Product Information Forecast Based on B-P Neural Network

Article Preview

Abstract:

According to the fast increase of Information data and continuous enhancement of people’s requirement for analyze of information, it is more and more difficult to get requisite information to judgment and decision-making. For solving these problems, the design of an intelligent forecast system based on back propagation neural network is introduced. And the framework, data processing and arithmetic of the system are presented. By continuously running program debugging and optimization settings to modify the parameters, the predicted results and actual test results is to be closer.

You might also be interested in these eBooks

Info:

Periodical:

Advanced Materials Research (Volumes 219-220)

Pages:

1293-1296

Citation:

Online since:

March 2011

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2011 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] Zhang Gai-Hong, Wang, Guo-Li, Zhang Jing, Tian Li. Optimal method to establish joint flood control operation rules with flood forecast information for cascade reservoirs. In Journal of Dalian University of Technology, (2010), pp.123-130.

Google Scholar

[2] B. Taskar, M.-F. Wong, P. Abbeel, and D. Koller. Link prediction in relational data. In Advances in Neural Information Processing Systems 16, (2004), pp.659-666.

Google Scholar

[3] Guo Xiao-Hong, Xu Xiao-Hui, Zhao Shu-Qiang, Yang Ji-Chun. Satellite telemetry parameter trend forecast algorithm based on new information and applications. Journal of Astronautics, v 31, n 8: (2010), p.1939-(1943).

Google Scholar

[4] Yuan, Jingxuan, Wang Bende, Tian Li. Research of flood control operation mode based on forecast information and risk analysis for Baiguishan reservoir. In Journal of Hydroelectric Engineering, v 29, n 2, (2010), pp.132-138.

Google Scholar

[5] Li Zuoyong. Prediction of radiant brightness values of deuterium lamps using B-P neural network. Guangxue Jishu/Optical Technique. n 2, (1998), pp.55-57.

Google Scholar

[6] Wei Wei, Song Chong Zhi. Research & realization on neural network for forecast of AM automatic logistics information-system. In Advanced Materials Research, v 97-101, (2010), pp.2845-2850.

DOI: 10.4028/www.scientific.net/amr.97-101.2845

Google Scholar

[7] Lima Carlos H.R., Lall Upmanu. Climate informed long term seasonal forecasts of hydroenergy inflow for the Brazilian hydropower system. In Journal of Hydrology, v 381, n 1-2, (2010), pp.65-75.

DOI: 10.1016/j.jhydrol.2009.11.026

Google Scholar

[8] Choi Han-Lim, How Jonathan P. Continuous trajectory planning of mobile sensors for informative forecasting. In Automatica, (2010), pp.1266-1275.

DOI: 10.1016/j.automatica.2010.05.004

Google Scholar