Page 218 - Concise Encyclopedia of Robotics

Neutral Network
degradation, so that if part of the system is destroyed, the rest can keep
things going, albeit at a slower speed and/or with less accuracy.
Limitations
Neural networks are imprecise. If you ask one to balance your checkbook,
it will come close, but it will not give an exact answer. Neural networks
are not designed to do calculations of the sort a digital computer can carry
out. A $5.00 calculator will outperform even the most complex neural
network at basic arithmetic. In that sense, neural network technology
resembles analog computer technology.
Another weakness of neural networks arises from the fact that they
inevitably make mistakes as they zero in on their conclusions. Digital
machines break problems down into miniscule pieces,meticulously grind-
ing out a solution to a level of exactness limited only by the number of
transistors that can be fabricated onto a chip of silicon. Neural networks
tackle problems as a whole,modifying their outlook until the results satisfy
certain conditions.
One might make the generalization that digital computers are analytical,
while neural networks are intuitive.
Fuzzy logic
Digital machines recognize, at the fundamental level, two conditions or
states: logic 1 and logic 0.These two logic states can be specified in terms of
high/low, true/false, plus/minus, yes/no, red/green, up/down, front/back,
or any other clear-cut dichotomy. The human brain is made up of neurons
and synapses in a huge network, all of which can communicate with a vast
number of others. In a neural network, “neurons” and “synapses” are the
processing elements and the data paths between them. The earliest neural-
network enthusiasts postulated that the human brain works like a huge
digital machine, its neurons and synapses either “firing” or “staying quiet.”
Later, it was learned that things are more complicated than that.
In some neural networks, the neurons can send only two different
types of signals, and represent the brain as theorized in the 1950s. How-
ever, results can be modified by giving some neurons and/or synapses
more importance than others. This creates fuzzy logic, in which truth
and falsity exist with varying validity.

Neural networks and artificial intelligence
Some researchers suggest that the ultimate goal of AI can be reached by a
“marriage”of digital and neural-network technologies.Others think neural
networks represent a dead end, and that digital technology has clearly

