Simple Recurrent Network
An SRN or Simple Recurrent Network (or Elman Network) is a kind of recurrent network. According to this site:
"The Simple Recurrent Network (SRN) was conceived and first used by
Jeff Elman, and was first published in a paper entitled Finding
structure in time (Elman, 1990). The paper was ground-breaking for many
cognitive scientists and psycholinguists, since it was the first to
completely break away from a prior commitment to specific linguistic
units (e.g. phonemes or words), and to explore the vision that these
units might be emergent consequences of a learning process operating
over the latent structure in the speech stream. Elman had actually
implemented an earlier model in which the input and output of the
network was a very low-level spectrogram-like representation, trained
using a spectral information extracted from a recording of his own
voice saying 'This is the voice of the neural network'. We will not
discuss the details of this network, except to note that it learned to
produce this utterence after repeated training, and contained no
explicit feature, phoneme, syllable, morpheme, or word-level units.""
Parameters
Number of Input Nodes: Desired number of input nodes.
Number of Hidden Nodes: Desired numder of hidden nodes.
Number of Output Nodes: Desired number of output nodes
Hidden Neuron Type: Set hidden neuron to desired type. Click here for list of neuron types.
Output Neuron Type: Set output neuron to desired type. Click here for list of neuron types.
Right Click Menu
Edit/Train SRN
Open edit dialog to train SRN network.
Rename
Change the name of the subnetwork.
Remove Network
Delete the selected subnetwork.
Clear SRN
Clear the SRN nodes.
View/Edit Data
View and edit training set data (input data and target data).