© seminar on artificial neural network and its applications by mr. susant kumar behera mrs. i....
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SEMINAR ONARTIFICIAL NEURAL NETWORK
AND ITS APPLICATIONS
By
Mr. Susant Kumar BeheraMrs. I. Vijaya
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ARTIFICIAL NEURALNETWORKS
DEVELOPED BY:
Warren McCulloch &
Walter Pits.
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A Tribute To Mr.Frank Rosenblatt
Father of Artificial Neuron Networking
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INTRODUCTION
There is no known algorithm for predicting solvent accessibility or coordination number.
Many different approaches were tried, and most of them utilized the concept of neural networks.
We shall discuss what these networks are, how do they work, and how we use them for our cause.
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ARTIFICIAL NEURAL NETWORK
• Attempts to mimic the actions of the neural networks of the human body
• Let’s first look at how a biological neural network works– A neuron is a single cell that conducts a
chemically-based electronic signal– At any point in time a neuron is in either an
excited or inhibited state
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STRUCTURE OF A NEURON
– A series of connected neurons forms a pathway– A series of excited neurons creates a strong
pathway– A biological neuron has multiple input tentacles
called dendrites and one primary output tentacle called an axon
– The gap between an axon and a dendrite is called a synapse
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Electron Micrograph of a Real NeuronElectron Micrograph of a
Real Neuron
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NEURAL NETWORKING IN A BIOLOGICAL CELL
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ARTIFICIAL NEURAL NETWORKS
• Each processing element in an artificial neural net is analogous to a biological neuron– An element accepts a certain number of input
values and produces a single output value of either 0 or 1
– Associated with each input value is a numeric weight
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FEATURES OF ANN
•NNs attempt to model the way the brain is structured:
–10 billion neurons that communicate via 60 trillion connections (synapses).
–Parallel rather than sequential processing.
•NNs are composed of the following elements:
–Neuron (soma)
–Inputs (dendrites)
–Outputs of Neurons (axons)
–Weights (synapse)
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THE ACTIVITIES WITHIN A PROCESSING UNIT
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HOW ANN WORK?
•In the preceding figure, all of the zeroth inputs to either the hidden our output layer are referred to as thresholds and are typically set to -1.
•The weights of a neural network can be any positive or negative value.
•The input values are multiplied by the weights that connect them to a particular neuron.
•Neurons take this weighted sum as input and use an activation function to compute the neurons output.
•The output of one neuron becomes the input to another neuron multiplied by a different subset of weights.
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TYPES OF NETWORK
Multilayer Perceptron
Radial Basis Function
Kohonen
Linear
Hopfield
Adaline/Madaline
Probabilistic Neural Network (PNN)
General Regression Neural Network (GRNN)
and at least thirty others
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NEURAL NETWORKS USES
• Speech recognition• Speech synthesis• Image recognition• Pattern recognition• Stock market prediction• Robot control and navigation
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Strengths of Artificial Neural Networks Neural Networks Are Versatile
Neural Networks Are Versatile
Neural Networks Can Produce Good Results in Complicated Domains
Neural Networks Can Handle Categorical and Continuous Data Types
Neural Networks Are Available in Many Off-the-Shelf Packages
STRENGTHS OF NEURAL NETWORKING
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All Inputs and Outputs Must Be Massaged to
Neural Networks Cannot Explain Results
Neural Networks May Converge on an Inferior Solution
WEAKNESSES OF ARTIFICIAL
NEURAL NETWORKS
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CONCLUSION
Neural network are very flexible and powerful.
If used sensibly they can produce some amazing results.
It has a very vast scope in this modern world.
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REFERENCESi. Neural Networks at Pacific Northwest National
Laboratory .
http://www.emsl.pnl.gov:2080/docs/cie/neural/neural.html
ii. Artificial Neural Networks in Medicine.
http://www.emsl.pnl.gov:2080/docs/cie/techbrief/NN.html
iii. Electronic Noses for Telemedicine.
http://www.emsl.pnl.gov:2080/docs/cie/neural/papers2/
keller.ccc95.abs.html
iv. Pattern Recognition of Pathology Images.
http://kopernik-eth.npac.syr.edu:1200/Task4/pattern.html
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