Abstract
A modified Recurrent Neural Network (RNN) is used to learn a Self-Routing Interconnection Network (SRIN) from a set of routing examples. The RNN is modified so that it has several distinct initial states. This is equivalent to a single RNN learning multiple different synchronous sequential machines. We define such a sequential machine structure as augmented and show that a SRIN is essentially an Augmented Synchronous Sequential Machine (ASSM). As an example, we learn a small six-switch SRIN. After training we extract the network's internal representation of the ASSM and corresponding SRIN.
Original language | English (US) |
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Pages (from-to) | 793-804 |
Number of pages | 12 |
Journal | Neural Networks |
Volume | 8 |
Issue number | 5 |
DOIs | |
State | Published - 1995 |
All Science Journal Classification (ASJC) codes
- Cognitive Neuroscience
- Artificial Intelligence