NDLI: A Particle Swarm Optimized Fuzzy Neural Network for Credit Risk Evaluation

Content Provider	IEEE Xplore Digital Library
Author	Fu-yuan Huang
Copyright Year	2008
Description	Author affiliation: Sch. of Econ. & Commerce, South China Univ. of Technol., Guangzhou (Fu-yuan Huang)
Abstract	Neural networks (NNs) have been widely used to financial risk management because of their excellent performances of treating non-linear data with self-learning capability. However, the shortcoming of neural networks is also significant due to a "black box" syndrome and the difficulty in dealing with qualitative information, which limited its applications in practice. To overcome these drawbacks of NNs, in this study a particle swarm optimized fuzzy neural network (PSO-FNN) are proposed to evaluate credit risk. the results indicate that the predictive accuracies obtained from PSO-FNN are much higher than the ones obtained from NNs. To make this clearer, an illustrative example is also demonstrated in this study.
Starting Page	153
Ending Page	157
File Size	255146
Page Count	5
File Format	PDF
ISBN	9780769533346
DOI	10.1109/WGEC.2008.25
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-09-25
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Analytical models Fuzzy Neural Network Artificial neural networks Credit Risk Neural Networks Fuzzy neural networks Data models Mathematical model Particle swarm optimization Particle Swarm Optimization
Content Type	Text
Resource Type	Article

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