited by 2 publications

(1 citation statement)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Yan Wei et al used fuzzy comprehensive evaluation method to evaluate intellectual property risks in dynamic alliance [4] . Xiao Lingnuo et al used BP neural network to assess risks in industry-university-research knowledge innovation alliance [5] . Li Bozhou et al evaluated risks in knowledge transfer between cooperation innovation enterprises [6] .…”

Section: Introductionmentioning

confidence: 99%

Evaluation on Risks in Inter-firm Knowledge Transfer Integrating ANP and BP Neural Network

Guo¹,

Qi²

2015

Proceedings of the 2015 International Conference on Electrical, Computer Engineering and Electronics

View full text Add to dashboard Cite

The risk attitude of the knowledge sender influences risks in inter-firm knowledge transfer. By introducing the risk attitude of the knowledge sender, a new risk evaluation method integrating Analytic Network Process and BP neural network was proposed. An example was applied to demonstrate this method by using SuperDecisions and MATLAB. The results showed that it is necessary to consider the risk attitude of the knowledge sender when evaluating risks in inter-firm knowledge transfer and this new method can evaluate risks well.

show abstract

Section: Introductionmentioning

confidence: 99%

Evaluation on Risks in Inter-firm Knowledge Transfer Integrating ANP and BP Neural Network

Guo¹,

Qi²

2015

Proceedings of the 2015 International Conference on Electrical, Computer Engineering and Electronics

View full text Add to dashboard Cite

show abstract

Method of digging tunnels horizontally into the error hypersurface to speed up training and to escape from local minima

Liang

Xia

1994

Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94)

View full text Add to dashboard Cite

In this paper, a general compressing method is reviewed systematically a t first. Then the idea and steps of digging horizontally into the error hypersurface are presented, as well as an example. Since there exists serious and complex nonlinearity in the error hypersurface, training by gradient decsending techniques is often too slow when it is on a plateau and has the risk of trapping into local minima. Digging tunnels into the error hypersurface by means of rotation transformation will jump out of the plateau to speed up training or skip from local minima.

show abstract

Industry-University-Research Knowledge Innovation Alliance Risk Assessment Based on BP Neural Network Algorithm

Cited by 2 publications

References 4 publications

Evaluation on Risks in Inter-firm Knowledge Transfer Integrating ANP and BP Neural Network

Evaluation on Risks in Inter-firm Knowledge Transfer Integrating ANP and BP Neural Network

Method of digging tunnels horizontally into the error hypersurface to speed up training and to escape from local minima

Contact Info

Product

Resources

About