An Artificial Neural Network (ANN) is an
information processing paradigm that is inspired by the way biological nervous
systems works, such as the brain, process information. The key element of this
paradigm is the structure of the information processing system. It is composed
of a large number of highly interconnected processing elements (neurons)
working together to solve specific problems. ANNs, it’s just like people, learn
by example. An ANN is designed for a specific application, such as a 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.
Why use neural networks?
Neural networks, with their ability to derive
meaning from complicated or imprecise data, can be used to get patterns and
detect trends that are too complex to be noticed by either humans or other
computer techniques. A trained neural network can be thought of as an
"expert" in the category of information it has been given to analyze.
This expert can then be used to provide projections given new situations of
interest and answer "what if" questions.
Other advantages include:
Other advantages include:
- Adaptive learning: An ability to learn how to do tasks based on the data given for training or initial experience.
- Self-Organization: An ANN can create its own organization or representation of the information it receives during learning time.
- Real Time Operation: ANN computations may be carried out in parallel, and special hardware devices are being designed and manufactured which take advantage of this capability.
2. Human and Artificial Neurons - investigating the similarities
2.1 How the Human Brain Learns?
Much is still unknown about how the brain
trains itself to process information, so theories abound. In the human brain, a
typical neuron collects signals from others through a host of fine structures
called dendrites. The neuron sends out signals of electrical
activity through a long, thin stand known as an axon, which splits into thousands of branches. At
the end of each branch, a structure called a synapse
converts the activity from the axon into electrical effects that inhibit or
excite activity in the connected neurons. When a neuron receives excitatory
input that is sufficiently large compared with its inhibitory input, it sends a
spike of electrical activity down its axon. Learning occurs by changing the
effectiveness of the synapses so that the influence of one neuron on another
changes.
Components of neuron The
synapse
2.2 From Human Neurons to Artificial Neurons
We conduct these neural networks by first
trying to find the essential features of neurons and their interconnections. We
then typically program a computer to simulate these features. However because
our knowledge of neurons is incomplete and our computing power is limited, our
models are necessarily gross idealizations of real networks of neurons.
3.1 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.
A simple neuron
Architecture of neural networks
Feed-forward networks
Feed-forward ANNs (figure 4-1) allow signals to
travel one way only; from input to output. There is no feedback (loops) i.e.
the output of any layer does not affect that same layer. Feed-forward ANNs tend
to be straight forward networks that associate inputs with outputs. They are
widely used in pattern recognition. This type of organization is also referred
to as bottom-up or top-down.
Feedback networks
Feedback networks (figure 4-2) can have signals
travelling in both directions by introducing loops in the network. Feedback
networks are very powerful and can get extremely complicated. Feedback networks
are dynamic; their 'state' is changing continuously until they reach a balanced
point. They remain at the balanced point until the input changes and a new
equilibrium needs to be found. Feedback architectures are also referred to as interactive
or recurrent, although the latter term is often used to denote feedback
connections in single-layer organizations.
Figure 4.1 An example of a simple feed-forward
network
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Figure 4.2 An example of a complicated
network
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Network layers
The commonest type of artificial neural network
consists of three groups, or layers, of units: a layer of "input"
units is connected to a layer of "hidden" units, which is
connected to a layer of "output" units.
(Figure 4.1)
The activity of the input units represents the
raw information that is fed into the network.The activity of each hidden unit is determined
by the activities of the input units and the weights on the connections between
the input and the hidden units.The behavior of the output units depends on the
activity of the hidden units and the weights between the hidden and output
units.
This simple type of network is interesting
because the hidden units are free to construct their own representations of the
input. The weights between the input and hidden units determine when each
hidden unit is active, and so by modifying these weights, a hidden unit can
choose what it represents.
We also distinguish single-layer and
multi-layer architectures. The single-layer organization, in which all units are
connected to one another, to form the most general case and is of more
potential computational power than hierarchically structured multi-layer
organizations. In multi-layer networks, units are often numbered by layer,
instead of following a global numbering.
The memorization of patterns and the subsequent
response of the network can be categorized into two general paradigms:
Associative mapping in which the
network learns to produce a particular pattern on the set of input units whenever
another particular pattern is applied on the set of input units. The
associative mapping can generally be broken down into two mechanisms: 
Auto-association: an input
pattern is associated with itself and the states of input and output units coincide.
This is used to provide pattern completion, ie to produce a pattern whenever a
portion of it or a distorted pattern is presented. In the second case, the
network actually stores pairs of patterns building an association between two
sets of patterns. Hetero-association: is related to
two recall mechanisms: 
Nearest-neighbor recall, where
the output pattern produced corresponds to the input pattern stored, which is
closest to the pattern presented, and Interpolative recall, where the output pattern is a
similarity dependent interpolation of
the patterns stored corresponding to the pattern presented. Yet another
paradigm, which is a variant associative mapping is classification, ie when
there is a fixed set of categories into which the input patterns are to be
classified. Regularity detection: in
which units learn to respond to particular properties of the input patterns.
Whereas in associative mapping the network stores the relationships among
patterns, in regularity detection the response of each unit has a particular
'meaning'. This type of learning mechanism is essential for feature discovery
and knowledge representation. Fixed networks in which
the weights cannot be changed, ie dW/dt=0. In such networks, the weights are
fixed a priori according to the problem to solve.Adaptive networks which are able
to change their weights, ie dW/dt not= 0.
All learning methods used for adaptive neural
networks can be classified into two major categories:
Supervised
learning which incorporates an external teacher, so
that each output unit is told what its desired response to input signals ought
to be. During the learning process global information may be required.
Paradigms of supervised learning include error-correction learning,
reinforcement learning and stochastic learning.
An important issue conserving supervised
learning is the problem of error convergence, ie the minimization of error
between the desired and computed unit values. The aim is to determine a set of
weights which minimizes the error. One well-known method, which is common to
many learning paradigms, is the least mean square (LMS) convergence.
Unsupervised
learning uses no external teacher and is based upon
only local information. It is also referred to as self-organization, in the
sense that it self-organizes data presented to the network and detects their
emergent collective properties. Paradigms of unsupervised learning are Hebbian
learning and competitive learning.Transfer Function
The behavior of an ANN (Artificial Neural
Network) depends on both the weights and the input-output function (transfer
function) that is specified for the units. This function typically falls into
one of three categories:
Linear (or ramp)ThresholdSigmoid
For linear units, the output
activity is proportional to the total weighted output.
For threshold units, the output are
set at one of two levels, depending on whether the total input is greater than
or less than some threshold value.
For sigmoid units, the output
varies continuously but not linearly as the input changes. Sigmoid units bear a
greater resemblance to real neurones than do linear or threshold units, but all
three must be considered rough approximations.
To make a neural network that performs some
specific task, we must choose how the units are connected to one another (see
figure 4.1), and we must set the weights on the connections appropriately. The
connections determine whether it is possible for one unit to influence another.
The weights specify the strength of the influence.
We can teach a three-layer network to perform a
particular task by using the following procedure:
- We present the network with training examples, which consist of a pattern of activities for the input units together with the desired pattern of activities for the output units.
- We determine how closely the actual output of the network matches the desired output.
- We change the weight of each connection so that the network produces a better approximation of the desired output.
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:
Weather forecasting Industrial process control Data validation Risk management Target marketing
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 multi-meaning 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) are 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. What is needed is a set of
examples that are representative of all the variations of the disease. The
quantity of examples is not as important as the 'quantity'. The examples need
to be selected very carefully if the system is to perform reliably and
efficiently.
Modeling and Diagnosing the Cardiovascular System
Neural Networks are used experimentally to
model the human cardiovascular system. Diagnosis can be achieved by building a
model of the cardiovascular system of an individual and comparing it with the
real time physiological measurements taken from the patient. If this routine is
carried out regularly, potential harmful medical conditions can be detected at
an early stage and thus make the process of combating the disease much easier.
Marketing
There is a marketing application which has been
integrated with a neural network system. The Airline Marketing Tactician (a
trademark abbreviated as AMT) is a computer system made of various intelligent
technologies including expert systems. A feed forward neural network is
integrated with the AMT and was trained using back-propagation to assist the
marketing control of airline seat allocations. The adaptive neural approach was
amenable to rule expression. Additionally, the application's environment
changed rapidly and constantly, which required a continuously adaptive
solution. The system is used to monitor and recommend booking advice for each
departure. Such information has a direct impact on the profitability of an
airline and can provide a technological advantage for users of the system.
[Hutchison & Stephens, 1987]
Conclusion
The computing world has a lot to gain from
neural networks. Their ability to learn by example makes them very flexible and
powerful. Furthermore there is no need to devise an algorithm in order to
perform a specific task; i.e. there is no need to understand the internal
mechanisms of that task. They are also very well suited for real time systems
because of their fast response and computational times which are due to their
parallel architecture.
Perhaps the most exciting aspect of neural
networks is the possibility that some day 'conscious' networks might be
produced. There is a number of scientists arguing that consciousness is a
'mechanical' property and that 'conscious' neural networks are a realistic
possibility.
Finally,
I would like to state that even though neural networks have a huge potential we
will only get the best of them when they are integrated with computing, AI,
fuzzy logic and related subjects.
References:
A Novel Approach to Modeling and Diagnosing the Cardiovascular System
http://www.emsl.pnl.gov:2080/docs/cie/neural/papers2/keller.wcnn95.abs.html
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Artificial Neural Networks in Medicine
http://www.emsl.pnl.gov:2080/docs/cie/techbrief/NN.techbrief.ht
http://www.emsl.pnl.gov:2080/docs/cie/techbrief/NN.techbrief.ht
Assimov, I (1984, 1950), Robot, Ballatine, New York.
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Electronic Noses for Telemedicine
http://www.emsl.pnl.gov:2080/docs/cie/neural/papers2/keller.ccc95.abs.html
http://www.emsl.pnl.gov:2080/docs/cie/neural/papers2/keller.ccc95.abs.html
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http://www.emsl.pnl.gov:2080/docs/cie/neural/neural.homepage.html
http://www.emsl.pnl.gov:2080/docs/cie/neural/neural.homepage.html
Neural Networks by Eric Davalo and Patrick Naim
