A Seminar On Neural Networks

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CONTENTS

1. Introduction to Neural Netwoks2. Human and Artificial Neurones -

investigating the similarities3. An Engineering approach4. Architecture of neural networks5. Applications of neural networks 6. Conclusion

Introduction●What Is 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.

●Why Use Neural Networks?

A trained neural network can be thought of as an "expert" in the category of information it has been given to analyze.

The Other advantages include: 1.Adaptive learning 2.Self-Organisation3.Real Time Operation4.Fault Tolerance via Redundant Information Coding

●Neural Networks Vs Conventional Computers

1.Conventional computers use an algorithmic approach i.e. the computer follows a set of instructions in order to solve a problem .

2. The network is composed of a large number of highly interconnected processing elements (neurones) working in parallel to solve a specific

problem. Neural networks learn by example.

Human and Artificial Neurones - Investigating The

Similarities● How the Human Brain

Learns?

An Engineering Approach● A Simple Neuron:

Firing Rules Before Firing:

After Firing:

Example

● If we represent black squares with 0 and white squares with 1 then the truth tables for the 3 neurones after generalisation are

Top Neuron

Middle Neuron

Bottom Neuron

A More Complicated Neuron

An MCP Neuron

Architecture Of Neural Networks

● Feed-forward networks

Feedback networks

Network Layers ● INPUT:The activity of the input units represents the raw

information that is fed into the network.

● HIDDEN: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.

● OUTPUT:The behaviour of the output units depends on the activity of the hidden units and the weights between the hidden and output units.

Perceptrons

Applications of neural networks

1. Neural networks in medicine ■ Electronic noses ■ Instant Physician

2. Neural Networks in business ■ sales forecasting ■ industrial process control ■ customer research ■ data validation ■ risk management ■ target marketing

Thanks