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BY

MANVIR SINGH GILL

M TECH (I &C )

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OUTLINE Introduction

History

How the human brain works Biological and artificial neuron model

Benefits of neural network

Neural networks versus conventional computersApplications

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INTRODUCTIONNeural network is a parallel distributed

processor made up of simple processing units.

Machine model that is designed in the sameway in which the brain performs a particulartask. (machine learning)

Learn fromexperience

Learn byexample

Learn byanalogy

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HISTORY Neural networks theory was first independently proposed

byAlexander Bain (1873) andWilliam James (1890)

According to Bain, every activity led to the firing of a certain set

of neurons. When activities were repeated, the connectionsbetween those neurons strengthened. And this repetition waswhat led to the formation of memory

James theory was similar to Bains he suggested thatmemories and actions resulted from electrical currents

flowing among the neurons in the brain. In 1898 C. S. Sherrington, conducted experiment to test

James theory , his work led to the discovery of the conceptof habituation.

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HUMAN BRAIN NN

DENDRITES

AXON

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HUMAN BRAIN NN (CONTD.)At the end of each branch, a structurecalled a synapse converts the activityfrom the axon into electrical effects that

inhibit or excite in the connectedneurones

FEATURES OF BRAIN

Brain incorporates nearly 10 billions neurons and 60 trillionconnections, synapses, between them .It perfoem hundreds of operations /sec.Need not to be replaced.Low reliability

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ANALOGY B/W BIOLOGICAL AND ARTIFICIALNN

BIOLOGICAL NN. ARTIFICIAL NN.

SOMA

DENDRITES AXON

SYNAPSES

NEURON

INPUT OUTPUT

WEIGHT

Input

values

weights

Summing

function

Bias

b

Activation

function

Induced

Field

vOutput

y

x1

x2

xm

w2

wm

w1

)(

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ARTIFITIAL NEURONArtificial neuron is device with many inputs and one

output.

The neuron has two modes of operation1. Training mode 2. Using mode

In training mode, neuron can be trained to perform aparticular task according to the input .

In using mode , if input pattern does not belong totaught list of input pattern , the firing rule determinewhether to fire or not . Firing rule not only relates theprevious input pattern but it relates all input patterns.

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BENEFITS Non linearity :- interconnection of linear or non linear

neurons.

I/O mapping:- minimize the difference b/t the desired outputand actual output with an appropriate statistical criteria.

Adaptability:- can easily be retrained to deal with minorchanges in non stationary environment.

Evidential response :- provide information not only aboutparticular pattern but also about the confidence in decision

made. Contextual informations :- It is dealt naturally by neural

network.

Fault tolerance :- has a potential to be inherently fault

tolerance.

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BENEFITS (contd.) VLSI Implementation :- parallel nature of neural

network make it potential to implement in VLSI .

Uniformity of analysis & design :- neuron , in one formor another, represent an ingredient common to all neuralnetwork

Neurobiological analogy :- engineers look toneurobiology for new ideas to solve more complex

problems than those based on conventional hard wireddesign technique.

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APPLICATIONS Signal processing:- suppress line noise, with adaptive echo

canceling, blind source separation Control:- e.g. backing up a truck: cab position, rear position,

and match with the dock get converted to steering instructions.

Manufacturing plants for controlling automated machines. Robotics :- navigation, vision recognition Pattern recognition:- i.e. recognizing handwritten characters,

recognition Medicine:- i.e. storing medical records based on case

information Speech production & recognition Financial Applications : time series analysis, stock market

prediction Game Playing:- eg chess

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