10-10-2012, 04:21 PM
Artificial Neural Networks
[attachment=35962]
ABSTRACT
Most people when asked if they think computers could ever become sentient
quickly respond no and refer to the fact that computers are unable to learn.
However, Neural Networks seems to do just that.
Neural Networks encompass a diverse set of computational models, which
share a set of simple underlying characteristics. Inspired by the
computational style of biological systems, a Neural Network can be viewed
as an assembly of simple, interconnected processing units (neurons) acting
in parallel, which communicate to each other using unidirectional
connections.
Neural networks are distinguished from other computer and mathematical
techniques by their design motivation. They are processing devices, that can
be algorithms or actual hardware that are modeled after the functioning of
human brain. Most Neural Networks have some sort of “training” rule
whereby the weights of connections are adjusted on the basis of presented
patterns. In other words, Neural Networks “learn” from examples, just like
children learn to recognize dogs from examples of dogs and exhibit some
structural capability for generalization.
INTRODUCTION
Artificial Neural network is a system loosely modeled on the human brain. The field
goes by many names, such as connectionism; parallel distributed processing, euro
computing, natural intelligent systems, machine learning algorithms and artificial neural
networks. It is an attempt to simulate within specialized hardware or sophisticated
software, the multiple layers of simple processing elements called neurons. Each neuron
is linked to certain of its neighbours with varying coefficients of connectivity that
represent the strengths of these connections. Learning is accomplished by adjusting these
strengths to cause the overall network to output appropriate results.
THE BIOLOGICAL NEURON
The most basic element of the human brain is a specific type of cell, which provides us
with the abilities to remember, think and apply previous experiences to our every action.
This power of the brain comes from the numbers of these basic components and the
multiple connections between them.
All natural neurons have four basic components, which are dendrites, soma, axon and
synapses. Basically, a biological neuron receives inputs from other sources, combines
them in some way, performs a generally non-linear operation on the result, and then
output the final result.
LAYERS
Biologically, neural networks are constructed in a three dimensional way from
microscopic components. These neurons seem capable of nearly un-restricted
interconnections. This is not true in any manmade network. Artificial neural networks are
the simple clustering of the primitive artificial neurons. This clustering occurs by creating
layers, which are then connected to one another. How these layers connect may also vary.
Basically, all artificial neural networks have a similar structure of topology. Some of the
neurons interface the real world to receive its inputs and other neurons provide the real
world with the network’s outputs. Al the rest of the neurons are hidden from view.
As the figure below shows, the neurons are grouped into layers. The input layer
consists of neurons that receive input from the external environment. The output layer
consists of neurons that communicate the output of the system to the user or external
environment. There are usually a number of hidden layers between these two layers.
COMMUNICATION AND TYPES OF CONNECTIONS
Neurons are connected via a network of paths carrying the output of one neuron as input
to another neuron. These paths is normally unidirectional, there might however be a twoway
connection between two neurons, because there may be another path in reverse
direction. A neuron receives input many neurons, but produces a single output, which is
communicated to other neurons.
The neuron in a layer may communicate with each other, or they may not have any
connections. The neurons of one layer are always connected to the neurons of at least
another layer.
OFF-LINE OR ON-LINE
One can categorize the learning methods into yet another group, off-line or on-line. When
the system uses input data to change its weights to learn the domain knowledge, the
system could be in training mode or learning mode. When the system is being used as a
decision aid to make recommendations, it is in the operation mode. This is also
sometimes called recall.
[attachment=35962]
ABSTRACT
Most people when asked if they think computers could ever become sentient
quickly respond no and refer to the fact that computers are unable to learn.
However, Neural Networks seems to do just that.
Neural Networks encompass a diverse set of computational models, which
share a set of simple underlying characteristics. Inspired by the
computational style of biological systems, a Neural Network can be viewed
as an assembly of simple, interconnected processing units (neurons) acting
in parallel, which communicate to each other using unidirectional
connections.
Neural networks are distinguished from other computer and mathematical
techniques by their design motivation. They are processing devices, that can
be algorithms or actual hardware that are modeled after the functioning of
human brain. Most Neural Networks have some sort of “training” rule
whereby the weights of connections are adjusted on the basis of presented
patterns. In other words, Neural Networks “learn” from examples, just like
children learn to recognize dogs from examples of dogs and exhibit some
structural capability for generalization.
INTRODUCTION
Artificial Neural network is a system loosely modeled on the human brain. The field
goes by many names, such as connectionism; parallel distributed processing, euro
computing, natural intelligent systems, machine learning algorithms and artificial neural
networks. It is an attempt to simulate within specialized hardware or sophisticated
software, the multiple layers of simple processing elements called neurons. Each neuron
is linked to certain of its neighbours with varying coefficients of connectivity that
represent the strengths of these connections. Learning is accomplished by adjusting these
strengths to cause the overall network to output appropriate results.
THE BIOLOGICAL NEURON
The most basic element of the human brain is a specific type of cell, which provides us
with the abilities to remember, think and apply previous experiences to our every action.
This power of the brain comes from the numbers of these basic components and the
multiple connections between them.
All natural neurons have four basic components, which are dendrites, soma, axon and
synapses. Basically, a biological neuron receives inputs from other sources, combines
them in some way, performs a generally non-linear operation on the result, and then
output the final result.
LAYERS
Biologically, neural networks are constructed in a three dimensional way from
microscopic components. These neurons seem capable of nearly un-restricted
interconnections. This is not true in any manmade network. Artificial neural networks are
the simple clustering of the primitive artificial neurons. This clustering occurs by creating
layers, which are then connected to one another. How these layers connect may also vary.
Basically, all artificial neural networks have a similar structure of topology. Some of the
neurons interface the real world to receive its inputs and other neurons provide the real
world with the network’s outputs. Al the rest of the neurons are hidden from view.
As the figure below shows, the neurons are grouped into layers. The input layer
consists of neurons that receive input from the external environment. The output layer
consists of neurons that communicate the output of the system to the user or external
environment. There are usually a number of hidden layers between these two layers.
COMMUNICATION AND TYPES OF CONNECTIONS
Neurons are connected via a network of paths carrying the output of one neuron as input
to another neuron. These paths is normally unidirectional, there might however be a twoway
connection between two neurons, because there may be another path in reverse
direction. A neuron receives input many neurons, but produces a single output, which is
communicated to other neurons.
The neuron in a layer may communicate with each other, or they may not have any
connections. The neurons of one layer are always connected to the neurons of at least
another layer.
OFF-LINE OR ON-LINE
One can categorize the learning methods into yet another group, off-line or on-line. When
the system uses input data to change its weights to learn the domain knowledge, the
system could be in training mode or learning mode. When the system is being used as a
decision aid to make recommendations, it is in the operation mode. This is also
sometimes called recall.