Artificial Neural Networks
Artificial Neural networks, sometimes refered to a just Neural Networks is a decision making system that was inspired by the nervous systems of animals. It works by having several processing systems working together to solve a problem rather than just one. These networks, just like humans, learn by example.
The first Artificial Neuron was created by Warren McCulloch, a neurophysiologist, and Walter Pits, a logician, in 1943. However it was limited by the technology of the time so it was unable to do much. In 1949 Donald Hebb brought to light the importance that nerual pathways are improved with each use. This becomes essential because of its similarity to the way in which humans learn. "MADALINE" was developed in 1959 by Windrom and Hoff of Stanford University. It was the the first nueral network applied to a real world problem, eliminating the echoes on phone lines. This system is still in commercial use.
The early successes of the neural networds coaused an exaggeration of the potential, such as a computer that could program itself. This led to a fear of the "thinking machine" which is still felt today, though even with all the advances in technology we are far from achieving the "thinking machines" we so fear.
In 1982 there were 3 major breakthroughs in ANNs. The first was John Hopfield whose proposition reneverd interest in the field. Early systems only used a one way connection between neurons but Hopfield proposed using a bidirectional connection between the lines which he believed would be able to create more useful machines. Also that year Reilly and Cooper craeted a multiple layer network. Each of the layers used a different strategy of problem solving. This they called a "Hybrid network."