Application of Chaos Particle Swarm Optimized Neural Networks for Evaluating the Credit Risk

Abstract:

Article Preview

This paper proposes a hybrid algorithm based on chaos optimization and particle swarm optimization (PSO) to improve the performance of the neural networks (NN) on evaluating credit risk. The hybrid algorthm not only maintains the advantage of simple structure, but also improves the convergence of the traditional PSO algorithm, and enhances the global optimization capability and accuracy of the algorithm. The test results indicate that the performance of the proposed model is better than the ones of NN model using the BP algorithm and traditional PSO algorithm.

Info:

Periodical:

Key Engineering Materials (Volumes 460-461)

Edited by:

Yanwen Wu

Pages:

687-691

DOI:

10.4028/www.scientific.net/KEM.460-461.687

Citation:

Z. B. Xiong "Application of Chaos Particle Swarm Optimized Neural Networks for Evaluating the Credit Risk", Key Engineering Materials, Vols. 460-461, pp. 687-691, 2011

Online since:

January 2011

Authors:

Export:

Price:

$35.00

In order to see related information, you need to Login.

In order to see related information, you need to Login.