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Foundation ::
Artificial Intelligence and Expert Systems ::
F77NNS
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F77NNS
a Fortran-77 Neural Network Simulator
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Moderators:
Adopt This Application!
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SOURCE CODE AVAILABLE
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F77NNS (A FORTRAN-77 Neural Network Simulator) simulates the popular
back error propagation neural network. F77NNS is an ANSI-77 FORTRAN program
designed to take advantage of vectorization when run on machines having
this capability, but it will run on any computer with an ANSI-77 FORTRAN
Compiler.
Artificial neural networks are formed from hundreds or thousands
of simulated neurons, connected to each other in a manner similar to biological
nerve cells.
Problems which involve pattern matching or system modeling
readily fit the class of problems which F77NNS is designed to solve.
The program's formulation trains a neural network using Rumelhart's backpropagation
algorithm. Typically the nodes of a network are grouped together
into clumps called layers. A network will generally have an input
layer through which the various environmental stimuli are presented to the
network, and an output layer for determining the network's response. The
number of nodes in these two layers is usually tied to features of the problem
being solved. Other layers, which form intermediate stops between the
input and output layers, are called hidden layers.
The back-propagation training algorithm can require massive computational
resources to implement a large network such as a network capable of
learning text-to-phoneme pronunciation rules as in the famous Sehnowski
experiment. The Sehnowski neural network learns to pronounce 1000 common
English words. The standard input data defines the specific inputs that
control the type of run to be made, and input files define the NN in terms of
the layers and nodes, as well as the input/output (I/O) pairs.
The program has a restart capability so that a neural network can be
solved in stages suitable to the user's resources and desires. F77NNS allows
the user to customize the patterns of connections between layers of
a network. The size of the neural network to be solved is limited only by
the amount of random access memory (RAM) available to the user.
F77NNS carries the NASA case number MSC-21638. It was originally released as part of the NASA COSMIC collection.
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