First-order versus second-order single-layer recurrent neural networks.
We examine the representational capabilities of first-order and second-order single-layer recurrent neural networks (SLRNN's) with hard-limiting neurons. We show that a second-order SLRNN is strictly more powerful than a first-order SLRNN. However, if the first-order SLRNN is augmented with output layers of feedforward neurons, it can implement any finite-state recognizer, but only if state-splitting is employed. When a state is split, it is divided into two equivalent states. The judicious use of state-splitting allows for efficient implementation of finite-state recognizers using augmented first-order SLRNN's.
Goudreau
M W
MW
Princeton Univ., NJ.
Giles
C L
CL
Chakradhar
S T
ST
Chen
D
D