20-12-2012, 04:36 PM
An Artificial Neural Network (ANN)
[attachment=44608]
Introduction to neural networks
What is a Neural Network?
An Artificial Neural Network (ANN) is an information-processing paradigm that is inspired by the way
biological nervous systems, such as the brain, process information. The key element of this paradigm is the
novel structure of the information processing system. It is composed of a large number of highly
interconnected processing elements (neurons) working in unison to solve specific problems. ANNs, like
people, learn by example. An ANN is configured for a specific application, such as pattern recognition or
data classification, through a learning process. Learning in biological systems involves adjustments to the
synaptic connections that exist between the neurons. This is true of ANNs as well.
Historical background
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. Following an
initial period of enthusiasm, the field survived a period of frustration and disrepute. During this period
when funding and professional support was minimal, relatively few researchers made important advances.
These pioneers were able to develop convincing technology, which surpassed the limitations identified by
Minsky and Papert. Minsky and Papert, published a book (in 1969) in which they summed up a general
feeling of frustration among researchers, and was thus accepted by most without further analysis.
Need of neural networks?
Either humans or other computer techniques can use neural networks, with their remarkable ability to
derive meaning from complicated or imprecise data, to extract patterns and detect trends that are too
complex to be noticed. A trained neural network can be thought of as an "expert" in the category of
information it has been given to analyze.
Neural networks versus conventional computers
Neural networks take a different approach to problem solving than that of conventional computers.
Conventional computers use an algorithmic approach i.e. the computer follows a set of instructions in order
to solve a problem. Unless the specific steps that the computer needs to follow are known the computer
cannot solve the problem. That restricts the problem solving capability of conventional computers to
problems that we already understand and know how to solve. But computers would be so much more useful
if they could do things that we don't exactly know how to do.
Neural networks process information in a similar way the human brain does. The network is composed of a
large number of highly interconnected processing elements (neurons) working in parallel to solve a specific
problem. Neural networks learn by example. They cannot be programmed to perform a specific task.
A simple neuron
An artificial neuron is a device with many inputs and one output. The neuron has two modes of operation;
the training mode and the using mode. In the training mode, the neuron can be trained to fire (or not), for
particular input patterns. In the using mode, when a taught input pattern is detected at the input, its
associated output becomes the current output. If the input pattern does not belong in the taught list of input
patterns, the firing rule is used to determine whether to fire or not.
The Analogy to the Brain
The most basic components of neural networks are modeled after the structure of the brain. Some
neural network structures are not closely to the brain and some does not have a biological
counterpart in the brain. However, neural networks have a strong similarity to the biological brain
and therefore a great deal of the terminology is borrowed from neuroscience.
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. These cells are
known as neurons, each of these neurons can connect with up to 200000 other neurons. The 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 nonlinear operation on the result, and then output the final result.
Applications of neural networks
Neural Networks in Practice
Given this description of neural networks and how they work, what real world applications are they suited
for? Neural networks have broad applicability to real world business problems. In fact, they have already
been successfully applied in many industries. Since neural networks are best at identifying patterns or
trends in data, they are well suited for prediction or forecasting needs including:
Sales forecasting
Industrial process control
Customer research
Data validation
But to give you some more specific examples; ANN are also used in the following specific paradigms:
recognition of speakers in communications; diagnosis of hepatitis; recovery of telecommunications from
faulty software; interpretation of multimeaning Chinese words; undersea mine detection; texture analysis;
three-dimensional object recognition; hand-written word recognition; and facial recognition.
Neural networks in medicine
Artificial Neural Networks (ANN) is currently a 'hot' research area in medicine and it is believed that they
will receive extensive application to biomedical systems in the next few years. At the moment, the research
is mostly on modeling parts of the human body and recognizing diseases from various scans (e.g.
cardiograms, CAT scans, ultrasonic scans, etc.). Neural networks are ideal in recognizing diseases using
scans since there is no need to provide a specific algorithm on how to identify the disease. Neural networks
learn by example so the details of how to recognize the disease are not needed.
[attachment=44608]
Introduction to neural networks
What is a Neural Network?
An Artificial Neural Network (ANN) is an information-processing paradigm that is inspired by the way
biological nervous systems, such as the brain, process information. The key element of this paradigm is the
novel structure of the information processing system. It is composed of a large number of highly
interconnected processing elements (neurons) working in unison to solve specific problems. ANNs, like
people, learn by example. An ANN is configured for a specific application, such as pattern recognition or
data classification, through a learning process. Learning in biological systems involves adjustments to the
synaptic connections that exist between the neurons. This is true of ANNs as well.
Historical background
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. Following an
initial period of enthusiasm, the field survived a period of frustration and disrepute. During this period
when funding and professional support was minimal, relatively few researchers made important advances.
These pioneers were able to develop convincing technology, which surpassed the limitations identified by
Minsky and Papert. Minsky and Papert, published a book (in 1969) in which they summed up a general
feeling of frustration among researchers, and was thus accepted by most without further analysis.
Need of neural networks?
Either humans or other computer techniques can use neural networks, with their remarkable ability to
derive meaning from complicated or imprecise data, to extract patterns and detect trends that are too
complex to be noticed. A trained neural network can be thought of as an "expert" in the category of
information it has been given to analyze.
Neural networks versus conventional computers
Neural networks take a different approach to problem solving than that of conventional computers.
Conventional computers use an algorithmic approach i.e. the computer follows a set of instructions in order
to solve a problem. Unless the specific steps that the computer needs to follow are known the computer
cannot solve the problem. That restricts the problem solving capability of conventional computers to
problems that we already understand and know how to solve. But computers would be so much more useful
if they could do things that we don't exactly know how to do.
Neural networks process information in a similar way the human brain does. The network is composed of a
large number of highly interconnected processing elements (neurons) working in parallel to solve a specific
problem. Neural networks learn by example. They cannot be programmed to perform a specific task.
A simple neuron
An artificial neuron is a device with many inputs and one output. The neuron has two modes of operation;
the training mode and the using mode. In the training mode, the neuron can be trained to fire (or not), for
particular input patterns. In the using mode, when a taught input pattern is detected at the input, its
associated output becomes the current output. If the input pattern does not belong in the taught list of input
patterns, the firing rule is used to determine whether to fire or not.
The Analogy to the Brain
The most basic components of neural networks are modeled after the structure of the brain. Some
neural network structures are not closely to the brain and some does not have a biological
counterpart in the brain. However, neural networks have a strong similarity to the biological brain
and therefore a great deal of the terminology is borrowed from neuroscience.
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. These cells are
known as neurons, each of these neurons can connect with up to 200000 other neurons. The 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 nonlinear operation on the result, and then output the final result.
Applications of neural networks
Neural Networks in Practice
Given this description of neural networks and how they work, what real world applications are they suited
for? Neural networks have broad applicability to real world business problems. In fact, they have already
been successfully applied in many industries. Since neural networks are best at identifying patterns or
trends in data, they are well suited for prediction or forecasting needs including:
Sales forecasting
Industrial process control
Customer research
Data validation
But to give you some more specific examples; ANN are also used in the following specific paradigms:
recognition of speakers in communications; diagnosis of hepatitis; recovery of telecommunications from
faulty software; interpretation of multimeaning Chinese words; undersea mine detection; texture analysis;
three-dimensional object recognition; hand-written word recognition; and facial recognition.
Neural networks in medicine
Artificial Neural Networks (ANN) is currently a 'hot' research area in medicine and it is believed that they
will receive extensive application to biomedical systems in the next few years. At the moment, the research
is mostly on modeling parts of the human body and recognizing diseases from various scans (e.g.
cardiograms, CAT scans, ultrasonic scans, etc.). Neural networks are ideal in recognizing diseases using
scans since there is no need to provide a specific algorithm on how to identify the disease. Neural networks
learn by example so the details of how to recognize the disease are not needed.