An Efficient Gradient-Based Algorithm for On-Line Training of Recurrent Network Trajectories

A novel variant of the familiar backpropagation-through-time approach to training recurrent networks is described. This algorithm is intended to be used on arbitrary recurrent networks that run continually without ever being reset to an initial state, and it is specifically designed for computationally efficient computer implementation. This algorithm can be viewed as a cross between epochwise backpropagation through time, which is not appropriate for continually running networks, and the widely used on-line gradient approximation technique of truncated backpropagation through time.

[1]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[2]  Pineda Generalization of back-propagation to recurrent neural networks. , 1987, Physical review letters.

[3]  Fernando J. Pineda,et al.  GENERALIZATION OF BACKPROPAGATION TO RECURRENT AND HIGH-ORDER NETWORKS. , 1987 .

[4]  Fernando J. Pineda,et al.  Dynamics and architecture for neural computation , 1988, J. Complex..

[5]  M. Gherrity A learning algorithm for analog, fully recurrent neural networks , 1989, International 1989 Joint Conference on Neural Networks.

[6]  Barak A. Pearlmutter Learning State Space Trajectories in Recurrent Neural Networks , 1989, Neural Computation.

[7]  Ronald J. Williams,et al.  Experimental Analysis of the Real-time Recurrent Learning Algorithm , 1989 .

[8]  James L. McClelland,et al.  Finite State Automata and Simple Recurrent Networks , 1989, Neural Computation.

[9]  David Zipser,et al.  A Subgrouping Strategy that Reduces Complexity and Speeds Up Learning in Recurrent Networks , 1989, Neural Computation.

[10]  Ronald J. Williams,et al.  A Learning Algorithm for Continually Running Fully Recurrent Neural Networks , 1989, Neural Computation.

[11]  L. B. Almeida A learning rule for asynchronous perceptrons with feedback in a combinatorial environment , 1990 .

[12]  Ronald J. Williams,et al.  Gradient-based learning algorithms for recurrent connectionist networks , 1990 .

[13]  S Z Qin,et al.  Comparison of four neural net learning methods for dynamic system identification , 1992, IEEE Trans. Neural Networks.