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A modified BPTT algorithm for trajectory learning in block-diagonal recurrent neural networks

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3 Author(s)
Sivakumar, S.C. ; Dept. of Electr. Eng., Tech. Univ. Nova Scotia, Halifax, NS, Canada ; Robertson, W. ; Phillips, W.J.

This paper deals with a discrete time recurrent neural network (DTRNN) with a block-diagonal feedback weight matrix, called the block-diagonal recurrent neural network (BDRNN), that allows a simplified approach to online trajectory learning. The BDRNN is a sparse but structured architecture in which the feedback connections are restricted to between pairs of state variables. The block-diagonal structure of the BDRNN is exploited to modify the backpropagation-through-time (BPTT) algorithm to reduce the storage requirements while still maintaining exactness and locality of gradient computation. To achieve this, a numerically stable method for recomputing the state variables in the backward pass of the BPTT algorithm is presented

Published in:

Electrical and Computer Engineering, 1997. Engineering Innovation: Voyage of Discovery. IEEE 1997 Canadian Conference on  (Volume:1 )

Date of Conference:

25-28 May 1997