Citation: |
[1] |
A. F. Atiya and A. G. Parlos, New results on recurrent network training: Unifying the algorithms and accelerating convergence, IEEE Transcations on Neural Networks, 11 (2000), 697-709.doi: 10.1109/72.846741. |
[2] |
Y. Fang and T. W. S. Chow, Non-linear dynamical systems control using a new RNN temporal learning strategy, IEEE Trans on Circuit and Systems, Part II, 52 (2005), 719-723. |
[3] |
R. A. Conn, K. Scheinberg and N. L. Vicente, "Introduction to Derivative-free Optimization," SIAM, 2009.doi: 10.1137/1.9780898718768. |
[4] |
J. F. G. Freitas, M. Niranjan, A. H. Gee and A. Doucet, Sequential Monte Carlo methods to train neural network models, Neural Computation, 12 (2000), 955-993.doi: 10.1162/089976600300015664. |
[5] |
L. K. Li, Learning sunspot series dynamics by recurrent neural networks, in "Advances in Data Mining and Modeling" (eds. W. K. Ching and K. P. Ng), World Science, (2003), 107-115.doi: 10.1142/9789812704955_0009. |
[6] |
L. K. Li, W. K. Pang, W. T. Yu and M. D. Trout, Forecasting short-term exchange Rates: a recurrent neural network approach, in "Neural Networks in Business Forecasting" (eds. G. P. Zhang), Idea Group Publishing, (2004), 195-212.doi: 10.4018/9781591401766.ch010. |
[7] |
L. K. Li and S. Shao, Dynamic properties of recurrent neural networks and its approximations, International Journal of Pure and Applied Mathematics, 39 (2007), 545-562. |
[8] |
L. K. Li and S. Shao, A neural network approach for global optimization with applications, Neural Network World, 18 (2008), 365-379. |
[9] |
L. K. Li, S. Shao and T. Zheleva, A state space search algorithm and its application to learn the short-term foreign exchange rates, Applied Mathematical Sciences, 2 (2008), 1705-1728. |
[10] |
X. D. Li, J. K. L. Ho and T. W. S. Chow, Approximation of dynamical time-variant systems by continuous-time recurrent neural networks, IEEE Trans on Circuit and Systems, Part II, 52 (2005), 656-660. |
[11] |
X. B. Liang and J. Wang, A recurrent neural network for nonlinear optimization with a continuously differentiable objective function and bound constraints, IEEE Transactions on Neural Networks, 11 (2000), 1251-1262.doi: 10.1109/72.883412. |
[12] |
Z. Liu and I. Elhanany, A Fast and Scalable Recurrent Neural Network Based on Stochastic Meta Descent, IEEE Transactions on Neural Networks, 19 (2008), 1652-1658.doi: 10.1109/TNN.2008.2000838. |
[13] |
S. Wang, Q. Shao and X. Zhou, Knot-optimizing spline networks (KOSNETS) for nonparametric regression, Journal of Industrial and Management Optimization, 4 (2008), 33?52. |
[14] |
X. Wang and E. K. Blum, Discrete-time versus continuous-time models of neural networks, Journal of Computer and System Sciences, 45 (1992), 1-19.doi: 10.1016/0022-0000(92)90038-K. |
[15] |
R. J. Williams and D. Zipser, A learning algorithm for continually running fully recurrent neural networks, Neural Computation, 1 (1989), 270-280.doi: 10.1162/neco.1989.1.2.270. |
[16] |
L. Xu and W. Liu, A new recurrent neural network adaptive approach for host-gate way rate control protocol within intranets using ATM ABR service, Journal of Industrial and Management Optimization, 1 (2005), 389-404. |
[17] |
J. Yao and C. L. Tan, A case study on using neural networks to perform technical forecasting of forex, Neural Computation, 34 (2000), 79-98. |
[18] |
K. F. C. Yiu, S. Wang, K. L. Teo and A. H. Tsoi, Nonlinear system modeling via knot-optimizing B-spline networks, IEEE Transactions on Neural Networks, 12 (2001), 1013-1022.doi: 10.1109/72.950131. |
[19] |
K. F. C. Yiu, Y. Liu and K. L. Teo, A hybrid descent method for global optimization, Journal of Global Optimization, 28 (2004), 229-238.doi: 10.1023/B:JOGO.0000015313.93974.b0. |