A BRIEF HISTORY:
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 important 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 perception, which was
capable of learning certain
classifications by adjusting connection weights.
Although the perception was
successful in classifying certain patterns, it had a
number of limitations. The perception was not able to solve the classic XOR
(exclusive or) problem. Such
limitations led to the decline of the field of neural
networks. However, the perception had laid foundations for later work in neural
computing.
What
is 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.
To be more clear, let us study
the model of a neural network with the help of
figure. The most common neural
network model is the multi-layer
(MLP). It is composed of of
hierarchical layers of neurons arranged so that information flows from the
input layer to the output layer of the network. The goal of this type of
network is to create a model that correctly maps the input to the output using
historical data so that the model can then be used to produce the output when
the desired output is unknown.
Resemblance with our 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.
So, our entire brain is composed
of these interconnected electro-chemical transmitting
neurons. From a very large number
of extremely simple processing units (each performing a weighted sum of its
inputs, and then firing a binary signal if the total input
exceeds a certain level) the
brain manages to perform extremely complex tasks. This is
the model on which artificial
neural networks are based.
Neural network is a sequence of
neuron layers. A neuron is a building block of a neural
net. It is very loosely based on
the brain's nerve cell. Neurons will receive inputs via
weighted links from other
neurons. This inputs will be processed according to the
neurons activation function.
Signals are then passed on to other neurons.
In a more practical way, neural
networks are made up of interconnected processing
elements called units which are
equivalent to the brains counterpart ,the neurons.
Neural network can be considered
as an artificial system that could perform "intelligent"
tasks similar to those performed
by the human brain. Neural networks resemble the
human brain in the following
ways:
·
A
neural network acquires knowledge through learning.
·
A
neural network's knowledge is stored within inter-neuron connection strengths
known
as synaptic weights.
·
Neural
networks modify own topology just as neurons in the brain can die and
new
synaptic connections grow.
