Transcript PPT - The Study Material

The most intelligent device “Human Brain”.
 The machine that revolutionized the
whole world – “computer”.
 Inefficiencies of the computer has lead
to the evolution of “ Artificial Neural
Network”
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A neural network is designed as an
interconnected system of processing
elements each with a limited numbers
of input and output rather than being
programmed these system learns to
recognize pattern.
Brain is divided into two parts – Left &
Right
 Left part – rules, concepts &
calculations.
 Follows “Rule Based Learning” so
similar to “Expert System”.
 Right part – picture, image, control.
 Follows “Experience Based Learning”
so similar to “Neural Network”
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Size
 No. of neurons
 Process
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Human’s capability in real time visual
perception, speech understanding and
sensory task that implement in
machine
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ANN classified two types
Recurrent
Non recurrent
Conventional computer –single
processor sequentially dictate every
piece of the action
 ANN – very large number of
processing elements that individually
deals with a piece of a big problem
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Trained by learning
example
Memory &
processing elements
collocated
Self organizing
during learning
Knowledge stored is
adaptable
Processing is
anarchic
Speed in millisecond
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Programmed with
instruction
Memory & processing
elements separate
Software dependent
Knowledge stored in
address memory
Processing is
autocratic
Speed nanosecond
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Input are summed and passed to a
scaling function and decide which one
pass first
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If neurons receives an input from
another neurons and if both highly
active the weight between the neurons
should be strengthened.
BACK
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If the desired output and the input are
both highly active or both inactive
increment the connection weight by
the learning rate otherwise decrement
the weight by the learning rate.
Neuron Organized in the form of layer.
 Simple form, because network is feedforward or acyclic type.
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Present more then one hidden layer
are connect is called neurons or unit.
 If, the size of i/p layer is very large
then hidden layer extracts higher order
statistic which is valuable.
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Teacher teaches n/w giving environment into
form of i/p-o/p pre-calculated example.
ANN observed i/p and compared predefine o/p.
Difference is calculated refer as error signal.
Is involved in exploring environment
because right input response available.
 System receive on i/p in environment
and process o/p response.
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Self origination learning because no
external teacher.
 Tuned the regularities after optimized
i/p.
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We can recognition last encounter person
using voice or smelling in tanning section or
define particular class.
Decision space is divided into region and
region associated with class.
A critical part of system are maintained
by controller.
 Relevance of learning control should
be supervising because “after all the
human brain is computer”.
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 Info
being generated by the
environment very with time.
 And also generate variation of
environment network & never stop.
 This learning is celled continues
learning or learning of fly.
Training example as possible that means
i/p o/p mapping computed by network is
correct.
 Many i/p o/p example end up memorized
the training data.
 Finding future data but not finding true
understanding function.
 Network over trained it losses the ability
to generalize between similar i/p o/p.
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Several Ann model available to chose in
particular problem.
 They are very fast.
 Increase Accuracy ,result in cost saving.
 Represent any function ,there for they
called “universal approximation”.
 Ann are able to learn representative
example by back propagation error.
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LOW LEARNING RATE: problem require
large but complex network.
 FORGETFULL : forget old data and
training new ones.
 IMPRECISION : not provide precise
numerical answer.
 BLACK BOX APPROACH : we cant see
physical part of training transfer data.
 LIMITED FLEXIBILITY : implemented
only one system available.
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TIME SERISE PREDICTION :
 1.forcastin:sort time evolution.
 2.Modelling :feature of long term.
 3.charecterition: define fundamental
properties.
 SPEECH GENERATION :it was training to
pronounce writing English text.
 SPEECH RECOGNITION: speech convert
into written text by markon model using
some symbols.
 AUTONOMOUS VEHICLE NEVIGATION:
this is vision based and robot guidance
method.
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HAND WRITING RECOGNITION:
hidden layer are reduced free parameter
and enhance provide by the writing.
 IN ROBOTICS FIELD : a device of AI
which behave just like human.
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At last I want to say that after 200 or 300
years neural networks is so developed that it
can find the errors of even human beings
and will be able to rectify that errors and
make human being more intelligent.