Transcript Artificial Neural Networks.pdf

&
Fuzzy logic
 Neural network is dubbed as an replica of a
human brain on the lines of its working model
 The functionality of neural network can be
equated to the functions of an human brain
 How does human brain learn
1. the human brain transforms the given input to
output is the general idea
(inputs)
(outputs)
 The human brain consists of small elements called
Neurons
1.The neurons collects the signal through a host of fine
structures called dendrites
2. the neurons then sends out the electrical activity
through a thin stand called Axons
3. These axons are then split into thousands of
branches
4.At the end of each branch there is a structure
called synapse
5.This synapse sends the electrical activity to
other neurons which are interconnected
 When the recipient neuron traces out that the input is
large in size in comparison to its original size, its
sends the electrical activity down the axon
Synapses play an significant role in transferring the
data from one neuron to another
So, by changing the effectiveness of the synapses
learning occurs
A neural network also functions in the exact style
A neural network is fused up by a series of small
elements called neurons
We can train a neural network to perform a particular
task
The fancy point about a neural network is it can be
adjusted and trained so that the input leads to the
specific target of output
Hence neural n/w is also called as artificial neural
n/w
This is called supervised learning like a learning of a
human brain
The human brain generated output based on the inputs
given
But the neural network is a good adjustor of neurons and
the desired and target result can be outputted
BASIC STRUCTURE OF A NEURAL NETWORK
hidden layer
Input layer
output layer
(weights ) synapses
BLOCK DIAGRAM OF NEURAL N/W
TARGET
INPUTS
O/P
NEURAL NETWORK
INCLUDING
CONNECTIONS
CALLED WEIGHTS
OUTPUTS
COMPARE
ADJUST WEIGHTS
The o/p is not matched with the target o/p the weights can be adjusted,
this particular flash-point as made neural network a remarkable tool
The connection between neurons are called weights the
Weight values are adjusted to get the target output.
Take a single neuron, in a n/w it has two modes of operation
1.Training mode
2.Firing mode
in training mode the neurons will be trained to fire for a
particular input patterns
In the firing mode the neuron has two tasks
1.
To fire if the given input is form the trained list of input
patterns/ fire in case of any similarities
2.
2.vice-versa
Lets take a best example of a 3-input neuron
X1,x2,x3 are three neurons
The neuron here is trained in such a style so as to
Case1:output 0(don’t fire) if the input is 111(or)101
Case2:Output1 (fire) if the input is 000 (or) 001
X1
0
0
0
0
1
1
1
1
X2
0
0
1
1
0
0
1
1
X3
0
1
0
1
0
1
0
1
O/P
0
Case1
(not
fire)
0
Case1
0/1
None
of the
above
0/1
None
of the
above
0/1
None
of the
above
1
Case2
(neuro
n to
fire)
0/1
None
of the
above
1
Case2
0
0
0
0/1
0/1
1
1
1
(after
firing
rule)
Consider the third column which is 010, which is before
undefined after firing outputs the value “0” HOW?
Lets consider fourth column which is before 0/1 after
applying the firing rule also holds the constant value,how!!!!
HOW?
CONSIDER
Case1
Case 1
Case 2
Case 2
011
COMPARE
undefined, after
applying
firingisrule itOutput
is 0,HOW?
output
is
Output
is
Output is
AND
1
1
0
0
CONTRAST
WITH ALL
THE FOUR
SETS
111
011
OUTPUT
AFTER
FIRING
X2=1
X3=1
DIFFERS
ONLY ONE
ELEMENT
(MAXIMUM
SIMILARITY)
HENCE TAKE
THE VALUE
OF 111CASE1)=
1
101
X3=1
DIFFERS IN
TWO
ELEMENTS
000
X1=0
DIFFERS IN
TWO
ELEMENTS
001
X1=0
X3=1
DIFFERS IN
ONLY ONE
ELEMENT
(MAXIMUM
SIMILARITY)
HENCE TAKE
THE VALUE
OF
001CASE2)= 0
Because it hold maximum similarities with both
the cases( case1,case2)
The firing rule states that it has to remain
undefined because of a tie
with the same mechanism of neurons getting
trained/adjusted/fired, and outputting the
target o/p has made neural n/w instrumental in
a many spheres
Neural network merged with fuzzy logic ha done
wonders in the fields of data mining ETC
Fuzzy logic tool was introduced in 1965 by lot fi zadeh
Fuzzy means something which is blurred/ hazy
Fuzzy logic means is a mathematical tool that deals with
uncertainty
Haziness persist in any realistic process, fuzzy logic task is to
decode exactness out of something which is inexact
The human brain has the capability to make a clear distinction
between an image and an object even if it is blur
Linear computing is able to read just pixels as a set of colours
Fuzzy logic capability to solve problems that linear computing
is not able to do.
Fuzzy logic hence embedded in neural networks
show more transparency
APPLICATIONS of neural networks :
speech recognition
Pattern recognition
image processing
data mining
robotics
data segmentation and compression
Fuzzy logic is used to model systems that has
ambiguity or opaqueness ,it can be vagueness/lack
of information/miscalculation of measurements
EXAMPLE
Entity x: to this entity a “short” person may be one
whose height is below 4.20
Entity y: to this entity a “short” person may be one
whose height is beneath or equal to 3.9
Here “short” is the language descriptor , it applies
the same meaning to both x and y but it established
that they don’t have a unique definition for short
Such type of information associated with dilemma
are made feasible to the computers with the tool
called fuzzy logic
The fuzzy logic incorporates a simple “ IF x AND y
THEN z” approach rather than modeling a system
mathematically
Example:
Rather than Dealing the temperature control in
terms such as 1. “SP=500f”
2.”T<1000f”
3.”210c<TEMP<220c”
Fuzzy logic deals in terms like
1. IF(process is too cool) AND(“ getting colder”)
THEN(add heat to the process)
2. IF(process is too hot) AND (process is heating rapidly) THEN
(cool the process quickly)
Because of this potential to deal with complex tasks fuzzy logic
has wide range of application having its share in all household
appliances
1.Washing machines
2. Electric rice cookers
3.Speech recognition
4.Stock market predictions
5.High speed trains
fuzzy logic in washing machines
The washing machine first tests how dirty the laundry is
Once it knows how dirty the laundry is it can easily
calculate how long it can wash it
First it always take a base of 10minutes
Then if the cloth put in it is 100% dirty then it adds two
minutes (10+2=12 minutes)
If the cloth put in the washing machine is 50% dirty then
it adds 1 minute to the base of 10min(10+1=11min)
The laundry can also be greasy at the same time
If the laundry is greasy then add 2minutes to the
base of 10min(=12min)
If the laundry is 50% greasy then add 1 minute to
the base of 10min(=11min)
1.
FUZZ MACHINE
2.
3.
Shirt 1: 100% dirty( 2min)
Shirt 2: 100% clean(0 min)
Shirt 3:50% greasy(1min)
Total time taken by the washing machine
working with fuzz logic is
=10(base)+2min+0min+1min = 13min ( for
three shirts)
ADVANTAGES OF NEURAL N/W AND FUZZY LOGIC
1. High accuracy: neural n/w are able to give the exact
result of complex systems
2. Noise tolerance: neural n/w are very flexible with
respect to incomplete, missing and noisy data
3. Ease of maintenance neural n/w can be updated with
fresh data making them useful for dynamic
environments
4. When an element (i.e) neuron fails the other neuron
undertakes the task
Though the advent and discovery of neural network
is dated back to 1943 by warren mc culloch, it has
been a wonder tool in networks till date
Neural networks has been enhanced and able to
hammer out solutions for the problems which are
complex for conventional computers/human beings
Neural network is merited in many ways with only
one setback i,e training the neurons to generate a
target o/p
Hence neural networks and fuzzy logic are
supplementary to computers