this presentation will familiarize many with artificial neural networks.....

1. ARTIFICIAL
NEURAL
NETWORKS
PRESENTED BY:
KOMAL SHARMA
B.Tech(IT)-III yr
ROLL No.-7199

2. CONTENTS…
The following points are covered in this presentation:
 Introduction
 History
 Inspiration from biological neurons
 Architecture of neural network
 Working of artificial neurons
 Characteristics
 Applications
 Advantages and disadvantages
 Future scope
 conclusion

3. INTRODUCTION
 An interconnected group of nodes , akin to the vast network of
neurons in a brain
 A computational model inspired in the natural neuron
 An attempt at modelling the information processing capabilities
of nervous systems.
 An biological approach to Artificial Intelligence
 Process information by their dynamic state response to external
inputs
A basic artificial
neuron network

4. HISTORY….
1943
Warren McCulloch &
Walter Pits
Computational model
for neural networks
1950 Possible to simulate a hypothetical neural network
1958 Frank Rosenblatt Formation of perceptron
1959
Bernard Widrow &
Marcian Hoff
MADALINE-first neural
network
1962 Neural research went down drastically and was left behind
1972 Kohonen &Anderson
Similar network
independently
1975 First multilayered network
1982 John Hopfield Renewed interest
1990s-present Continuous advances in various fields

5. INSPIRATION FROM
BIOLOGICAL NEURONS
 Examinations of humans’ CNS inspired the concept of artificial
neural networks
 Animals react adaptively to changes in their external and internal
environment-use their nervous system to perform these behavior
 An appropriate/simulation of the nervous system should be able to
produce similar responses and behaviors in artificial systems.
A biological neuron An artificial neuron

6. ARCHITECTURE….
 NETWORK LAYERS
a) Input Layer
b) Hidden Layer
c) Output Layer
 RECURRENT STRUCTURE-Feedback Networks
 NON-RECURRENT STRUCTURE-Feedforward Networks
Network layers structure

7. FROM HUMAN NEURONS TO
ARTIFICIAL NEURONS…..
Try to deduce the essential features of neurons and their interconnections
A computer is programmed to simulate features
Incomplete knowledge of neurons and limited computing power result into..
Necessarily gross idealizations of real networks of neurons
The neuron model A basic artificial neuron

8. WORKING…..of ANNs
Perceptron-
artificial neuron
Electrical signals as
numerical values
A network of
neurons is formed

9. WORKING…..of ANNs
Principle used…
 Determine how one calculates whether one should fire for any input pattern
 Some sets which cause it to fire have 1-taught set of patterns and others which
do not have 0-taught set.
 Accounts for high flexibility
 For example:
Suppose there is 3-input
neuron which is taught to
produce output 1 when the
input is 111 or 101 and outputs
0 when the input is 000 or
001.

10. CHARACTERISTICS…
 Parallel Processing Ability
 Distributed Memory
 Fault Tolerance Ability
 Collective Solution
 Learning Ability

11. APPLICATIONS…..
 Pattern Recognition
 Character Recognition
 Prediction of stock price index
 Neural networks in Medicine
 Travelling Salesman’s Problem
 Airline security control

12. AN EXAMPLE…stock market prediction
Training data
This month’s stock price
Unadjusted retail sales
industrial production
index
Govt. receipts
Govt. expenditures
Gold price
Dollar value
Input layer Hidden layer
Next month’s
stock price
Output layer

13. ADVANTAGES….
 Perform tasks that a linear program can not do.
 A neural network learns and does not need to be
reprogrammed
 It can be implemented in any application
 No algorithm is required. They learn by examples.

14. DISADVANTAGES…
 Training is needed to operate neural network.
 Emulation is needed because architecture of neural network
is different from the architecture of the microprocessors.
 High processing time is required for large neural networks.
 Not a general purpose problem solver.
 No structured methodology.

15. RECENT ADVANCES &
FUTURE APPLICATIONS…
Integration of fuzzy logic into neural networks
Pulsed Neural Networks
Improvement of existing technology
Common usage of self-driving cars
Robots that can see, feel or predict the world around them

16. CONCLUSION…
Computing world to gain a lot from neural networks
Have a very promising future due to its flexibility
Possibility that some day “conscious” networks might be produced
Despite having a huge potential, these are best used only when they
are integrated with computing, AI, fuzzy logic and related subjects

17. REFERENCES…
 https://en.wikipedia.org/wiki/Artificialneuralnetwork
 www.psych.utoronto.ca/users/reingold/courses/ai/cache/neural2.
html
 www.doc.ic.ac.uk/~nd/surprise96/journal/vol4/cs11/report.html
 https://datajobs.com/data-science.../Neural-net[carlos-
Gershenson].pdf
 www.cse.unr.edu/~bebis/Mathematical/NNs/lecture.pdf
 www.softcomputing.net/annchapter.pdf
 pages.cs.wisc.edu/~bolo/shipyard/neural/local.html