Abstract
BP neural network as a data-mining technique can be used to factor analysis, but multi-layer BP neural network with hidden layer and between layers of neurons connected by weights staggered, so input variables on output variables. The size of impact is not intuitive. This research is based on different input variables change in value of the output variable degree of sensitivity analysis, the sensitivity of the size of the response by the input variables influence the output variables. Finally, application of the method on the cost factors affecting the sensitivity analysis, the factors that influence the ranking of a more reasonable level, the sensitivity of the BP neural network analysis of factors affecting the feasibility of certain.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Hornik, K., Stinchcome, M., White, H.: Multilayer feedford networks are universal approximaters. Neural Networks 2, 359–379 (1989)
Therneau, T.M., Grambsch, P.M., Fleming, T.: Martingale-based residuals for survival models. Biometrika 77, 147–153 (1990)
Xiaohua, W., Yigang, H.: Optimal design of frequency-response-masking filters using neural networks. Acta Electronica Sinica 36(3), 486–489 (2008)
Ge, S.S., Hang, C.C., Lee, T.H., et al.: Stable Adaptive Neural Network Control. Kluwer Academic Publishers, Boston (2001)
Guang, T., Feihu, Q.: Feature transformation and SVM based hierarchical pedestrian detection with a monocular moving camera. Acta Electronica Sinica 36(5), 1024–1028 (2008)
Saltelli, A., Ratto, M., Tarantola, S., et al.: Sensitivity analysis practices: Strategies for model-based inference. Reliability Engineering and System Safety 91(10-11), 1109–1125 (2006)
Ratto, M., Tarantola, S., Saltelli, A.: Sensitivity analysis in model calibration: GSA-GLUE approach. Computer Physics Communications 136(3), 212–224 (2001)
Cariboni, J., Catelli, D., Liska, R., et al.: The role of sensitivity analysis in ecological modeling. Ecological Modelling 203(1-2), 167–182 (2007)
Chaohui, Y., Hong, L., Yi, H., et al.: Analysis of Influencing Factors of Medical Expenses of Three Single Internal Diseases by Cumulative Logistic Regression Model in Certain Tertiary Hospital in Wuhan City. Medicine and Society 23(3), 13–15 (2010)
Fengjiang, W., Zhuang, C., Changping, L., et al.: Analysis of Hospitalization Cost and Relative Factors on Acute Appendicitis. Modern Preventive Medicine 37(5), 847–849 (2010)
Chen, T., Han, D.: The parameter sensitivity analysis of the neural network method and its engineering application. Chinese Journal of Computational Mechanics 21(6), 752–756 (2004)
Zhu, C., Ni, Z.: Based on the BP neural network model analysis and application of the influence. Chinese Journal of Health Statistics 19(6), 342–344 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wu, J., Wang, G., Yin, S., Yu, L. (2010). BP Neural Network Sensitivity Analysis and Application. In: Zhu, R., Zhang, Y., Liu, B., Liu, C. (eds) Information Computing and Applications. ICICA 2010. Communications in Computer and Information Science, vol 105. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16336-4_59
Download citation
DOI: https://doi.org/10.1007/978-3-642-16336-4_59
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-16335-7
Online ISBN: 978-3-642-16336-4
eBook Packages: Computer ScienceComputer Science (R0)