neuro network1
TRANSCRIPT
ARTIFICIAL NEURALNETWORKS
PRESENTED BY: KOMAL SHARMA B.Tech(IT)-III yr ROLL No.-7199
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
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
HISTORY….1943 Warren McCulloch &
Walter PitsComputational 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 interest1990s-present Continuous advances in various fields
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
ARCHITECTURE…. NETWORK LAYERS a) Input Layer b) Hidden Layer c) Output Layer RECURRENT STRUCTURE-Feedback Networks NON-RECURRENT STRUCTURE-Feedforward Networks
Network layers structure
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
WORKING…..of ANNs
Perceptron- artificial neuron
Electrical signals as numerical values
A network of neurons is formed
WORKING…..of ANNs
Principle used… FIRING RULES 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.
CHARACTERISTICS… Parallel Processing Ability
Distributed Memory
Fault Tolerance Ability
Collective Solution
Learning Ability
APPLICATIONS….. Pattern Recognition
Character Recognition
Prediction of stock price index
Neural networks in Medicine
Travelling Salesman’s Problem
Airline security control
AN EXAMPLE…stock market predictionTraining data
This month’s stock priceUnadjusted retail salesindustrial production index
Govt. receipts
Govt. expenditures
Gold price
Dollar value
Input layer Hidden layer
Next month’s stock price
Output layer
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.
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.
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
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
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-Gershens
on].pdf www.cse.unr.edu/~bebis/Mathematical/NNs/lecture.pdf www.softcomputing.net/annchapter.pdf pages.cs.wisc.edu/~bolo/shipyard/neural/local.html