Artificial Neural Network

Coordinator:

Ms. Neha Chaudhary

AdityaCS-3rdyear

ContentIntroductionHistoryCapabilitiesApplicationsReal world implementationIssuesAdvantage/DisadvantageConclusionsReferences

IntroductionBrain : A highly complex, non-linear &

parallel computing

Structural constituent : ‘Neurons’

A computer program designed to model the human brain.

Biological Neuron

• Biological neuron

Conti…Our brains are made up of about 10

billion tiny units called neurons .

Tree like Nerve fibres are called dendrites .

Signals coming into the neuron are received via junctions called synapses .

Artificial Neuron•

Dendrites

Soma (cell body)

Axon

SynapsesThe information transmission happens at the synapses.

Conti…A network of interconnected functional

elements.

A NN is trained to recognize and generalize the relationship between a set of inputs and outputs.

Artificial neural networks

Inputs

Output

Several inputs & one output.

HistoryInspiration for development came from

attempts to model the human central nervous system.

McCulloch-Pitts 1943 Introduced a simple NN model using electrical circuits.

Conti…Hebb 1949 Wrote that neural pathways

are strengthened every time they are used.

Minsky 1954: Learning Machine.

Rosenblatt’s Perceptron 1957.

Conti…

Widrow 1960 : Adaline model.

Recent work includes Boltzmann machines, competitive learning models, multilayer networks, and adaptive resonance theory models.

Artificial neuronsNeurons work by processing

information.

The McCulloch-Pitts model

Outputw2

w1

w3

wn

wn-1

. . .

x1

x2

x3

…

xn-1

xn

y)(;

1

zHyxwzn

iii == ∑

=

CapabilitiesNon-linearityInput/Output mappingAdaptivityEvidential responseFault tolerenceVLSI implementability

ApplicationsRoboticsImage processingSpeech/Pattern recogntionGamingTarget RecognitionMedical Diagnosis Voice and touch interface with

computers and other devices.

Real World ImplementationLogical reasoningPattern recognitionPlanningGenetic programmingCommon sense knowledgeRepresentationControl system

IssuesComplex programsDifficult to implementMachine prediction may not be accurate Human beings may lose their importance

Advantage

Pattern recognitionDoes not need to be reprogrammedImplemented in any application Adaptive learningSelf-OrganisationReal Time OperationFault Tolerance

Disadvantage

Loss of human control.

They may dominant us in the near future.

ConclusionsOnce NNs are trained they can be

reapplied over and over again.

Can be linked with other models to solve complex problems.

Conti…Neural Networks LEARN. They are not

programmed.

Can be applied to areas where humans are often wrong too.

Referenceswww.wikipedia.org

www.ai-junkie.com

“Artificial Neural Network” by B.Yegnanarayana

Thank You