How embedded memory in recurrent neural network architectures helps learning long-term temporal dependencies

[1]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[2]  Sun-Yuan Kung,et al.  A delay damage model selection algorithm for NARX neural networks , 1997, IEEE Trans. Signal Process..

[3]  Hava T. Siegelmann,et al.  Computational capabilities of recurrent NARX neural networks , 1997, IEEE Trans. Syst. Man Cybern. Part B.

[4]  Richard D. Braatz,et al.  On the "Identification and control of dynamical systems using neural networks" , 1997, IEEE Trans. Neural Networks.

[5]  Paolo Frasconi,et al.  Computational capabilities of local-feedback recurrent networks acting as finite-state machines , 1996, IEEE Trans. Neural Networks.

[6]  Peter Tiño,et al.  Learning long-term dependencies in NARX recurrent neural networks , 1996, IEEE Trans. Neural Networks.

[7]  C. Lee Giles,et al.  Rule Revision With Recurrent Neural Networks , 1996, IEEE Trans. Knowl. Data Eng..

[8]  Peter Tiño,et al.  Learning long-term dependencies is not as difficult with NARX networks , 1995, NIPS.

[9]  Yoshua Bengio,et al.  Hierarchical Recurrent Neural Networks for Long-Term Dependencies , 1995, NIPS.

[10]  Peter Tiňo,et al.  Learning long-term dependencies is not as difficult with NARX recurrent neural networks , 1995 .

[11]  Giovanni Soda,et al.  Unified Integration of Explicit Knowledge and Learning by Example in Recurrent Networks , 1995, IEEE Trans. Knowl. Data Eng..

[12]  Andreas S. Weigend,et al.  Time Series Prediction: Forecasting the Future and Understanding the Past , 1994 .

[13]  Ah Chung Tsoi,et al.  Locally recurrent globally feedforward networks: a critical review of architectures , 1994, IEEE Trans. Neural Networks.

[14]  Yoshua Bengio,et al.  Learning long-term dependencies with gradient descent is difficult , 1994, IEEE Trans. Neural Networks.

[15]  C. L. Giles,et al.  Dynamic recurrent neural networks: Theory and applications , 1994, IEEE Trans. Neural Networks Learn. Syst..

[16]  P. S. Sastry,et al.  Memory neuron networks for identification and control of dynamical systems , 1994, IEEE Trans. Neural Networks.

[17]  C. Lee Giles,et al.  An experimental comparison of recurrent neural networks , 1994, NIPS.

[18]  Lars Kai Hansen,et al.  Recurrent Networks: Second Order Properties and Pruning , 1994, NIPS.

[19]  C. L. Giles,et al.  Inserting rules into recurrent neural networks , 1992, Neural Networks for Signal Processing II Proceedings of the 1992 IEEE Workshop.

[20]  Hava T. Siegelmann,et al.  On the computational power of neural nets , 1992, COLT '92.

[21]  José Carlos Príncipe,et al.  The gamma model--A new neural model for temporal processing , 1992, Neural Networks.

[22]  P. Werbos,et al.  Long-term predictions of chemical processes using recurrent neural networks: a parallel training approach , 1992 .

[23]  Jürgen Schmidhuber,et al.  Learning Complex, Extended Sequences Using the Principle of History Compression , 1992, Neural Computation.

[24]  Giovanni Soda,et al.  Local Feedback Multilayered Networks , 1992, Neural Computation.

[25]  Jürgen Schmidhuber,et al.  Learning Unambiguous Reduced Sequence Descriptions , 1991, NIPS.

[26]  Michael C. Mozer,et al.  Induction of Multiscale Temporal Structure , 1991, NIPS.

[27]  Ah Chung Tsoi,et al.  FIR and IIR Synapses, a New Neural Network Architecture for Time Series Modeling , 1991, Neural Computation.

[28]  R. D. Lorenz,et al.  A structure by which a recurrent neural network can approximate a nonlinear dynamic system , 1991, IJCNN-91-Seattle International Joint Conference on Neural Networks.

[29]  Hong-Te Su,et al.  Identification of Chemical Processes using Recurrent Networks , 1991, 1991 American Control Conference.

[30]  Jeffrey L. Elman,et al.  Finding Structure in Time , 1990, Cogn. Sci..

[31]  Michael I. Jordan.  Attractor dynamics and parallelism in a connectionist sequential machine , 1990 .

[32]  Stephen A. Billings,et al.  Non-linear system identification using neural networks , 1990 .

[33]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .