|
|
|
Foundation ::
Artificial Intelligence and Expert Systems ::
NNETS
|
NNETS
Neural Network Environment on a Transputer System
|
 |
Moderators:
Adopt This Application!
|
SOURCE CODE AVAILABLE
|
|
The primary purpose of NNETS (Neural Network Environment on a Transputer
System) is to provide users a high degree of flexibility in creating
and manipulating a wide variety of neural network topologies at processing
speeds not found in conventional computing environments. To accomplish
this purpose, NNETS supports back propagation and back propagation
related algorithms. The back propagation algorithm used is an implementation
of Rumelhart's Generalized Delta Rule. NNETS was developed on the INMOS
Transputer.
NNETS predefines a Back Propagation Network, a Jordan Network, and a
Reinforcement Network to assist users in learning and defining their own
networks. The program also allows users to configure other neural network
paradigms from the NNETS basic architecture.
The Jordan network is basically a feed forward network that has the
outputs connected to a pseudo input layer. The state of the network is dependent
on the inputs from the environment plus the state of the network.
The Reinforcement network learns via a scalar feedback signal called
reinforcement. The network propagates forward randomly. The environment
looks at the outputs of the network to produce a reinforcement signal that
is fed back to the network.
NNETS carries the NASA case number MSC-21485. It was originally released as part of the NASA COSMIC collection.
|
|
More software from National Technology Transfer Center
|
|
|
|
|
|
