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Foundation ::
Artificial Intelligence and Expert Systems ::
NETS
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NETS
A Neural Network Development Tool, Version 3.0
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Moderators:
Adopt This Application!
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SOURCE CODE AVAILABLE
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NETS, A Tool for the Development and Evaluation of Neural Networks,
provides a simulation of Neural Network algorithms plus an environment for
developing such algorithms. Neural Networks are a class of systems modeled
after the human brain. Artificial Neural Networks are formed from hundreds
or thousands of simulated neurons, connected to each other in a manner similar
to brain neurons. Problems which involve pattern matching readily fit
the class of problems which NETS is designed to solve.
NETS uses the back propagation learning method for all of the networks
which it creates. The nodes of a network are usually grouped together into
clumps called layers. Generally, a network will have an input layer through
which the various environment 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 some features of the problem being
solved. Other layers, which form intermediate stops between the input and
output layers, are called hidden layers. NETS allows the user to customize
the patterns of connections between layers of a network. NETS also provides
features for saving the weight values of a network during the learning process,
which allows for more precise control over the learning process.
NETS is an interpreter. Its method of execution is the familiar "readevaluate-print"
loop found in interpreted languages such as BASIC and LISP.
The user is presented with a prompt which is the simulator's way of asking
for input. After a command is issued, NETS will attempt to evaluate the command,
which may produce more prompts requesting specific information or an
error if the command is not understood. The typical process involved when
using NETS consists of translating the problem into a format which uses input/output
pairs, designing a network configuration for the problem, and
finally training the network with input/output pairs until an acceptable
error is reached.
NETS allows the user to generate C code to implement the network loaded
into the system. This permits the placement of networks as components, or
subroutines, in other systems. In short, once a network performs satisfactorily,
the Generate C Code option provides the means for creating a program
separate from NETS to run the network. Other features: files may be stored
in binary or ASCII format; multiple input propagation is permitted; bias
values may be included; capability to scale data without writing scaling
code; quick interactive testing of network from the main menu; and several
options that allow the user to manipulate learning efficiency.
NETS carries the NASA case number MSC-22108 for the machine independent version, and MSC-21588 for the Macintosh version. It was originally released as part of the NASA COSMIC collection.
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