The Application Research on Fuzzy Theory and Genetic Algorithm

Article Preview

Abstract:

Fuzzy theory is one of the newly adduced self-adaptive strategies,which is applied to dynamically adjust the parameters of genetic algorithms for the purpose of enhancing the performance.In this paper, the financial time series analysis and forecasting as the main case study to the theory of soft computing technology framework that focuses on the fuzzy logic genetic algorithms(FGA) as a method of integration. the financial time series forecasting model based on fuzzy theory and genetic algorithms was built. the ShangZheng index cards as an example. The experimental results show that FGA perform s much better than BP neural network,not only in the precision.but also in the searching speed.The hybrid algorithm has a strong feasibility and superiority.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

1768-1771

Citation:

Online since:

December 2012

Authors:

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2013 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] LI Hongxing, HUANG Hanpang, "New method of fuzzy-based genetic algorithms", Computer Engineering and Design , 2008.29(14),,pp: 3714-3718.

Google Scholar

[2] Liu Li,He Xianping, "Model of time series forecasting based on GA and fuzzy decision tre", Computer Engineering and Design, 2008,29(19),pp.5044-5048.

Google Scholar

[3] Yang Bingru。Xiong Fanlun, "KD(D&K)and double-bases coperating mechanism", Journal of System Engineering and Electronics,. 1999,10(2),pp:48-55

Google Scholar

[4] Srinvas M, Patnaik L, "M,Adaptive Probabilities of Crossover and Mutation in Genetic Algorithms", IEEE Trans on Systems , Man and Cybernetics , 1994 , 24(4),pp:162 - 167.

DOI: 10.1109/21.286385

Google Scholar

[5] Pan H, "A new fuzzy genetic algorithm based on population diversity", Proceedings of 2001 IEEE International Symposium on Computational Intelligence in Robots and Automation, 2001, pp.108-112.

DOI: 10.1109/cira.2001.1013181

Google Scholar

[6] Eric I Chang, Richard P L ippmann, "Using genetic algorithms to improve pattern classification performance", Proceedings of the 1990 Conference on advances in neural information processing systems , Denver, Colorado, United States, pp.797-803.

Google Scholar