Một phương pháp lai để đào tạo mạng nơ-ron hồi tiếp: ứng dụng trong dự đoán nhiều bước về phía trước cho các tập dữ liệu ồn ào và lớn

Nerrand O, P.Roussel-Ragot, Urbani D, L.Personnaz, Dreyfus G (1994) Training recurrent neural networks: why and how? an illustration in dynamical process modeling. IEEE Trans Neural Netw 5(2):178–184 Inoue H, Narihisa H (2000) Predicting chaotic time series by ensembles self-generating neural networks. International joint conference on neural network (IJCNN’00), p 2231 Connor T, Douglas R (1994) Reccurent neural networks and robust time series prediction. IEEE Trans Neural Netw 5(2):240–254 Deshpande M, Karipys G (2004) Selective Markov models for predicting web pages accesses. ACM Trans Internet Technol 4(2):163–184 Tran N, Reed A (2001) ARIMA time series modeling and forecasting for adaptive I/O prefetching. In: Proceedings of the international conference on supercomputing, pp 473–485 Ho SL, Xie M, Goh TN (2002) A comparative study of neural network and Box–Jenkins ARIMA modeling in time series prediction. In: Proceedings of the 26th international conference on computers and industrial engineering 42(2–4):371–375 Owens AJ (2000) Empirical modeling of very large data sets using neural network. In: Proceedings of the IEEE-INNS-ENNS international joint conference on neural network, vol 6, pp 302–307 Principe JC, Wang L, Motter MA (1998) Local dynamic modelling with self-organizing maps and applications to nonlinear system identification and control. Proc IEEE 86(11) Cho K, Wang B (1996) Radial basis function based adaptive fuzzy systems and their application to system identification and prediction. Fuzzy Sets Syst 83:325–339 Kohonen T (1999) Self-organizing maps and learning vector quantization for feature sequences. Neural Processing Lett 10(2):151–159 Leung H, Lo T, Wang S (2001) Prediction of noisy chaotic time series using an optimal radial basis function neural network. IEEE Trans Neural Netw 12(5):1163–1172 Lin T, Horn G, Tino P, Lee Giles C (1996) Learning long-term dependencies in NARX recurrent neural networks. IEEE Trans Neural Netw 7(6):1329 Lin T, Giles CL, Horne B, Kung SY (1997) A delay damage model selection algorithm for NARX neural networks. IEEE Trans Signal Process 45(11):2719–2730 Parlos AG, Rais OT, Atiya AF (2000) Multi-step-ahead prediction using dynamic recurrent neural networks. Neural Netw 13(7):765–786 Lee Giles C, Steve Lawrence, Tsoi AC (2001) Noisy time series prediction using a recurrent neural network and grammatical inference. Mach Learning 44(1/2):161–183 Motter AM (2000) Predictive multiple model switching control with the self-organizing map. In: Proceedings of international joint conference in neural network, 4(4):IEEE-INNS-ENNS, 4317 Wichard JD, Ogorzalek M (2004) Time series prediction with ensemble models. In: Proceedings of international joint conference in neural network, Busdapest Chtourou S, Chtourou M, Hammami O (2006) Neural network based memory access prediction support for soc dynamic reconfiguration. In: Proceedings of the international joint conference on neural network, pp 5130–5136 Sherwood T, Sair S, Calder B (2003) Phase tracking and prediction. In: Proceedings of the 30th international symposium on computer architecture (ISCA), pp 336–347 Jimenez DA, Lin C (2002) Neural methods for dynamic branch prediction. ACM Trans Comput Syst 20(4):369–397 Sakr MF, Giles CL, Levitan SP, Horne BG, Maggini M, Chiarulli DM (1996) On-line prediction of multiprocessor memory access patterns. In: Proceedings of the IEEE international conference on neural networks, p 1564 Sakr MF, Levitan SP, Chiarulli DM, Horne BG, Giles CL (1997) Predicting multiprocessor memory access patterns with learning models. In: Fisher D (ed) Proceedings of the fourteenth international conference on machine learning. Morgan Kaufmann, San Francisco, pp 305–312 Atiya FA, Parlos AG (2000) New results on recurrent network training: unifying the algorithms and accelerating convergence. IEEE Trans Neural Netw 11(3):697–709 Chalup SK, Blair AD (2003) Incremental training of first order recurrent neural networks to predict a context-sensitive language. Neural Netw 16:955–972 Liu D, Chang T-S, Zhang Y (2002) A constructive algorithm for feedforward neural networks with incremental training. IEEE Trans Circuits Syst I Fundamental Theory Appl 49(12):1876–1879 Dittenbach M, Merkl D, Rauber A (2000) The growing hierarchical self organizing map. In: Proceedings of the international joint conference on neural networks, pp 15–19 Hammond J, MacClean D, Valova I (2006) A parallel implementation of a growing SOM promoting independent neural networks over distributed input space. In: Proceedings of the international joint conference on neural networks, pp 958–965 http://rogue.colorado.edu/Pin/index.html