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
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;
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
DOI :
10.1109/ICCCAS.2008.4657937
