Term Paper Neural Networks

Term Paper Neural Networks-14
In 1949, Donald Hebb wrote The Organization of Behavior, a work which pointed out the fact that neural pathways are strengthened each time they are used, a concept fundamentally essential to the ways in which humans learn.If two nerves fire at the same time, he argued, the connection between them is enhanced.

ADALINE was developed to recognize binary patterns so that if it was reading streaming bits from a phone line, it could predict the next bit.

MADALINE was the first neural network applied to a real world problem, using an adaptive filter that eliminates echoes on phone lines.

Such ideas were appealing but very difficult to implement.

In addition, von Neumann architecture was gaining in popularity.

They both used matrix mathematics to describe their ideas but did not realize that what they were doing was creating an array of analog ADALINE circuits.

The neurons are supposed to activate a set of outputs instead of just one.The first multilayered network was developed in 1975, an unsupervised network.An artificial neural network is an interconnected group of nodes, inspired by a simplification of neurons in a brain.In 1943, neurophysiologist Warren Mc Culloch and mathematician Walter Pitts wrote a paper on how neurons might work.In order to describe how neurons in the brain might work, they modeled a simple neural network using electrical circuits.As a result, research and funding went drastically down.This was coupled with the fact that the early successes of some neural networks led to an exaggeration of the potential of neural networks, especially considering the practical technology at the time.As computers became more advanced in the 1950's, it was finally possible to simulate a hypothetical neural network.The first step towards this was made by Nathanial Rochester from the IBM research laboratories.0 or 1) according to the rule: Weight Change = (Pre-Weight line value) * (Error / (Number of Inputs)).It is based on the idea that while one active perceptron may have a big error, one can adjust the weight values to distribute it across the network, or at least to adjacent perceptrons.


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