07 Neural Networks1

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Negnevitsky, Pearson Education, 2005 1

Lecture 7Lecture 7

Artificial neural networks:Artificial neural networks:Supervised learningSupervised learning

Introduction, or how the brain worksIntroduction, or how the brain works

The neuron as a simple computing elementThe neuron as a simple computing element

The perceptronThe perceptron

Multilayer neural networksMultilayer neural networks

Accelerated learning in multilayer neural networksAccelerated learning in multilayer neural networks The Hopfield networkThe Hopfield network

Bidirectional associative memories (BAMBidirectional associative memories (BAM

!ummary!ummary


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Introduction, or how the brain worksIntroduction, or how the brain works

Machine learning involves adaptive mechanismsMachine learning involves adaptive mechanismsthat enable computers to learn from e"perience,that enable computers to learn from e"perience,

learn by e"ample and learn by analogy# $earninglearn by e"ample and learn by analogy# $earning

capabilities can improve the performance of ancapabilities can improve the performance of an

intelligent system over time# The most popularintelligent system over time# The most popular

approaches to machine learning areapproaches to machine learning are artificialartificial

neural networksneural networks andand genetic algorithmsgenetic algorithms# This# This

lecture is dedicated to neural networks#lecture is dedicated to neural networks#


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AA neural networkneural network can be defined as a model ofcan be defined as a model of

reasoning based on the human brain# The brainreasoning based on the human brain# The brain

consists of a densely interconnected set of nerveconsists of a densely interconnected set of nerve

cells, or basic information%processing units, calledcells, or basic information%processing units, called

neuronsneurons##

The human brain incorporates nearly &' billionThe human brain incorporates nearly &' billion

neurons and ' trillion connections,neurons and ' trillion connections,synapsessynapses,,

between them# By using multiple neuronsbetween them# By using multiple neurons

simultaneously, the brain can perform its functionssimultaneously, the brain can perform its functionsmuch faster than the fastest computers in e"istencemuch faster than the fastest computers in e"istence

today#today#


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)ach neuron has a very simple structure, but an)ach neuron has a very simple structure, but an

army of such elements constitutes a tremendousarmy of such elements constitutes a tremendous

processing power#processing power#

A neuron consists of a cell body,A neuron consists of a cell body, somasoma, a number, a number

of fibers calledof fibers called dendritesdendrites, and a single long fiber, and a single long fiber

called thecalled the axonaxon##


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Biological neural networkBiological neural network

Soma Soma

Synapse

Synapse

Dendrites

Axon

Synapse

Dendrites

Axon


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*ur brain can be considered as a highly comple",*ur brain can be considered as a highly comple",

non%linear and parallel information%processingnon%linear and parallel information%processing

system#system# Information is stored and processed in a neuralInformation is stored and processed in a neural

network simultaneously throughout the wholenetwork simultaneously throughout the whole

network, rather than at specific locations# In othernetwork, rather than at specific locations# In otherwords, in neural networks, both data and itswords, in neural networks, both data and its

processing areprocessing are globalglobal rather than local#rather than local#

$earning is a fundamental and essential$earning is a fundamental and essentialcharacteristic of biological neural networks# Thecharacteristic of biological neural networks# The

ease with which they can learn led to attempts toease with which they can learn led to attempts to

emulate a biological neural network in a computer#emulate a biological neural network in a computer#


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An artificial neural network consists of a number ofAn artificial neural network consists of a number of

very simple processors, also calledvery simple processors, also called neuronsneurons, which, which

are analogous to the biological neurons in the brain#are analogous to the biological neurons in the brain# The neurons are connected by weighted linksThe neurons are connected by weighted links

passing signals from one neuron to another#passing signals from one neuron to another#

The output signal is transmitted through theThe output signal is transmitted through theneuron+s outgoing connection# The outgoingneuron+s outgoing connection# The outgoing

connection splits into a number of branches thatconnection splits into a number of branches that

transmit the same signal# The outgoing branchestransmit the same signal# The outgoing branchesterminate at the incoming connections of otherterminate at the incoming connections of other

neurons in the network#neurons in the network#



Architecture of a tpical artificial neural networkArchitecture of a tpical artificial neural network

InputLayer OutputLayer

MiddleLayer

InputS

ignals

Ou

tputS

ignals



Analog between biological andAnalog between biological and

artificial neural networksartificial neural networks

BiologicalNeuralNetwork Artifcial NeuralNetworkSomaDendriteAxonSynapse

NeuronInputOutputWeight



!he neuron as a simple computing element!he neuron as a simple computing element

"iagram of a"iagram of aneuronneuron

Neuron Y

Input Signals

x1

x2

xn

OutputSignals

Y

Y

Y

w2

w1

wn

Weights



The neuron computes the weighted sum of the inputThe neuron computes the weighted sum of the input

signals and compares the result with asignals and compares the result with a thresholdthreshold

valuevalue

,,

# If the net input is less than the threshold,# If the net input is less than the threshold,

the neuron output is But if the net input isthe neuron output is But if the net input is

greater than or e-ual to the threshold, the neurongreater than or e-ual to the threshold, the neuron

becomes activated and its output attains a value .becomes activated and its output attains a value .

The neuron uses the following transfer orThe neuron uses the following transfer or activationactivation

functionfunction//

This type of activation function is called aThis type of activation function is called a signsign

functionfunction##

==n

iiiwxX

1



Activation functions of a neuronActivation functions of a neuron

Step function Sign function

.&

%&

'

.&

%&

'X

Y

X

Y

& &

%&

' X

Y

Sigmoidfunction

%&

' X

Y

Linear function

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07 Neural Networks1