DocumentCode
573407
Title
Analysis of book purchasing model based on improved genetic neural network
Author
Wang, Runhua ; Tang, Yi ; Liu, Guoquan ; Li, Lei
Author_Institution
Principal´´s Office, Chongqing Univ. of Sci. & Technol., Chongqing, China
fYear
2012
fDate
22-24 Aug. 2012
Firstpage
440
Lastpage
443
Abstract
A modeling method based on genetic neural network used for book purchasing is put forward on account of lacking of a set of scientific and uniform purchasing mode and model in current book purchasing process. This method improves standard genetic algorithm first, and then uses the improved standard genetic algorithm as a method of feed forward neural network training and threshold value of feed forward neural network weight adjustment, after that, explores potential relationship between various properties of book and whether it is purchased or not through optimized neural network, thereby to realize the forecast classification whether the book should be purchased or not. Simulation experiment shows good forecast performance and generalization ability of the book purchasing model, thus it is worth for promotion.
Keywords
feedforward neural nets; forecasting theory; generalisation (artificial intelligence); genetic algorithms; learning (artificial intelligence); purchasing; book purchasing model; book purchasing process; feed forward neural network training; feed forward neural network weight adjustment; forecast classification; forecast performance; generalization ability; improved genetic neural network; improved standard genetic algorithm; modeling method; optimized neural network; scientific purchasing mode; simulation experiment; threshold value; uniform purchasing mode; Biological cells; Biological neural networks; Encoding; Genetic algorithms; Genetics; Predictive models; Book purchasing model; genetic neural network; optimization;
fLanguage
English
Publisher
ieee
Conference_Titel
Cognitive Informatics & Cognitive Computing (ICCI*CC), 2012 IEEE 11th International Conference on
Conference_Location
Kyoto
Print_ISBN
978-1-4673-2794-7
Type
conf
DOI
10.1109/ICCI-CC.2012.6311188
Filename
6311188
Link To Document