18-05-2012, 01:09 PM
artificial neural network
27ARTIFICIAL-NEURAL-NETWORKS.pdf (Size: 110.15 KB / Downloads: 37)
ABSTRACT
Just as life attempts to understand itself better by modeling it, and in the process
create something new, so Neural computing is an attempt at modeling the
workings of a brain and this presentation is an attempt to understand the basic
concept of artificial neural networks.
.
In this paper, a small but effective overall content of artificial
neural networks is presented . .First,the history of Neural Networks which deals
with the comparative study of how vast the Neural Networks have developed
over the years is presented. Next, having known what exactly is a neural network
with the help of a MLP model, we proceed to next session: resemblance with
brain where in the comparison between brain and neural networks as well as
neurons and perceptrons are made with the help of figures.
Brief Historical background of neural networks
Neural network simulations appear to be a recent development. However, this
field was established before the advent of computers, and has survived at least one
major setback and several eras.Many importand advances have been boosted by
the use of inexpensive computer emulations.
The earliest work in neural computing goes back to the 1940's when McCulloch
and Pitts introduced the first neural network computing model. In the 1950's,
Rosenblatt's work resulted in a two-layer network, the perceptron, which was
capable of learning certain classifications by adjusting connection weights.
What exactly is a neural network?
A neural network is a powerful data modeling tool that is able to capture and
represent complex input/output relationships .In the broader sense , a neural
network is a collection of mathematical models that emulate some of the observed
properties of biological nervous systems and draw on the analogies of adaptive
biological learning. It is composed of a large number of highly interconnected
processing elements that are analogous to neurons and are tied together with
weighted connections that are analogous to synapses.
Resemblence with brain
The brain is principally composed of about 10 billion neurons, each connected to about
10,000 other neurons. Each neuron receives electrochemical inputs from other neurons
at the dendrites. If the sum of these electrical inputs is sufficiently powerful to activate
the neuron, it transmits an electrochemical signal along the axon, and passes this signal to
the other neurons whose dendrites are attached at any of the axon terminals. These
attached neurons may then fire.
27ARTIFICIAL-NEURAL-NETWORKS.pdf (Size: 110.15 KB / Downloads: 37)
ABSTRACT
Just as life attempts to understand itself better by modeling it, and in the process
create something new, so Neural computing is an attempt at modeling the
workings of a brain and this presentation is an attempt to understand the basic
concept of artificial neural networks.
.
In this paper, a small but effective overall content of artificial
neural networks is presented . .First,the history of Neural Networks which deals
with the comparative study of how vast the Neural Networks have developed
over the years is presented. Next, having known what exactly is a neural network
with the help of a MLP model, we proceed to next session: resemblance with
brain where in the comparison between brain and neural networks as well as
neurons and perceptrons are made with the help of figures.
Brief Historical background of neural networks
Neural network simulations appear to be a recent development. However, this
field was established before the advent of computers, and has survived at least one
major setback and several eras.Many importand advances have been boosted by
the use of inexpensive computer emulations.
The earliest work in neural computing goes back to the 1940's when McCulloch
and Pitts introduced the first neural network computing model. In the 1950's,
Rosenblatt's work resulted in a two-layer network, the perceptron, which was
capable of learning certain classifications by adjusting connection weights.
What exactly is a neural network?
A neural network is a powerful data modeling tool that is able to capture and
represent complex input/output relationships .In the broader sense , a neural
network is a collection of mathematical models that emulate some of the observed
properties of biological nervous systems and draw on the analogies of adaptive
biological learning. It is composed of a large number of highly interconnected
processing elements that are analogous to neurons and are tied together with
weighted connections that are analogous to synapses.
Resemblence with brain
The brain is principally composed of about 10 billion neurons, each connected to about
10,000 other neurons. Each neuron receives electrochemical inputs from other neurons
at the dendrites. If the sum of these electrical inputs is sufficiently powerful to activate
the neuron, it transmits an electrochemical signal along the axon, and passes this signal to
the other neurons whose dendrites are attached at any of the axon terminals. These
attached neurons may then fire.