Research on the Early Warning Model Based on the Fuzzy Rough Set and BP Neural Network

Author

Jiang, Guorui ; Ma, Liduan

Author_Institution

Sch. of Econ. & Manage., Beijing Univ. of Technol., Beijing, China

fYear

2010

fDate

22-23 May 2010

Firstpage

1

Lastpage

4

Abstract

In this paper, a method of complement of fuzzy rough set and BP neural network was proposed, and an early warning model of electronic information products on Technical Barriers to Trade (TBT) was given by the method. The attribute reduction for indicators of early warning based on fuzzy rough set can not only enhance the veracity of attribute reduction, but also improve the accuracy of the training of BP neural network through reducing the input dimension of BP neural network at the same time. The new TBT early warning model of electronic information products was proved more feasible and effective.

Keywords

backpropagation; electronic products; electronics industry; fuzzy set theory; neural nets; production engineering computing; rough set theory; BP neural network; attribute reduction; early warning model; electronic information products; fuzzy rough set theory; technical barriers to trade; Alarm systems; Electronics industry; Fuzzy neural networks; Fuzzy sets; Industrial economics; Industrial electronics; Management training; Mathematical model; Neural networks; Technology management;

fLanguage

English

Publisher

ieee

Conference_Titel

e-Business and Information System Security (EBISS), 2010 2nd International Conference on

Conference_Location

Wuhan

Print_ISBN

978-1-4244-5893-6

Electronic_ISBN

978-1-4244-5895-0

Type

conf

DOI

10.1109/EBISS.2010.5473722

Filename

5473722