DocumentCode :
3167600
Title :
Particle swarm optimization based BP neural network on credit risk evaluating control system
Author :
Yu, Zhao-Ji ; Ma, Xiao-qing
Author_Institution :
Sch. of Adm., Shenyang Univ. of Technol., Shenyang, China
fYear :
2011
fDate :
8-10 Aug. 2011
Firstpage :
4694
Lastpage :
4697
Abstract :
The purpose of this paper is to optimize evaluating control system, describe the topology structure of particle swarm optimization (PSO) based BP neural network (PSOBPNN), introduce the principle of PSOBPNN, and give the implement step of PSOBPNN. The algorithm is applied to electronic commerce enterprise credit risk evaluating system, then compare its result with conventional BP neural network. The comparing result shows that PSOBPNN fits to complex system such as credit evaluating control system, it improves in a certain extent on training speed and precision, it can improve the quality of electronic business enterprise credit evaluating control system, and it fits to solve some problems in which evaluating indexes weights are difficult to be determined or there exists complex non-linear relation among them.
Keywords :
backpropagation; electronic commerce; finance; particle swarm optimisation; risk analysis; PSOBPNN; complex nonlinear relation; electronic business enterprise; electronic commerce enterprise credit risk evaluating system; particle swarm optimization based BP neural network; Decision support systems; BP neural network; Credit Evaluating control system; Particle swarm;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Artificial Intelligence, Management Science and Electronic Commerce (AIMSEC), 2011 2nd International Conference on
Conference_Location :
Deng Leng
Print_ISBN :
978-1-4577-0535-9
Type :
conf
DOI :
10.1109/AIMSEC.2011.6010276
Filename :
6010276
Link To Document :
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