Bibliography to a novice

[fsegovia@batman.fi.upm.es]

The following articles treat the problem of recurrent NN and their 
learning procedures. They also include modifications to the original
BP applied to recurrent networks (the Rumelhart's trick of unfold in
time the network's operation) which make the learning phase more
practical (less computations, less time):

Williams, R.J., and Zipser, D. 1990. "Gradient-Based learning algo
rithms for recurrent connectionist networks". Tech. Rep. NU-CCS-90-9.
Northeastern University, College of Computer Science, Boston.

Williams, R.J., and Peng, J. 1990. "An Efficient Gradient-Based Algo
rithm for On-Line Training of Recurrent Network Trajectories". Neural
 Computation, Vol. 2, Num 4, 490-501.

For an extension to continuous time see:

Pearlmutter, B.A. 1989. "Learning state space trjectories in recurrent neural
neural networks". Neural Computation, Vol 1, Num 2, 263-269.

Sejnowski, T.J., and Fang, Y. 1990. "Faster learning for dynamic
recurrent backpropagation". Neural Computation, Vol 2, Num 3, 270-273.

The papers mentioned above include experiments and good references for
related works.

Javier Segovia