The application of ant colony algorithm and artificial neural network in tax assessment

Tax assessment is an important and complex task in tax administration. A large number of data is involved in the process. Hence, a scientific model is in demanding. In this paper, we present a model which integrates the ant colony algorithm into artificial neural network to improve the performance of neural network in judgment of whether the taxpayer is credible. In details, we use ant colony algorithm to train the weights of artificial neural network, and this method could avoid some defects of artificial neural network. The simulation result validates the effectiveness of our method.

[1]  Qu Shifu,et al.  The research of a new tax assessment model , 2009 .

[2]  Zhangsu Bing,et al.  Neural network training using ant algorithm in ATM traffic control , 2001, ISCAS 2001. The 2001 IEEE International Symposium on Circuits and Systems (Cat. No.01CH37196).

[3]  Zemin Liu,et al.  A general CAC approach using novel ant algorithm training based neural network , 1999, IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339).

[4]  Guo Chen-xi Application of Tax Assessment Based on Genetic Algorithm Optimized BP Neural Network , 2008 .

[5]  Liu Zemin,et al.  Neural network training using ant algorithm in ATM traffic control , 2001 .