DocumentCode
3194994
Title
A proposed GM-GRNN model for prediction of behavior in complex system
Author
Pan, Wei ; Huang, Yupeng ; DeGaris, Hugo
Author_Institution
Pattern Recognition & Intell. Syst. Inst., Xiamen Univ., Xiamen
fYear
2008
fDate
25-27 May 2008
Firstpage
996
Lastpage
999
Abstract
This paper analyses the kernel of the general regression neural network (GRNN) model in detail, and presents its deficiencies in the domain of complex systems forecasting. We import various aspects of the Broyden-Fletcher-Goldfarb-Shanno (BFGS) quasi-Newton method and GM(1,h) algorithms to improve the kernel of the GRNN model. We then apply this modified model to the problem of unemployment forecasting in China, as an example of its ability to model time-varying environments.
Keywords
Newton method; large-scale systems; neural nets; regression analysis; Broyden-Fletcher-Goldfarb-Shanno quasiNewton method; GM-GRNN model; complex systems; general regression neural network; time-varying environments; Equations; Information science; Intelligent networks; Intelligent systems; Kernel; Neural networks; Paper technology; Power system modeling; Predictive models; Unemployment;
fLanguage
English
Publisher
ieee
Conference_Titel
Communications, Circuits and Systems, 2008. ICCCAS 2008. International Conference on
Conference_Location
Fujian
Print_ISBN
978-1-4244-2063-6
Electronic_ISBN
978-1-4244-2064-3
Type
conf
DOI
10.1109/ICCCAS.2008.4657937
Filename
4657937
Link To Document