NDLI: A Modified Adaptive Genetic BP Neural Network with Application to Financial Distress Analysis

Content Provider	IEEE Xplore Digital Library
Author	Zhibin Xiong
Copyright Year	2008
Description	Author affiliation: Sch. of Math. Sci., South China Normal Univ., Guangzhou (Zhibin Xiong)
Abstract	Neural networks (NNs) have been widely used to predict financial distress because of their excellent performances of treating non-linear data with self-learning capability. However, common neural networks often suffer from long convergent processes and occasionally involve in a local optimal solution that more or less limited their applications in practice. To overcome the drawbacks of neural networks, this study develops a new modified genetic algorithm-multi-population adaptive genetic back-propagation algorithm (MAGBPA). In this paper, a hybrid system combining feed-forward neural network and MAGBPA-multi-population adaptive genetic back-propagation neural network (MAGBPNN) is proposed to overcome NN's drawbacks. Furthermore, the new model has been applied to financial distress analysis based on the data collected from a set of Chinese listed corporations, and the results indicate that the performance of MAGBPNN model is much better than the ones of common neural network model.
Starting Page	149
Ending Page	152
File Size	229594
Page Count	4
File Format	PDF
ISBN	9780769533346
DOI	10.1109/WGEC.2008.46
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	Adaptation model Neural networks Artificial neural networks Adaptive genetic BP algorithm Predictive models Genetics Data models Mathematical model Genetic algorithms Financial distress
Content Type	Text
Resource Type	Article

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