NEURAL NETWORK

BY…

SIDDHARTH PATEL

CLASS: IT-B (SEM: V)

ENR.NO: 100530116032

CONTENTS :

IntroductionArchitectureHuman and Artificial NeuronesApplicationsAdvantagesDisadvantagesNeural network in futureConclusion

1. INTRODUCTION .

1.1 WHAT IS A NEURAL NETWORK?

NN is an information processing paradigm . The key element of this paradigm is the

novel structure.

1.2 WHY USE NEURAL NETWORKS?

Adaptive learning. Self-Organisation. Real Time Operation.

2. ARCHITECTURE .

2.1 FEED-FORWARD (ASSOCIATIVE) NETWORKS

Allow signals to travel one way only; from input to output.

There is no feedback. It tend to be straight

forward networks .

2.2 FEEDBACK (AUTO ASSOCIATIVE) NETWORKS Signals travelling in

both directions. It is dynamic. Their 'state' is

changing continuously.

It is very powerful.

2.3 NETWORK LAYERS.

I. Input: represents the raw information.

II. Hidden: determined by the activities of the input units .

III. Output: depends on the activity of the hidden units.

3.HUMAN AND ARTIFICIAL NEURONES

3.1 HOW THE HUMAN BRAIN LEARNS?

Neuron collects signals from others through a host called dendrites.

Neuron sends out spikes of electrical activity through a long, thin stand known as an axon.

A synapse converts the activity from the axon into electrical effects that excite activity from the axon in the connected neurones.

 

Components of a neuron

 The synapse

4.APPLICATIONS

4.1 NEURAL NETWORKS IN BUSINESS

 Sales forecasting  Industrial process control Customer research Data validation Risk management Target marketing

4.2 NEURAL NETWORKS IN MEDICINE

cardiograms CAT scans ultrasonic scans, etc…

4.3 NEURAL NETWORKS IN BUSINESS

Marketing Credit Evaluation Stock Market

OTHER APPLICATIONS

Character Recognition Image Compression Food Processing Signature Analysis Monitoring

5.ADVANTAGES: Adapt to unknown situation. Autonomous learning & generalization. Robustness: fault tolerance due to network

redundancy. Noise tolerance Ease of maintenance

6.DISADVANTAGES: No exact. Large complexity of the network structure. NN needs training to operate. Requires high processing time for large NN. NN sometimes become unstable.

7.NEURAL NETWORK IN FUTURE Robots that can see, feel, and predict the

world around them. Composition of music. Handwritten documents to be automatically

transformed into formatted word processing documents.

Self-diagnosis of medical problems using neural networks.

8.CONCLUSION: Their ability to learn by example makes them

very flexible and powerful. There is no need to devise an algorithm to perform a specific task. There is no need to understand the internal mechanisms of that task. They are also very well suited for real time systems.

THANK YOU…