Artificial
Neural Network
“A computer
system modeled on human brain or nervous system”- Wikipedia
The
inventor of first neuro-computer Dr. Robert Hecht- Neilson defines a neural
network as –
“… a
computing system made up of a number of simple highly interconnected processing
elements which process information by their dynamic state response to external
inputs”
An
artificial neural network usually involves a large number of processors operating
in parallel and arranged in tiers. The first tier receives the raw input information
analogous to optic nerves in human vision processing. Each successive tier receives
the output of preceding tier, rather than the raw input. The last tier produces
output of the system.
Each
processing node has its own small sphere of knowledge, including what it has
seen and any rules it was originally programmed with, or developed for itself.
Definition:
What
is neural network?
A
neural network is an artificial representation of the human brain that tries to
simulate its learning process. An artificial neural network is often called as
Neural Network or Neural Net
Traditionally,
the word neural network is referred to a network of biological neurons in
nervous system that process and transmit the information.
ANN is an interconnected group of artificial
neurons that uses a mathematical model or computational model for information
processing based on a connectionist approach to computation.
The
artificial neurons may share some properties of biological neurons
ANN
is an adaptive system that changes its structure based on external or internal
information that flows through the network.
Neural
computers mimic certain processing capabilities of human brain
Neural
computing is an information processing paradigm inspired by biological system ,
composed of large number if highly interconnected processing elements (neurons)
working in union to solve specific problem.
An ANN is configured for a specific
application such as pattern recognition, or data classification through a
learning process.
A simple artificial neuron
- Takes input
- Calculate summation of inputs
- Compare it with threshold being set during learning stage
- Outputs the result
ANNs
are non linear data driven self adaptive approach as opposed to traditional
model based methods. They are powerful tools for modelling especially when
relation between the underlying data is unknown.
ANNs
can identify and learn correlated patterns between input data sets and corresponding
target values.
After
training ANNs can be used to predict the outcome of new independent input data.
ANNs
can imitate the learning process of human brain and can process problems involving
non linear and complex data even if data is imprecise and noisy
A
very important feature of ANN is their adaptive nature where learning by
example replaces programming in solving problems
This
feature makes computational models very appealing in application domains where
one has little or incomplete understanding problem to be solved but where
training data is readily available. These networks are “neural” in the sense
that they are inspired by the neuroscience but not necessarily faithful models
of biological neural phenomena.
In fact
majority of network are more closely related to traditional mathematical and/
or statistical models such as Non- Parametric Pattern classifiers, clustering
algorithms, non linear filters, statistical regression models than they are to
neurobiology models.
NNs
have been used in wide range of applications. They have been used for
classification problems such as identifying underwater current sonar, recognizing
speech and predicting the secondary structure of globular proteins. NNs have
also been used in predicting stock market performance.
1.
The NNs exhibit mapping capabilities, that is, they can map input
patterns to their associated output patterns.
Characteristics of Neural
Network:
2.
NNs learn by example. Thus NN architectures can be trained with known
examples of a problem before they are tested for their inference capabilities
on unknown instances of problem. They can, therefore identify new objects
previously untrained.
3.
The NNs posses the capability to
generalize. Thus hey can predict new outcomes from past trends.
4.
The NNs are robust system and are fault tolerant. They
can therefore
recall full patterns from incomplete , partial or noisy patterns
5.
NNs can process information in parallel, at high speed, in a
distributed manner.
