Open Access
MATEC Web of Conferences
Volume 44, 2016
2016 International Conference on Electronic, Information and Computer Engineering
Article Number 02093
Number of page(s) 6
Section Electronics, Information and Engineering Application
Published online 08 March 2016
  1. Altman, E., 1968, Financial Ratios, Discriminate Analysis and the Prediction of Corporate Bankruptcy, Journal of Finance 23 (Sept.)
  2. Changjiang Lv, Yan Zhao, Classification of fmancial status of listed companies. J. Accounting Research, 11 (2004).
  3. Song Jiao, Corporate Financial Distress on support vector machine. J, Harbin University of Commerce (Natural Science), 23(2007)
  4. Shue Yang, Li Huang, financial early warning model of listed companies BP neural network. J, Based on System Engineering Theory and Practice, 25 (2005)
  5. Storn R, Price K,. Differential evolution ---- A simple and efficient adaptive scheme for global optimization over continuous spaces,R, Berkeley: University of California, (2006).
  6. Storn R, Price K. Minimizing the real functions of the ICEC'96 contest by differential evolution. C, Proc of IEEE Int Conf on Evolutionary Computation. Nagoya, (1996).
  7. Bo Liu, Ling Wang, Yihui Jin, Research differential evolution algorithm. J, Control and Decision, 22(2007)
  8. Shengshuang Chen, based on XML documents ultimate learning machine classification. J, Computer Engineering, 37 (2011).
  9. G.-B. Huang, et al.,Universal Approximation Using Incremental Networks with Random Hidden Computational Nodes, IEEE Transactions on Neural Networks, 4( 2006).
  10. G.-B. Huang, et al., Extreme Learning Machine: Theory and Applications, Neurocomputing, (2006).
  11. N.-Y. Liang, et al., A Fast and Accurate On-line Sequential Learning Algorithm for Feedforward Networks, IEEE Transactions on Neural Networks, 6,17(2006).
  12. G.-B. Huang, et al., Can Threshold Networks Be Trained Directly?, IEEE Transactions on Circuits and Systems II, 3,53(2006).
  13. K. Hornik, M. Stinchcombe, and H. White, Multilayer feedforward networks are universal approximators, Neural Netw., 5,2(1989).
  14. H. jun Rong, Y.-S. Ong, A.-W. Tan, and Z. Zhu, A fast pruned-extreme learning machine for classification problem, Neurocomputing, 1, 72(2008).
  15. G. Bontempi, M. Birattari, and H. Bersini, Recursive lazy learning for modeling and control, in Proc. Eur. Conf. Mach. Learn., (1998).
  16. Shizhong Liao, Chang Feng. Meta-ELM: ELM with ELM hidden nodes, J, Neurocomputing, (Mar. 27) (2014).
  17. Extreme Learning Machines (Trends & Controversies). J, IEEE intelligent systems, 28 (2013).