Neural network & its applications

Neural Networks and its
Applications

Presented By:
Ahmed Hashmi
Chinmoy Das

What is neural network

An Artificial Neural Network (ANN) is an information
processing paradigm that is inspired by biological
nervous systems.
It is composed of a large number of highly
interconnected processing elements called neurons.
An ANN is configured for a specific application, such
as pattern recognition or data classification

Why use neural networks

ability to derive meaning from complicated or
imprecise data
extract patterns and detect trends that are too
complex to be noticed by either humans or other
computer techniques
Adaptive learning
Real Time Operation

Neural Networks v/s Conventional
Computers

Conventional computers use an algorithmic
approach, but neural networks works similar to
human brain and learns by example.

Inspiration from Neurobiology
A neuron: many-inputs / one-
output unit
output can be excited or not
excited
incoming signals from other
neurons determine if the
neuron shall excite ("fire")
Output subject to attenuation
in the synapses, which are
junction parts of the neuron

A simple neuron

Takes the Inputs .
Calculate the summation
of the Inputs .
Compare it with the
threshold being set
during the learning stage.

Firing Rules

A firing rule determines how one calculates whether a
neuron should fire for any input pattern.
some sets cause it to fire (the 1-taught set of
patterns) and others which prevent it from doing so
(the 0-taught set)

Example…

X
For example, a 3-input 1:
0 0 0 0 1 1 1 1
neuron is taught to X
0 0 1 1 0 0 1 1
output 1 when the input 2:
(X1,X2 and X3) is 111 or 101 X
0 1 0 1 0 1 0 1
and to output 0 when the 3:
input is 000 or 001.
O
0/ 0/ 0/ 0/
U 0 0 1 1
1 1 1 1
T:

Example…

Take the pattern 010. It differs X
from 000 in 1 element, from 001 0 0 0 0 1 1 1 1
1:
in 2 elements, from 101 in 3
elements and from 111 in 2 X
elements. Therefore, the 0 0 1 1 0 0 1 1
2:
'nearest' pattern is 000 which
belongs in the 0-taught set. Thus X
0 1 0 1 0 1 0 1
the firing rule requires that the 3:
neuron should not fire when the
input is 001. On the other hand,
011 is equally distant from two O
taught patterns that have 0/ 0/
U 0 0 0 1 1 1
different outputs and thus the 1 1
T:
output stays undefined (0/1).

Types of neural network

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.

The Learning Process
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:

Hetero-association: is related to two recall mechanisms:

Nearest-neighbour 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.

Supervised Learning
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 concerning supervised learning is the
problem of error convergence, ie the minimisation of error
between the desired and computed unit values. The aim is
to determine a set of weights which minimises the error.
One well-known method, which is common to many
learning paradigms is the least mean square (LMS)
convergence.

Unsupervised Learning
Unsupervised learning uses no external teacher and is
based upon only local information. It is also referred to as
self-organisation, in the sense that it self-organises data
presented to the network and detects their emergent
collective properties.
From Human Neurons to Artificial Neurons their aspect of
learning concerns the distinction or not of a separate
phase, during which the network is trained, and a
subsequent operation phase. We say that a neural network
learns off-line if the learning phase and the operation
phase are distinct. A neural network learns on-line if it
learns and operates at the same time. Usually, supervised
learning is performed off-line, whereas unsupervised
learning is performed on-line.

Back-propagation Algorithm

it calculates how the error changes as each weight is
increased or decreased slightly.
The algorithm computes each EW by first computing
the EA, the rate at which the error changes as the
activity level of a unit is changed. For output
units, the EA is simply the difference between the
actual and the desired output.

Transfer Function
The behaviour 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)
threshold
sigmoid
For linear units, the output activity is proportional to the total
weighted output.
For threshold units, the output is 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.

Application

INTRODUCTION
 Features of finger prints
 Finger print recognition system
 Why neural networks?
 Goal of the system
 Preprocessing system
 Feature extraction using neural networks
 Classification
 result

Features of finger prints

Finger prints are the unique
pattern of ridges and
valleys in every person’s
fingers.
Their patterns are
permanent and
unchangeable for whole life
of a person.
They are unique and the
probability that two
fingerprints are alike is only 1
in 1.9x10^15.
Their uniqueness is used for
identification of a person.

Finger print recognition system

Image edge Ridge Thinin Feature classifi
acquisiti detecti extractio g extracti cation
on on n on

Image acquisition: the acquired image is digitalized into 512x512
image with each pixel assigned a particular gray scale value
(raster image).
edge detection and thinning: these are preprocessing of the
image , remove noise and enhance the image.

Finger print recognition system

Feature extraction: this
the step where we point
out the features such as
ridge bifurcation and
ridge endings of the
finger print with the help
of neural network.
Classification: here a class
label is assigned to the
image depending on the
extracted features.

Why using neural networks?

Neural networks enable us to find solution
where algorithmic methods are
computationally intensive or do not exist.
There is no need to program neural networks
they learn with examples.
Neural networks offer significant speed
advantage over conventional techniques.

Preprocessing system

The first phase of finger print recognition is to capture a
image .
The image is captured using total internal reflection of light
(TIR).
The image is stored as a two dimensional array of 512x512
size, each element of array representing a pixel and assigned a
gray scale value from 256 gray scale levels.


After image is captured ,noise is
removed using edge
detection, ridge extraction and
thinning.
Edge detection: the edge of the
image is defined where the gray
scale levels changes greatly.
also, orientation of ridges is
determined for each 32x32 block of
pixels using gray scale gradient.
Ridge extraction: ridges are
extracted using the fact that gray
scale value of pixels are maximum
along the direction normal to the
ridge orientation.


Thinning: the extracted ridges
are converted into skeletal
structure in which ridges are
only one pixel wide. thinning
should not-
Remove isolated as well as
surrounded pixel.
Break connectedness.
Make the image shorter.

Feature extraction using neural
networks

Multilayer perceptron network of
three layers is trained to detect
minutiae in the thinned image.
The first layer has nine perceptrons
The hidden layer has five
perceptrons
The output layer has one perceptron.
The network is trained to output ‘1’
when the input window is centered
at the minutiae and it outputs ‘0’
when minutiae are not present.

Feature extraction using neural
networks
Trained neural networks
are used to analyze the
image by scanning the
image with a 3x3 window.
To avoid falsely reported
features which are due to
noise –
The size of scanning
window is increased to
5x5
If the minutiae are too
close to each other than
we ignore all of them.

classification

finger prints can be
classified mainly in four
classes depending upon
their general pattern-
Arch
Tented arch
Right loop
Left loop

Applications of Fingerprint
Recognition

As finger print recognition system can be easily
embedded in any system. It is used in-
Recognition of criminals in law enforcement bodies.
Used to provide security to cars, lockers, banks ,shops.
To differentiate between a person who has voted and those
who have not voted in govt. elections.
To count individuals.

Neural Network Toolbox in MATLAB
Neural Network Toolbox™ provides tools for
designing, implementing, visualizing, and simulating neural
networks. Neural networks are used for applications where
formal analysis would be difficult or impossible, such as
pattern recognition and nonlinear system identification and
control. Neural Network Toolbox supports feedforward
networks, radial basis networks, dynamic networks, self-
organizing maps, and other proven network paradigms.

Key Features
Neural network design, training, and simulation
Pattern recognition, clustering, and data-fitting tools
Supervised networks including feedforward, radial basis, LVQ, time
delay, nonlinear autoregressive (NARX), and layer-recurrent
Unsupervised networks including self-organizing maps and
competitive layers
Preprocessing and postprocessing for improving the efficiency of
network training and assessing network performance
Modular network representation for managing and visualizing
networks of arbitrary size
Routines for improving generalization to prevent overfitting
Simulink blocks for building and evaluating neural networks, and
advanced blocks for control systems applications

Working with Neural Network
Toolbox
Like its counterpart in the biological nervous system, a neural
network can learn and therefore can be trained to find
solutions, recognize patterns, classify data, and forecast future
events. The behavior of a neural network is defined by the way its
individual computing elements are connected and by the strength
of those connections, or weights. The weights are automatically
adjusted by training the network according to a specified learning
rule until it performs the desired task correctly.
Neural Network Toolbox includes command-line functions and
graphical tools for creating, training, and simulating neural
networks. Graphical tools make it easy to develop neural
networks for tasks such as data fitting (including time-series
data), pattern recognition, and clustering. After creating your
networks in these tools, you can automatically
generate MATLAB code to capture your work and automate
tasks.

Neural network & its applications

Network Architectures
Neural Network Toolbox supports a variety of supervised
and unsupervised network architectures. With the toolbox’s
modular approach to building networks, you can develop
custom architectures for your specific problem. You can
view the network architecture including all
inputs, layers, outputs, and interconnections.

Supervised Networks
Supervised neural networks are trained to produce desired outputs in response to
sample inputs, making them particularly well-suited to modeling and controlling
dynamic systems, classifying noisy data, and predicting future events.
Neural Network Toolbox supports four types of supervised networks:
Feedforward networks have one-way connections from input to output layers. They
are most commonly used for prediction, pattern recognition, and nonlinear function
fitting. Supported feedforward networks include feedforward backpropagation,
cascade-forward backpropagation, feedforward input-delay backpropagation, linear,
and perceptron networks.
Radial basis networks provide an alternative, fast method for designing nonlinear
feedforward networks. Supported variations include generalized regression and
probabilistic neural networks.
Dynamic networks use memory and recurrent feedback connections to recognize
spatial and temporal patterns in data. They are commonly used for time-series
prediction, nonlinear dynamic system modeling, and control systems applications.
Prebuilt dynamic networks in the toolbox include focused and distributed time-delay,
nonlinear autoregressive (NARX), layer-recurrent, Elman, and Hopfield networks. The
toolbox also supports dynamic training of custom networks with arbitrary connections.
Learning vector quantization (LVQ) is a powerful method for classifying patterns that
are not linearly separable. LVQ lets you specify class boundaries and the granularity of
classification.

Unsupervised Networks
Unsupervised neural networks are trained by letting the
network continually adjust itself to new inputs. They find
relationships within data and can automatically define
classification schemes.
Neural Network Toolbox supports two types of self-organizing,
unsupervised networks:
Competitive layers recognize and group similar input vectors,
enabling them to automatically sort inputs into categories.
Competitive layers are commonly used for classification and
pattern recognition.
Self-organizing maps learn to classify input vectors according to
similarity. Like competitive layers, they are used for classification
and pattern recognition tasks; however, they differ from
competitive layers because they are able to preserve the
topology of the input vectors, assigning nearby inputs to nearby
categories.

Training and Learning Functions
Training and learning functions are mathematical procedures used to
automatically adjust the network's weights and biases. The training
function dictates a global algorithm that affects all the weights and
biases of a given network. The learning function can be applied to
individual weights and biases within a network.
Neural Network Toolbox supports a variety of training algorithms,
including several gradient descent methods, conjugate gradient
methods, the Levenberg-Marquardt algorithm (LM), and the resilient
backpropagation algorithm (Rprop). The toolbox’s modular framework
lets you quickly develop custom training algorithms that can be
integrated with built-in algorithms. While training your neural network,
you can use error weights to define the relative importance of desired
outputs, which can be prioritized in terms of sample, timestep (for
time-series problems), output element, or any combination of these.
You can access training algorithms from the command line or via a
graphical tool that shows a diagram of the network being trained and
provides network performance plots and status information to help
you monitor the training process.

Improving Generalization
Improving the network’s ability to generalize helps prevent overfitting,
a common problem in neural network design. Overfitting occurs when
a network has memorized the training set but has not learned to
generalize to new inputs. Overfitting produces a relatively small error
on the training set but a much larger error when new data is presented
to the network.

Neural Network Toolbox provides two solutions to improve
generalization:
Regularization modifies the network’s performance function (the
measure of error that the training process minimizes). By including the
sizes of the weights and biases, regularization produces a network that
performs well with the training data and exhibits smoother behavior
when presented with new data.
Early stopping uses two different data sets: the training set, to update
the weights and biases, and the validation set, to stop training when
the network begins to overfit the data.

Some different applications
Character Recognition - The idea of character recognition has
become very important as handheld devices like the Palm Pilot
are becoming increasingly popular. Neural networks can be
used to recognize handwritten characters.

Image Compression - Neural networks can receive and process
vast amounts of information at once, making them useful in
image compression. With the Internet explosion and more sites
using more images on their sites, using neural networks for
image compression is worth a look.

Stock Market Prediction - The day-to-day business of the stock
market is extremely complicated. Many factors weigh in
whether a given stock will go up or down on any given day. Since
neural networks can examine a lot of information quickly and
sort it all out, they can be used to predict stock prices.

Traveling Salesman Problem- Interestingly enough, neural
networks can solve the traveling salesman problem, but only to
a certain degree of approximation.

Medicine, Electronic Nose, Security, and Loan Applications -
These are some applications that are in their proof-of-concept
stage, with the acceptance of a neural network that will decide
whether or not to grant a loan, something that has already been
used more successfully than many humans.

Miscellaneous Applications - These are some very interesting
(albeit at times a little absurd) applications of neural networks.

Application principles

The solution of a problem must be the simple.

Complicated solutions waste time and resources.

If a problem can be solved with a small look-up table that can be
easily calculated that is a more preferred solution than a complex
neural network with many layers that learns with back-
propagation.


The speed is crucial for computer game applications.

If it is possible on-line neural network solutions should be avoided,
because they are big time consumers. Preferably, neural networks should
be applied in an off-line fashion, when the learning phase doesn’t happen
during the game playing time.


On-line neural network solutions should be very simple.

Using many layer neural networks should be avoided, if possible.
Complex learning algorithms should be avoided. If possible a priori
knowledge should be used to set the initial parameters such that very
short training is needed for optimal performance.


All the available data should be collected about the problem.

Having redundant data is usually a smaller problem than not having the
necessary data.

The data should be partitioned in training, validation and testing data.


The neural network solution of a problem should be selected from a
large enough pool of potential solutions.

Because of the nature of the neural networks, it is likely that if a single
solution is build than that will not be the optimal one.

If a pool of potential solutions is generated and trained, it is more likely
that one which is close to the optimal one is found.

Problem

Problem analysis:
• variables
• modularisation into sub-problems
• objectives
• data collection

Neural network solution

Data collection and organization:
training, validation and testing data sets

Example:
Training set: ~ 75% of the data
Validation set: ~ 10% of the data
Testing set: ~ 5% of the data


Neural network solution selection
each candidate solution is tested with the 5
2.5 5
validation data and the best performing network is 0
4
-2.5
selected
1 3
2
3 2

4
1
Network 11 Network 4 Network 7 5

7.5
5 5 5
5
2.5 5
2.5 2.5
0 0 0
4 4 4
-2.5 -2.5 -2.5

1 3 1 3
1 3
2 2
2
3 2 3 2
3 2
4 4
4 1
1 5
1 5


Choosing a solution representation:
the solution can be represented directly as a neural
network specifying the parameters of the neurons
alternatively the solution can be represented as a
multi-dimensional look-up table
the representation should allow fast use of the solution
within the application

Summary
• Neural network solutions should be kept as simple as possible.
• For the sake of the gaming speed neural networks should be applied preferably
off-line.
• A large data set should be collected and it should be divided into training,
validation, and testing data.
• Neural networks fit as solutions of complex problems.
• A pool of candidate solutions should be generated, and the best candidate
solution should be selected using the validation data.
• The solution should be represented to allow fast application.

Neural network & its applications

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