Yuyang Zhu, Linxian Zhi, and Weiduo Zhang

View Full Paper


  1. [1] T.Z. Tan, C. Quek, and G.S. Ng, Brain-inspired genetic complementary learning for stock market prediction, The 2005 IEEE Congress on Evolutionary Computation, 3, 2005, 2653– 2660.
  2. [2] G.E. Box, G.M. Jenkins, G.C. Reinsel, and G.M. Ljung, Time series analysis: Forecasting and control (John Wiley and Sons, Hoboken, New Jersey, 2015).
  3. [3] W. Hong et al., Stock Price movements Prediction with Textual Information, Mechatronics Systems and Control, 46, 2018, 3039–3047.
  4. [4] T. Bollerslev, Generalized autoregressive conditional heteroskedasticity, Journal of Econometrics, 31, 1986, 307–327.
  5. [5] D.K. Mohanty, A.K. Parida, and S.S. Khuntia, Financial market prediction under deep learning framework using auto encoder and kernel extreme learning machine, Applied Soft Computing, 99, 2021, 106898.
  6. [6] X. Zhong and D. Enke, A comprehensive cluster and classification mining procedure for daily stock market return forecasting, Neurocomputing, 267, 2017, 152–168.
  7. [7] X. Li et al., Empirical analysis: Stock market prediction via extreme learning machine, Neural Computing and Applications, 27, 2016, 67–78.
  8. [8] J. Sweafford Jr. and F. Fahimi, A neural network model-based control method for a class of discrete-time nonlinear systems, Mechatronic Systems and Control, 49, 2021, 0134–0142.
  9. [9] G.-B. Huang, Q.-Y. Zhu, and C.-K. Siew, Extreme learning machine: Theory and applications, Neurocomputing, 70, 2006, 489–501.
  10. [10] Y. Hu, H. Wang, A. Yazdani, et al., Adaptive full order sliding mode control for electronic throttle valve system with fixed time convergence using extreme learning machine, Neural Computing and Applications, 34, 2021, 5241–5253.
  11. [11] J. Zhang, H. Wang, M. Ma, M. Yu, A. Yazdani, and L. Chen, Active front steering-based electronic stability control for steerby-wire vehicles via terminal sliding mode and extreme learning machine, IEEE Transactions on Vehicular Technology, 69, 2020, 14713–14726.
  12. [12] M. Ye and H. Wang, Robust adaptive integral terminal sliding mode control for steer-by-wire systems based on extreme learning machine, Computers and Electrical Engineering, 86, 2020, 106756.
  13. [13] Y. Hu, H. Wang, Z. Cao, et al., Extreme-learning-machinebased FNTSM control strategy for electronic throttle, Neural Computing and Applications, 32, 2020, 14507–14518.
  14. [14] V. Vapnik, The nature of statistical learning theory (Springer Science and Business Media, New York, 2013).
  15. [15] W. Deng, Q. Zheng, and L. Chen, Regularized extreme learning machine, Proc. IEEE Symposium on CIDM 2009, Nashville, TN, USA, 2009, 389–395.
  16. [16] S. Niazi et al., Neuro-predictive controller for stabilization of Gimbal Mechanism with Cross-coupling, Mechatronics Systems and Control, 49, 2021, 0198–0205.
  17. [17] J. Tavoosi, A novel recurrent type-2 fuzzy neural network for stepper motor control, Mechatronics Systems and Control, 49, 2021, 0097–0105.
  18. [18] M.C. Khani and S. Shabanlou, Assessment of discharge coefficient in trapezoidal and rectangular canals through regularized extreme learning machine, Measurement, 180, 2021, 109493.
  19. [19] Z.Z. Man, Z. Cao et al., A new intelligent pattern classifier based on structured sparse representation, Computers and Electrical Engineering, 84, 2020, 106641.
  20. [20] B. Liu, G. Chen, H. Lin, et al., Prediction of IGBT junction temperature using improved cuckoo search-based extreme learning machine, Microelectronics Reliability, 124, 2021, 114267.

Important Links:

Go Back