INTRODUCTION TO ARTIFICIAL NEURAL NETWORK

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Artificial Neural Network

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Introduction to Artificial Neural
Network
Models of the
brain and
nervous system

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 An Artificial Neural Network is a computer program that can
recognize pattern in a given collection of data and produce a
model for that data.
 These are highly parallel means process information much
more like brain than a serial computer.
Introduction to Artificial Neural
Network

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Inspiration from Neurobiology
The original inspiration for the term Artificial Neural Network came
from examination of Central Nervous Systems and their
Neurons
Axons
Dendrites
Synapses

5. Page 5Structure of a Neuron

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An Artificial Neural Network
is composed of many
artificial neurons that are
linked together according to
a specific network
architecture. The objective
of the neural network is to
transform the inputs into
meaningful outputs.

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Mathematical Representation
The neuron calculates a weighted sum of inputs and compares it to a
threshold. If the sum is higher than the threshold, the output is set to 1,
otherwise to -1.
Non-Linearity

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How do Artificial Neural Network work?
Adaptive System
Input Output
Cost
Desired
Training algorithm
Error

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Why we use Artificial Neural Network?
 Adaptive learning
 Self-Organization
 Real Time Operation
 Fault Tolerance via Redundant Information Coding

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Learning Methods in Artificial Neural Network
Learning Process is categorized into three parts:
 Supervised Learning
 Unsupervised Learning
 Reinforced Learning

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Supervised Learning
 A teacher is present during learning process and presents
expected output.
 Every input pattern is used to train the network.
 Learning process is based on comparison between the
calculated output and desired output.
 The error generated is used to change network parameters
that result improved performance.

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Unsupervised Learning
 No teacher is present.
 The desired output is not presented to the network.
 The system learns of it own by discovering and adapting to
the structural features in the input patterns.

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Unsupervised

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Reinforced Learning
 A teacher is present but does not present the expected output
to the network.
 A reward is given for correct output and a penalty for wrong
answer.

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Reinforced Learning

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Where are ANN used?
 Recognizing and matching complicated, or in completed
pattern
 Data is unreliable

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Applications of Artificial Neural Networks
 Function approximation
 Classification
 Data mining
 Time series prediction

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Function Approximation
Function approximation:
Create continuous input output
map
Inputs Outputs
The ANN must approximate f() in order to find the appropriate output for
each set of inputs.

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Example of Function Approximation
 Use a neural network to create a model that can be used
to estimate the body density(e.g. body fat)
Two steps:
 Train the network
 Use the network

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Classification
 Similar to function approximation except that
the output is a class
Classification
Interpret output as
a class
Numerical
Inputs
Numerical
Outputs
On/off
Outputs

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 Recognition
 Pattern recognition
 Character recognition
 Handwriting: processing checks
 Data association
 Not only identify the characters that were scanned
but identify when the scanner is not working properly

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 Data Conceptualization
 infer grouping relationships
e.g. extract from a database the names of those most
likely to buy a particular product.
 Data Filtering
 Planning
 Unknown environments
 Sensor data is noisy
 Fairly new approach to planning

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Types of Artificial Neural Networks
They can be distinguished by:
 Their type
 Their structure
 The learning algorithm they
use

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Types of Artificial Neural Networks
 Perceptron
 Multi-Layer- Perceptron
 Backpropagation Net
 Hopfield Net
 Kohonen Feature Map

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Perceptron
Perceptron structure

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Multi-Layer-Perceptron
Multi-Layer-
Perceptron
structure

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Back propagation Net
Backpropagation
Net structure

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Hopfield Net
Hopfield Net
structure

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Kohonen Feature Map
Kohonen Feature Map
structure

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