next up previous
Next: About this document ... Up: A FIXED SIZE STORAGE Previous: ACKNOWLEDGEMENTS


Gherrity, 1989
Gherrity, M. (1989).
A learning algorithm for analog fully recurrent neural networks.
In IEEE/INNS International Joint Conference on Neural Networks, San Diego, volume 1, pages 643-644.

Pearlmutter, 1989
Pearlmutter, B. A. (1989).
Learning state space trajectories in recurrent neural networks.
Neural Computation, 1(2):263-269.

Pineda, 1990
Pineda, F. J. (1990).
Time dependent adaptive neural networks.
In Touretzky, D. S., editor, Advances in Neural Information Processing Systems 2, pages 710-718. Morgan Kaufmann.

Robinson and Fallside, 1987
Robinson, A. J. and Fallside, F. (1987).
The utility driven dynamic error propagation network.
Technical Report CUED/F-INFENG/TR.1, Cambridge University Engineering Department.

Schmidhuber, 1991
Schmidhuber, J. (1991).
Adaptive decomposition of time.
In Kohonen, T., Mäkisara, K., Simula, O., and Kangas, J., editors, Artificial Neural Networks, pages 909-914. Elsevier Science Publishers B.V., North-Holland.

Schmidhuber, 1992
Schmidhuber, J. (1992).
Learning complex, extended sequences using the principle of history compression.
Neural Computation, 4(2):234-242.

Williams, 1989
Williams, R. J. (1989).
Complexity of exact gradient computation algorithms for recurrent neural networks.
Technical Report Technical Report NU-CCS-89-27, Boston: Northeastern University, College of Computer Science.

Williams and Peng, 1990
Williams, R. J. and Peng, J. (1990).
An efficient gradient-based algorithm for on-line training of recurrent network trajectories.
Neural Computation, 4:491-501.

Williams and Zipser, 1989
Williams, R. J. and Zipser, D. (1989).
Experimental analysis of the real-time recurrent learning algorithm.
Connection Science, 1(1):87-111.

Williams and Zipser, 1994
Williams, R. J. and Zipser, D. (1994).
Gradient-based learning algorithms for recurrent networks and their computational complexity.
In Back-propagation: Theory, Architectures and Applications. Hillsdale, NJ: Erlbaum.

Zipser, 1989
Zipser, D. (1989).
A subgrouping strategy that reduces learning complexity and speeds up learning in recurrent networks.
Neural Computation, 1(4):552-558.

Juergen Schmidhuber 2003-02-13

Back to Recurrent Neural Networks page