 Neural
Networks
Neural networks find their archetype in nature and are a simplified
imitation of the human brain. They are designed as software or hardware
systems to simulate the operation of biological nervous systems. Similar
to biological brains, neural networks have the
capacity to learn, memorize and create their own relationships amongst
data with the advantage that no formal description is required. They provide
more
intuitive solutions
with good predictive accuracy to complicated problems. Nowadays neural networks
are routinely used to
- Store Human Experience & Know-how
- Recognize Relations
- Make Decisions & Predictions
- Control Machines
- ...
Neural networks do not rely rule based on programming for their
performance, they use learning algorithms instead to tune outputs to
inputs. Neural networks are useful in situations in which rules are not
explicitly available, and in which mapping inputs to outputs is easier
than analyzing the underlying reasoning process or model structure. The technology is widely used in the fields of
-
 Enterprise Data Analysis
- Medicine & Bioinformatics
- Financial Services
- Defence
- Network Management
- Speech Recognition
- Machine Vision
- Robotics
Neural networks are no longer the grist of science fiction writers nor
are they a flash in the pan that will soon fade from use. The field has matured
in the last two decades and found so many technical, financial, scientific and other
applications that the notion to use neural networks for solving real world
problems no longer needs a "sales pitch" to the management in many
companies.
| 
