artificial neural network
(foldoc) | artificial neural network
neural nets
neural network
neuron
NN
(ANN, commonly just "neural network"
or "neural net") A network of many very simple processors
("units" or "neurons"), each possibly having a (small amount
of) local memory. The units are connected by unidirectional
communication channels ("connections"), which carry numeric
(as opposed to symbolic) data. The units operate only on
their local data and on the inputs they receive via the
connections.
A neural network is a processing device, either an
algorithm, or actual hardware, whose design was inspired by
the design and functioning of animal brains and components
thereof.
Most neural networks have some sort of "training" rule whereby
the weights of connections are adjusted on the basis of
presented patterns. In other words, neural networks "learn"
from examples, just like children learn to recognise dogs from
examples of dogs, and exhibit some structural capability for
generalisation.
Neurons are often elementary non-linear signal processors (in
the limit they are simple threshold discriminators). Another
feature of NNs which distinguishes them from other computing
devices is a high degree of interconnection which allows a
high degree of parallelism. Further, there is no idle memory
containing data and programs, but rather each neuron is
pre-programmed and continuously active.
The term "neural net" should logically, but in common usage
never does, also include biological neural networks, whose
elementary structures are far more complicated than the
mathematical models used for ANNs.
See Aspirin, Hopfield network, McCulloch-Pitts neuron.
Usenet newsgroup: news:comp.ai.neural-nets.
(1997-10-13)
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