NDLI: A Modified Particle Swarm Algorithm Combined with Fuzzy Neural Network with Application to Financial Risk Early Warning

Content Provider	IEEE Xplore Digital Library
Author	Huang Fu-yuan Li Rong-jun Liu Han-xia Li Rui
Copyright Year	2006
Description	Author affiliation: Sch. of Bus. Adm., South China Univ. of Technol., Guangzhou City (Huang Fu-yuan; Li Rong-jun; Liu Han-xia; Li Rui)
Abstract	Particle swarm optimization (PSO) algorithm and fuzzy neural network (FNN) system has been widely used to solve complex decision making problems in practice. However, both of them more or less suffer from the slow convergence and occasionally involve in a local optimal solution. To overcome these drawbacks of PSO and FNN, in this study a modified particle swarm optimization algorithm (MPSO) is developed and then combined with neural network to optimize the network weight training process. Furthermore, the new MPSO-FNN model has been applied to financial risk early warning problem, and the results indicate that the predictive accuracies obtained from MPSO-FNN are much higher than the ones obtained from original FNN system. To make this clearer, an illustrative example is also demonstrated in this study. It seems that the proposed new comprehensive evolution algorithm may be an efficient forecasting system in financial time series analysis
Sponsorship	IEEE Comput. Soc. South China Univ. of Technol
Starting Page	168
Ending Page	173
File Size	346057
Page Count	6
File Format	PDF
ISBN	0769527515
DOI	10.1109/APSCC.2006.12
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-12-12
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fuzzy logic Accuracy Neural networks Decision making Time series analysis Evolutionary computation Predictive models Cities and towns Fuzzy neural networks Particle swarm optimization
Content Type	Text
Resource Type	Article

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