W98-1202 RCC net of the same size . An Elman network with 9 hidden units scored 64
W98-1202 generalisation and hidden layer size . An Elman network with 4 hidden units scored 60
W98-1202 unit activations to zero when the Elman network encountered i sentence boundary
W98-1202 sequences correctly learned by the Elman network that had learned 72 % of the
S15-2002 single output , composed as an Elman network ( Elman , 1990 ) with tied weights
P15-1113 . Our method employs an RNN or Elman network ( El - man , 1990 ) to represent
W98-1202 Discussion 4.1 . Training the Elman network After 10 iterations , the network
W98-1202 Learning by Elman networks An Elman network having 9 hidden units and trained
W98-1202 hidden units did better than an Elman network with the same number of hidden
W98-1202 3 . Results 3.1 . Learning by Elman networks An Elman network having 9 hidden
W98-1202 number of hidden units for an Elman network is not a satisfactory technique
P15-1130 based on a simple form known as Elman network ( El - man , 1990 ) . Recent
W98-1209 Neural Networks : An Analysis of an Elman Network trained on a Natural Language
W15-5007 , 1990 ) , it is also known as Elman Network or Simple Recurrent Network .
P15-1130 Pascanu et al. , 2013 ) . Moreover , Elman network simply combines previous hidden
W98-1202 networks used in this study were an Elman network and an RCC network . For both
W98-1202 learning task . The neural networks , Elman networks and Recurrent Cascade Correlation
W98-1202 the current word as input . The Elman network appears to be a more useful model
W98-1202 boundary as is sometimes done . The Elman network was trained by standard bacicpropagation
W98-1202 sequences correctly learned by the Elman network included some that were not predicted
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