Credit Risk Prediction Study Based on Modified Particle Swarm Optimized Fuzzy Neural Networks

Abstract:

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In last decade, neural networks (NNs) have been proposed to predict credit risk because of their advantages of treating non-linear data with self-learning capability. However, the shortcoming of NNs is also significant due to a “black box” syndrome. Moreover, in many situations NNs more or less suffer from the slow convergence and occasionally involve in a local optimal solution, which strongly limited their applications in practice. To overcome NN’s drawbacks, this paper presents a hybrid system that merges fuzzy neural network and niche evolution particle swarm optimization into a comprehensive mode, named as niche evolution particle swarm optimization fuzzy neural network (NEPSO-FNN), the new model has been applied to credit risk prediction based on the data collected from a set of Chinese listed corporations, and the results indicate that the performance of the proposed model is much better than the one of NN model using the cross-validation approach.

Info:

Periodical:

Advanced Materials Research (Volumes 108-111)

Edited by:

Yanwen Wu

Pages:

1326-1331

DOI:

10.4028/www.scientific.net/AMR.108-111.1326

Citation:

Z. B. Xiong "Credit Risk Prediction Study Based on Modified Particle Swarm Optimized Fuzzy Neural Networks", Advanced Materials Research, Vols. 108-111, pp. 1326-1331, 2010

Online since:

May 2010

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Price:

$35.00

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