1. ARTIFICIAL NEURAL NETWORK Intramantra Global Solution PVT LTD, Indore
A SEMINAR ON
ARTIFICIAL NEURAL NETWORK
Presented by
Intramantra Global Solution PVT LTD, Indore

2. INTRODUCTION
Artificial neural network is a graph like structure developed artificially to be automated.
McCulloch and Pitts were credited with the development of first artificial neural network.
Other scientists who contributed in this field were:-
Asbhy (1952)
Minsky (1954)
Minsky and Selfridge (1961)
Block (1962)
Rosenblatt (1962)

3. ANALOGY TO THE BRAIN
The neural network rather receives the inspiration from the known facts about the functioning of brain instead of duplicating them.
The emphasis is given on ‘brain metaphor' rather on ‘digital computing metaphor’.
Thus new neural networks are dubbed connectionist system.

4. BIOLOGICAL NEURON
DENDRITES
SOMA
AXON
SYNAPSES

5. ARTIFICIAL NEURON I/P-x Weights-w x1 w1 x2 w2 Sum Transfer O/P wn
PROCESSING ELEMENT xn

6. NEURAL NET ARCHITECTURE
1) Inter Connections
Fully connected
Partially connected
Layered network
Acyclic network
Feed Forward
Modular network

7. LAYERED NETWORK
Input layer
Hidden layer
Output layer

8. LEARNING
Learning denotes changes in the system that are adaptive in the sense that they enable the system to do the same task drawn from the same population more efficiently and more effectively the next time.
There are the schemes :
Rote Learning
Reinforcement Learning
Unsupervised Learning

9. LEARNING LAWS HEBB’S RULE
“The neuron recieves an input from an another neuron, and if both are highly active, the weight between the neuron should be strengthened.
HOPFIELD’S LAW
“If the desired output and the input are both active or both inactive, increment the connection weight by the learning rate ,otherwise decrement the weight by learning rate.

10. DELTA RULE
“This rule changes the connection weights in the way that minimizes the mean squared error of the network.
This rule is also referred to as the Windrow - Hoff rule and the Least mean square learning rule

11. APPLICATIONS ^ IN MEDICINE * Diagonising the cardit vascular system
* Instant physician
* Detection and reconstruction of odour b ANN’s
^ IN BUSINESS
* Marketing
* Credit evaluation
^ IN PRACTICE

12. ADVANTAGES &
DISADVANTAGES

13. CONCLUSION

14. QUERIES