Application of Ant Colony Neural Network to Credit Evaluation of Small and Middle Enterprises

DOI

10.2991/iccse-15.2015.38How to use a DOI?

Keywords

Ant colony optimization algorithm; artificial neural network; credit evaluation

Abstract

In order to improve capacity of BP neural networks and make short term credit evaluation of small and middle enterprises forecasting more accurate and fast, presents a credit model-based the ACO neural network. Based on the analysis of the importance of credit and according to the demands of credit evaluation of small and middle enterprises, uses ACO algorithm to train neural network. And then this network model is applied to credit evaluation system of small and middle enterprises. Finally, using training samples and test samples,can detect the ant colony neural network. The result demonstrates that the ACO neural network has strong generalization ability than those of the traditional BP neural network method, and that application of credit evaluation system of small and middle enterprises has very high accuracy rate.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - HongJing Liu
PY  - 2015/07
DA  - 2015/07
TI  - Application of Ant Colony Neural Network to Credit Evaluation of Small and Middle Enterprises
BT  - Proceedings of the 2015 International Conference on Computational Science and Engineering
PB  - Atlantis Press
SP  - 218
EP  - 222
SN  - 2352-538X
UR  - https://doi.org/10.2991/iccse-15.2015.38
DO  - 10.2991/iccse-15.2015.38
ID  - Liu2015/07
ER  -