DocumentCode
527818
Title
Notice of Retraction
Research on genetic process neural networks and its application in economic prediction
Author
Li Ge
Author_Institution
Sch. of Comput. & Inf. Eng., Harbin Univ. of Commerce, Harbin, China
Volume
4
fYear
2010
fDate
10-12 Aug. 2010
Firstpage
1805
Lastpage
1807
Abstract
Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
Genetic process neural network (GPNN) is presented in this paper, after genetic algorithm was introduced into process neural networks. The concrete realization process and learning algorithm are given, and apply it into the prediction of economic time series. Making use of the characteristics of process neural network with time-varied input function, the prediction of economic time series is achieved, and the effectiveness of the model and the algorithm is proved by per capita GDP prediction.
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
Genetic process neural network (GPNN) is presented in this paper, after genetic algorithm was introduced into process neural networks. The concrete realization process and learning algorithm are given, and apply it into the prediction of economic time series. Making use of the characteristics of process neural network with time-varied input function, the prediction of economic time series is achieved, and the effectiveness of the model and the algorithm is proved by per capita GDP prediction.
Keywords
economic indicators; genetic algorithms; neural nets; time series; concrete realization process; economic prediction; economic time series; genetic algorithm; genetic process neural networks; learning algorithm; per capita GDP prediction; Artificial neural networks; Economic indicators; Genetics; Neurons; Time series analysis; Training; economic time series; genetic process neural network; per capita GDP; prediction;
fLanguage
English
Publisher
ieee
Conference_Titel
Natural Computation (ICNC), 2010 Sixth International Conference on
Conference_Location
Yantai
Print_ISBN
978-1-4244-5958-2
Type
conf
DOI
10.1109/ICNC.2010.5584471
Filename
5584471
Link To Document