lab 1 neural network

8/13/2019 LAB 1 Neural Network

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AIM: To understand fuzzy logic system by using Matlabs Fuzzy Logic toolbox.

1.0 OBJECTIVES

After completing this units, students should be able to:

. To determine fuzzy !ariables, form the rule base and use fuzzy reasoning to chec"

the operability of the rule base.

#. To construct fuzzy logic system by using Matlabs fuzzy logic toolbox.

2.0 THEORY

$lease refer lectures notes.

3.0 EQUIPMENT LIST:

%. $ersonal computer %.# Matlab Fuzzy Logic Toolbox

4.0 EXPERIMENT PROCEDURE

Tas 1 : T!""!#$ "%&'()*

The &uality of ser!ice in restaurant is e!aluated according to scale '(', )here ' mean

excellent and ' poor ser!ice. The &uestion is, ho) much tip )e should gi!e, that the relationbet)een tip and ser!ice is correct. *reate F+ )ith -+ follo)ing rules belo)/

There are three main rules:

i0 +f the ser!ice is poor, the tip is cheap.

ii0 +f the ser!ice is good, the tip is a!erage.

iii0 +f the ser!ice is excellent, the tip is generous.

According to practice, the cheap tip is 12, a!erage 12 and generous #12 of the total

bill. Formulate fuzzy deduction system 3tip. se Matlab Fuzzy Logic Toolbox.

DEPARTMENT OF COMPUTER ENGINEERING

MASTERS LABORATORY 1(MEE 10501)

LABORATORY REPORT

Code of Coure MEE 10501

N!"e of Coure MASTERS LABORATORY 1

N!"e of S#ude$#

ZAHARI BIN HASAN GE120194

T%#&e of E'er%"e$# NEURAL NETWORK APPLICATION

No of E'er%"e$# CI 02

N!"e of Le*#urer DR ABD. KADIR BIN MAHAMAD

FACULTY OF ELECTRICAL AND ELECTRONIC ENGINEERING


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F!*u+ of E&e*#r%*!& !$d E&e*#ro$%* E$,%$eer%$,

Ex!"#$!%& ' CI 02 ( )UZZY LOGIC APPLICATION

The F+ )ith -+ )as created referring to the three rules gi!en.

Tas 2: +URNACE TEMPERATURE CONTROL

AIM: T& ,#-)%sa#- s)/)%a( ")s & #),%a( #)&% *&-)(s ' ,s!#$ Ma(a's #),%a(

#)&%&&('&.

1.0 OBJECTIVES

After completing this unit, students should be able:

. To apply some basic neuron models and learning algorithms by using Matlabsneural net)or" toolbox.

#. To demonstrate multi(layer feedfor)ard 4MLFF0 neural net)or"s by using

Matlabs neural net)or" toolbox.

%. To practice clustering )ith a self(organizing feature map and pattern association

)ith a 5opfield net)or".

2.0 THEORY

$lease refer lectures notes.

3.0 EQUIPMENT LIST:

%. $ersonal *omputer

%.# Matlab 6eural 6et)or" Toolbox

4.0 EXPERIMENT PROCEDURE

7efer to Labsheet

JEP, FKEE (Semester 1 2012/2013) #


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5.0 RESULT AND DISCUSSION

TOPIC 1 : SIMPLE NEURON MODELS AND LEARNIN6 AL6ORITHMS

TAS7 1 : DEMONSTRATION IN MATLAB

A. S!*"() #),% a#- %a#s)% ,#8!s

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For the first tas", $urelin transfer function )as selected. The )eight and bias of # are pic"ed.The output are li"e belo):

8ith the same transfer function, )e!e changed the bias into '.

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8e can se that the border line changed by (axis to '. Then )e changed the )eight to .



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8e can see that the degree of gradient changed depending on the )eight !alue

1. H& 9) )!$9s a#- '!as /a(,)s a)8 9) &,", & a #),%.

The )eight are the !alue of the m, that is the gradient of the border line. 8hile the bias

is the !alue of c, that is the border line interception on (axis.

6ext, 5ardlim transfer function )as selected. The )eight and bias of # are pic"ed. The output

are li"e belo):

JEP, FKEE (Semester 1 2012/2013) ;


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The hardlimit function graph are little bit bac")ard in

to '.

The output seems li"e are all !alue of . Then )e changed the bias !alue to '

The output became li"e figure and )e can see there are area of ' !alue and !alue. Then

changed )eight !alue to (#.

JEP, FKEE (Semester 1 2012/2013) =


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The output sho)n the !alue of in the negati!e side. Then )e changed the bias !alue to (#.

The output sho) hardlimit !alue in negati!e side

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1. H& 9) )!$9s a#- '!as /a(,)s a)8 9) &,", & a #),%.

The )eight determine the side of positi!e !alue )here )hen )eight !alue is positi!e, the

!alue of are to)ard positi!e area and !ice !ersa. 8hile the bias determining the point

of !alue change from ' to . 8hen the bias !alue is positi!e, the change point of !alue is

in the negati!e area and !ice !ersa.

B. N),% !9 /)8&% !#",

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Ha%-(!* a#- Ha%-(!*s %a#s)% ,#8!s.

a 1 a 1

P,%)(!# a#- Sa(!# %a#s)% ,#8!s.

a 3 a 1

Sa(!#s a#- L&$s!$ %a#s)% ,#8!s.

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a 1 a 1

Ta#s!$ %a#s)% ,#8!s.

a 1

2. H& 9) 89&!8) & a8!/a! ,#8! ;&% %a#s)% ,#8!< a)8s 9) &,", & a

#),%.E")%!*)# !9 9) &((&!#$ ,#8!s: !-)#! ;",%)(!#


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9%)s9&(- ;9a%-(!*= 9a%-(!*s


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Learn button8ithout bias )ith bias

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JEP, FKEE (Semester 1 2012/2013) ;


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JEP, FKEE (Semester 1 2012/2013) =


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3. H& -)8!s! '&,#-a% 89a#$)s -,%!#$ %a!#!#$ !9 9) ")%8)"% ()a%#!#$ %,().

@ecision boundary changes by testing e!ery points until its satisfy each point.

D. C(ass!!8a! !9 a 2>!#", ")%8)"%

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4. H& 9) ")%8)"% ()a%#!#$ %,() &%s &% (!#)a%( s)"a%a'() "%&'()*s.

For linearly separable problems, perceptron learning rule )or"s by di!iding the area by

classified input.

E. L!#)a%( #>s)"a%a'() /)8&%s

JEP, FKEE (Semester 1 2012/2013) ?


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5. H& 9) ")%8)"% ()a%#!#$ %,() &%s &% #>(!#)a%( s)"a%a'() "%&'()*s.

The perceptron learning rule test all the point and cannot classify the input by di!iding the

area as there are no linear border to classify the input.

TAS7 2 : TRAININ6 THE PERCEPTRON

@uring this tas" )e ha!e to study the beha!ior of the perceptron learning rule in t)o

JEP, FKEE (Semester 1 2012/2013) #'


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difference classification problems.

$ data. T data.

$ data. T data.

The output $ e!en after ''' epoch

JEP, FKEE (Semester 1 2012/2013) #


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The output $# e!en after ''' epoch

1. D&)s 9) %a!#!#$ 8/)%$)?

nly $ can con!erge.

JEP, FKEE (Semester 1 2012/2013) ##


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2. Is 9) ")%8)"% a'() & 8(ass! a(( %a!#!#$ /)8&%s 8&%%)8( a)% %a!#!#$?6o

3. I #&= 9?

Because the !ectors cannot be classified and di!ided by linear line

4. Ca# &, sa s&*)9!#$ a'&, 9) ")%8)"%s a'!(! & 8(ass! #) ,##

"a)%#s? ;#&) 9) "&s!! & 9) -)8!s! (!#)e(9

9'' 9.#9e(';

1'' 9.'=e(';

;'' #.'>e('?='' 9.'e('=

>'' .=#e(';

?'' .>%e('1

''' .;%e('1

#. *omment the result: The expected !alue of the s&uared error loss or &uadratic loss. MC

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measures the a!erage of the s&uares of the Derrors.D

Cpoch E '', MC E 9.;9e('; Cpoch E #'', MC E #.9;e('>

Cpoch E %'', MC E .'>e(9 Cpoch E 9'', MC E 9.#9e(';

Cpoch E 1'', MC E 9.'=e('; Cpoch E ;'', MC E #.'>e('?

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Cpoch E ='', MC E 9.'e('= Cpoch E >'', MC E .=#e(';

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Cpoch E ?'', MC E .>%e('1 Cpoch E ''', MC E .;%e('1

%. 7epeat the experiment.

E"&89 MSE;)s1< MSE;)s2 >.1=e('1

%'' .'>e(9 .>?e('>

9'' 9.#9e('; '.'''#?91'' 9.'=e('; =.;#e(#'

;'' #.'>e('? .%#e(';

='' 9.'e('= ;.>1e('>

>'' .=#e('; #.>9e('

?'' .>%e('1 ;.>1e('>

''' .;%e('1 %.'9e('1

*ommnet

6ot consistence , the result depend an estimator is one of many )ays to &uantify the

difference bet)een !alues implied by an estimator and the true !alues of the &uantity being

estimated.

JEP, FKEE (Semester 1 2012/2013) #=


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Cpoch E '', MC E '.'''#1 Cpoch E #'', MC E '.'''#1

Cpoch E %'', MC E .>?e('> Cpoch E 9'', MC E '.'''#?9

JEP, FKEE (Semester 1 2012/2013) #>


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Cpoch E 1'', MC E =.;#e(#' Cpoch E ;'', MC E .%#e(';

Cpoch E ='', MC E ;.>1e('> Cpoch E >'', MC E #.>9e('

JEP, FKEE (Semester 1 2012/2013) #?


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Cpoch E ?'', MC E ;.>1e('> Cpoch E ''', MC E %.'9e('1

TAS7 2 : +UNCTION APPROXIMATION

JEP, FKEE (Semester 1 2012/2013) %'


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-eneralization +n!estigate ho) the number of neurons affects the ability of a MLFF to

generalize 4that is, to classify unseen pattern !ectors0. The tas" of the net)or" is toapproximate the follo)ing function

y E x '.% sin4# x0

For this tas" )e are trying to )or" out )ith the code belo)/

1. P(& 9) %)s,( !# 9) sa*) $%a"9 9a &, 9a/) ,s)- &% "(&!#$ 9) ,#8! a#-

%a!#!#$ -aa=!.).= "(& as a ,#8! & ;).$.= %)- (!#)


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6umber of neuron in hidden layer E

*omment:

The approximation is good because the net)or" reaches its maximum capabality.

2. D& 9) sa*) !#!!a(!a!= %a!#!#$ a#- )s!#$ "%&8)-,%) as a'&/) ', &%

-!)%)# #,*')% & #),%s !# 9) 9!--)# (a)%: 2=3=4=5===F=G=10 a#- 50. T9)

$%a"9 & 9) a""%&!*a! &$)9)% !9 9) %)a( ,#8! a#- %a!#!#$ -aa

s9&,(- ') "%)s)#)- !# 9) %)"&%. D!s8,ss 9) $)#)%a(!a! ")%&%*a#8) & 9)

#)&% !# )a89 8as) ).$.= -&)s ! ,#-)%! &% &/)%! 9) -aa?

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6um of neuron in hidden layer E# 6um of neuron in hidden layer E%

6um of neuron in hidden layer E9 6um of neuron in hidden layer E 1

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6um of neuron in hidden layer E ; 6um of neuron in hidden layer E =

6um of neuron in hidden layer E > 6um of neuron in hidden layer E ?

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6um of neuron in hidden layer E ' 6um of neuron in hidden layer E 1'

O's)%/a!:

As )e can see from the graf, )hen the !alue of hidden neuron is increasing the the

net)or" reaches its maximum capabilty. The mean s&uare error is minimized , but the

net)or" response is only able to match a small part of the function.

3. 9!89 #)&% $a/) 9) ')s %)s,(s? Y&, s9&,(- a(s& s9& a a'() &% $%a"9

s9&!#$ 9) SSE a$a!#s 9) #,*')% & 9!--)# #),%s.

N,* &

N),%

!# 9)

H!--)#

La)%

SSE;")%&%*a#8)

lab 1 neural network

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