A Fast BP Algorithm and its Application in China Stock Market

Abstract:

Article Preview

Aiming at the drawback of large calculation and slow convergent speed in BP algorithm, this paper proposes a new BP algorithm with the concept of error deviation rate. By carrying out serious methmatical deduction, the paper revises the the chain rule of traditional BP algorithm and proves its theoretical feasibility. Then, this paper applies it to Chinese stock market and has gained quite well convergent speed and predicting effect, consequently proving the new method’s effectiveness.

Info:

Periodical:

Advanced Materials Research (Volumes 121-122)

Edited by:

Donald C. Wunsch II, Honghua Tan, Dehuai Zeng, Qi Luo

Pages:

453-457

DOI:

10.4028/www.scientific.net/AMR.121-122.453

Citation:

S. H. Chen "A Fast BP Algorithm and its Application in China Stock Market", Advanced Materials Research, Vols. 121-122, pp. 453-457, 2010

Online since:

June 2010

Authors:

Export:

Price:

$38.00

[1] H. White, Economic prediction using neural networks: the case of IBM daily stock returns. Neural Networks, IEEE International Conference on, 1988, 2(6): 451-458.

DOI: 10.1109/icnn.1988.23959

[2] T. Kimoto, K. Asakawa, M. Yoda, et al. Stock Market Prediction System with Modular Neural Networks. Neural Networks, IJCNN, International Joint Conference on, 1990, Vol. 1. 1-6.

DOI: 10.1109/ijcnn.1990.137535

[3] N. Baba, M. kozaki, An intelligent forecasting system of stock price using neural networks. Neural Networks, IJCNN, International Joint Conference on, 1992, Vol. 1. 371-377.

DOI: 10.1109/ijcnn.1992.287183

[4] G. Ramazan, Non-linear Prediction of Security Returns with Moving Average Rules. Journal of Forecasting, 1998, Vol. 15, 165-174.

DOI: 10.1002/(sici)1099-131x(199604)15:3<165::aid-for617>3.0.co;2-v

[8] Li. Jun, Li. Yuanxiang, Xu. Jingwen, Zhang. Jinbo, 2000, Parallel Training Algorithm of BP Neural Networks, ,Proceedings of the 3d World Congress on Intelligent Control and Automation,Volume: 2, 872-876.

DOI: 10.1109/wcica.2000.863356

[9] D.E. Rumelhart, C.E. Hinton, and R. J. Williams, 1986, Learning Internal Representations by Error Propagation., In Parallel Distributed Processing: Explorations in the Microstructures of Cognition, Vol. 1 Cambridge, MA, MIT Press, 318-362.

[10] Simon Haykin, 1999, 《 Neural Networks A Comprehensive Foundation 》 Second Edition. Prentice-Hall, 112-122.

[11] N. Ampazis and S. J., 2002, Two highly efficient second-order algorithm for training feedforward networks., IEEE Transactions on Neural Networks, Vol. 13, 1064-1073.

DOI: 10.1109/tnn.2002.1031939

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