EFFICIENCY & ERRORS IN MATLAB
mean(values),max(values),min(values));
else
disp('Must have a positive number of
values');
end;
22.19 Review
• definite vs.
indefinite iteration
• FOR loop
• WHILE loop
• BREAK statement
• loop usage
• loop control
• loops vs. implicit
vectorisation
23 EFFICIENCY & ERRORS
• Correctness of
code is clearly a vital goal
Requires both a
correct programming approach and understanding of computer operations
• Efficiency of code
is also an important consideration
minimise impact on
computer
• This lecture
covers the following topics of relevance to both correctness and efficiency:
numeric limitations
of the computer and implications for program correctness
debugging techniques
order of an
algorithm (complexity)
timing functions in
Matlab
performance
profiling in Matlab
some guidelines for
efficiency
looping vs. implicit
vectorisation
References: For Engineers (Ch. 2)
Abernathy
& Allen (Ch. 8)
Mastering
(Ch. 11, 15), Student (Ch. 10)
23.1 Motivation
• A correct program
is not simply a matter of luck
- programmer must understand
syntax & semantic of language being employed
- programmer must employ a correct
algorithm & have considered all alternatives
- programmer must understand
inherent limitations of the computer and how that might affect an otherwise
“correct” program
• Simply correct is
not always sufficient for engineering & real-world problems:
- A correct program is little use
if it takes 1 year to execute but the answer is required today
• Efficient code
should always be an objective of a programmer:
- program is a more efficient user
of computer resources (runs faster, uses less memory etc.)
- aesthetically pleasing
23.2 Numeric Limitations
• Computers set
aside a fixed size storage area for numeric values
- implies limits to representation
(range and precision)
- implies programs can be
logically correct but still produce incorrect results
- there are means of circumventing
this problem though they are costly in compute time & memory
• All Matlab numeric
variables are stored as double-precision floats
- 64-bits of storage
23.2.1 Overflow
• Numbers may become
too large for the machine to represent:
>> x=1e10
x =
1.0000e+10
>> for
cnt=1:20
x=x*x;
fprintf('%d: %f\n',cnt,x);
end;
1: 100000000000000000000.000000
2:
10000000000000000303786028427003666890752.000000
Numerical
Limitations (Cont)
3:
100000000000000000026609864708367276537402401181200809098131977453489758916313088.000000
4: 10000000000000000065284077450682265568456642148886267118448844545520511777838181142510337509988867035816342470187175785193750117648543530356184548650438281396224.000000
5: Inf
:
20: Inf
23.2.2 Underflow
• Very small values
(close to zero) round to zero
>> x=1e-10
x =
1.0000e-10
>> for
cnt=1:10
x=x*x;
fprintf('%d %e\n',cnt,x);
end;
1 1.000000e-20
2 1.000000e-40
3 1.000000e-80
4 1.000000e-160
5 9.999889e-321
6 0.000000e+00
7 0.000000e+00
8 0.000000e+00
9 0.000000e+00
10 0.000000e+00
>> diary off
23.3 Rounding & Cancellation
23.3.1 Rounding (Precision)
• Values may not be
represented exactly inside the machine
>>
test=9.8765432109876543210987654321
test =
9.87654321098765
>>
fprintf('%30.28f\n',test);
9.8765432109876538646631161100
23.3.2 Cancelation (Order of Precedence)
• Large values may
cancel small values and order of mathematical operations can be critical
>> x=1e50
x =
1.0000e+50
>> y=-1e50
y =
-1.0000e+50
>> x+y+1
ans =
1
>> x+1+y
ans =
0
>> x+(1+y)
ans =
0
23.4 Work-Arounds for Numeric Limitations
• No simple fixes
for these problems:
- be aware of the limitations of
computers
- think about how those
limitations might impact the program being created
23.4.1 For Instance: Comparing Two Numbers
• Don’t check for
equality between two floating point numbers
- round-off error
may mean that they never become equal
• Check that the
difference is small
Don’t
while x~=y
<do something to make x
closer to y>
end;
Do
while abs(x-y)>eps
<do something to make x
closer to y>
end;
Don’t
while x~=0
<do something>
end;
Do
while abs(x)>eps
<do something>
23.5 Errors & Debugging in Matlab
• Testing is a
necessary step in (attempting to) ensuring that code is error free
- not only logic errors but also
possibility of numeric limitations
• The following are
useful simple steps in debugging an M-file
- remove semi-colons from lines to
see results as they are being calculated
- place disp() and fprintf()
statements in the code to both view the value of variables and see when
sections of code are being reached
- insert the keyboard command into
the M-file
• execution is
suspended when the program reaches that point
• use standard
Matlab commands to view variables etc.
|
• type the word
return when you are finished and execution of the M-file resumes from the
following line.
|
23.6 Matlab Debugger
• Matlab includes a
debugging tool for tracing execution, setting breakpoints, viewing the
workspace, etc.
|
• Graphical form
integrated into Matlab’s editor (PC & X-Window version)
• Command line form
- most important commands include
dbstop, dbstatus, dbclear, dbcont and dbstep (see help pages)
• General usage
pattern is:
- set breakpoint (red stop-sign in
graphical version) on line of code
- run the M-file using the
debugger
- execution will stop when
break-point is hit
- examine environment (variables
etc.)
- continue execution or step
through a line at a time when satisfied
23.7 Order of an Algorithm
• The time an
algorithm requires to complete is usually a function of the input data size
- e.g., to sort a list of names
takes longer and longer as the list grows in size
• The order notation
has been developed to quantify the complexity of different algorithms
- the highest polynomial order or
exponent is recorded (constants are ignored)
- n is used to represent the size
of the input data
- used as a measure of the
efficiency of an algorithm
• For instance:
- To search a list of un-ordered
names is O(n) For instance, for 1000 names it is a linear function of 1000
operations.
|
Kami ada di Jakarta Selatan. KAMI MEMBERIKAN KURSUS MATLAB ONLINE - HUBUNGI MASTER ENGINEERING EXPERT (MEE) 081219449060. Kami membuka kursus Matlab untuk pemula dan mahasiswa atau insinyur yang ingin memperdalam Matlab dan menerapkan dalam bidang teknikal, engineering, rekayasa, dsb. Format bimbingannya tugas-tugas yang bisa membantu Skripsi, Tesis, DISERTASI
Bimbingan dilakukan secara online bisa lewat WA atau email Dijamin Bisa, atau bisa mengulang kembali. Kami juga dapat membantumembuatkan aplikasi atau program matlab/lainnya. Anda akan dilatih oleh Tim Profesional - HUBUNGI MASTER ENGINEERING EXPERT (MEE) 081219449060. Email: kursusmatlab@gmail.com - To efficiently search a list of ordered names using a binary search is O(log2n). For instance to search a list of 1000 names is some linear function of 10 operations
- The fastest sorting algorithms
are O(nlogn)
• Many difficult
problems such as those known as NP-complete (e.g., travelling salesman) have
no known algorithm that guarantees the optimal solution in polynomial time
23.8 Timing in Matlab
• Matlab provides a
number of date and time related functions
- some of these functions are
useful for recording how long a task takes to complete (a measure of
efficiency)
• tic and toc start
and stop a stop-watch (respectively)
- toc displays the elapsed user
time since the stopwatch was started
>> tic; [X Y
Z]=mapGen(50); Z10=mapSmooth(Z,10); toc
elapsed_time =
11.5290
• cputime returns
the current CPU seconds used since the Matlab session was started
- record before and after a task
and examine the difference
- CPU time is a far better measure
of the work an algorithm entails than the user (elapsed) time.
|
>>
before=cputime;
>> [X Y
Z]=mapGen(50); Z10=mapSmooth(Z,10);
>>
fprintf('CPU time=%6.4f\n',cputime-before);
CPU time=11.2600
>> diary off
23.9 Performance Profiling in Matlab
• M-file performance
can be analysed using Matlab’s built-in profiler
- profiler examines a running
program to determine what portion of time is spent in each component of the
code
- results can be used to help
fine-tune and speed the code
• General operation
is the following
- reset the profiler
- indicate to the profiler which
M-file is to be analysed
- run the M-file with real data
- generate a report using the
profiler
23.10 Profiler Example
>> [X Y Z] =
mapGen(50);
>> profile
reset
>> profile
mapMeteor
>> Zmeteor =
mapMeteor(Z);
>> profile
report
Total time in
"CS1E/Matlab/Map/mapMeteor.m": 0.22 seconds
100% of the total
time was spent on lines:
[74 72 77]
71: for jIndex=1:size(oMap,1)
0.07s, 32% 72:
if (abs((iIndex-xCentre)^2+(jIndex-yCentre)^2-radius^2)<=
73:
newMap(jIndex,iIndex)=depth+0.02*difference;
0.11s, 50% 74:
elseif ((iIndex-xCentre)^2+(jIndex-yCentre)^2 < radius^2)
75:
newMap(jIndex,iIndex)=depth*(100.0+randn)/100.0;
76: else
0.04s, 18% 77:
newMap(jIndex,iIndex)=oMap(jIndex,iIndex);
78: end;
>> profile
done
>> diary off
23.11 Guidelines for Efficiency
• Clearly, writing
efficient algorithms is a desirable goal
• However, there is
no simply formula for writing efficient algorithms
- experience and consideration of
the current problem are vital
• That said, there
are often certain guidelines or recurring areas in which efficiencies can be
made
23.11.1 Special Cases and Redundant Checking
• Avoid redundant
checks and checks for special cases, especially inside loops
- e.g., checking for first or last
item inside a large loop
Don’t (100 redundant checks)
for num=1:100
if rem(num,10)==0
fprintf(‘%4d\n’,cnt);
else
fprintf(‘%4d\t’,cnt);
end;
end;
Do
for outer=0:9
for inner=1:10
fprintf(‘%4d\t’,outer*10+inner);
end;
fprintf(‘\n’);
end;
23.12 Efficiency: Redundancy & Functions
23.12.1 Avoid Redundant Computations
• Many times
calculations are performed (inside a loop) needlessly
- the value has previously been
calculated or can be derived simply from an earlier value
Don’t (needless trig
calls, multiplications, powers)
for
intervals=0:0.1:5
xLoc=velocity*cos(angle)*intervals;
yloc=velocity*sin(angle)*intervals-…
0.5*gravity*intervals^2;
end;
Do
xDelta=velocity*cos(angle)*0.1;
yMult=velocity*sin(angle);
gravConst=0.5*gravity;
xLoc=0; yLoc=0;
for intervals=0.1:0.1:5
xLoc=xLox+xDelta;
yLoc=intervals*(yMult-gravConst*intervals);
end;
23.12.2 Minimise Costly Function Usage
• Functions have a
cost overhead (calling process)
- trig functions, raising to a
power, etc.
|
- minimise their wasted usage
• See above example
23.13 Efficiency: Arrays
23.13.1 Minimise Array Referencing
• Referencing array
elements has a cost overhead
- calculation of the (memory)
address of the element is necessary
• Repeatedly
referenced elements should be saved as a scalar
Don’t (Repeated reference to values(1))
for
index=2:size(values,2)
if values(index)>values(1)
<do something special>
end;
end;
Do
firstValue=values(1);
for
index=2:size(values,2)
if values(index)>firstValue
<do something special>
end;
end;
23.13.2 Time vs Space
• Time and space can
often be traded off against each other for the needs of the problem
- e.g., calculation of a trig function through a
series expansion versus storage as a table
23.14 Efficiency: Loop Termination
23.14.1 Avoid Late Termination of Loops (Needless
Calculations)
• Many times a task
completes sooner than the worst case
- if completion detection is cheap
then further work should be abandoned.
|
Kami ada di Jakarta Selatan. KAMI MEMBERIKAN KURSUS MATLAB ONLINE - HUBUNGI MASTER ENGINEERING EXPERT (MEE) 081219449060. Kami membuka kursus Matlab untuk pemula dan mahasiswa atau insinyur yang ingin memperdalam Matlab dan menerapkan dalam bidang teknikal, engineering, rekayasa, dsb. Format bimbingannya tugas-tugas yang bisa membantu Skripsi, Tesis, DISERTASI
Bimbingan dilakukan secara online bisa lewat WA atau email Dijamin Bisa, atau bisa mengulang kembali. Kami juga dapat membantumembuatkan aplikasi atau program matlab/lainnya. Anda akan dilatih oleh Tim Profesional - HUBUNGI MASTER ENGINEERING EXPERT (MEE) 081219449060. Email: kursusmatlab@gmail.com
Don’t (no detection of completion + extra
looping)
for
outer=1:size(values,2)-1
for index=1:size(values,2)-1
if values(index)>values(index+1)
tmp=values(index);
values(index)=values(index+1);
values(index+1)=tmp;
end;
end;
end;
Do
sorted=0;
while ~sorted
sorted=1;
for index=1:size(values,2)-1
if values(index)>values(index+1)
tmp=values(index);
values(index)=values(index+1);
values(index+1)=tmp;
sorted=0;
end;
end;
end;
23.15 Efficiency – Complicating Issues
• Code written for
efficiency often looses (a degree of) readability
- extra code added (see above
examples)
- “simple” structure of original
problem can be altered due to efficiency considerations (e.g., projectile
motion example above)
• The trade-off
between readability and efficiency is yet another issue a programmer must
consider
- relative weighting (importance)
can be task/problem dependent
• Integrated
programming environments such as Matlab complicate questions of efficiency
- implementation/translation
process may have in-built efficiencies & inefficiencies
+ e.g., Matlab and
vectorisation
- Hence accumulated
wisdom/experience/intuition may be incorrect
- In such cases quantifiable
measures such as a profiler should be relied upon.
|
23.16 Looping vs Implicit Vectorisation
• Matlab is built
around the concept of a Matrix/array
- all variables are considered to
be arrays
• In-built operators
and functions are often defined to perform an operation on an entire array in
a single step
- This is implicit vectorisation
• In practice this
means that code which requires explicit looping in most other languages can
be written as single statements in Matlab
- e.g., tables of values, processing
array elements etc.
|
• Matlab performs
(executes) implicit vectorisation far faster than the equivalent, explicitly
looping code
In practice, any
Matlab code that can be written to utilise implicit vectorisation should do
so rather than using explicit looping.
|
23.17 Timing Example
• Three different
scripts to perform the same task, each timed
- first looping without
considering efficiency
- second looping but considering
efficiency
- third using implicit
vectorisation
>> %% Loop
with no thoughts of efficiency
>> shell1
shell1 took 3.70 cpu seconds to complete
elapsed_time =
4.1651
>> % Loop with efficiency considered
>> shell2
shell2 took 1.86 cpu seconds to complete
elapsed_time =
2.0539
>> % Implicit
vectorisation. No attempted efficiency
>> shell3
shell3 took 0.01 cpu seconds to complete
elapsed_time =
0.0640
23.18 Shell1 – Looping without Efficiency
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% shell1.m
% Calculate the flight of a shell at
0.01
% second intervals for 300 seconds.
The shell
% has a muzzle velocity of 800
metres/sec
% and was fired at an angle of 45
degrees.
|
%
% This M-file is one of three
"sister"
% scripts all solving the same
problem.
|
Kami ada di Jakarta Selatan. KAMI MEMBERIKAN KURSUS MATLAB ONLINE - HUBUNGI MASTER ENGINEERING EXPERT (MEE) 081219449060. Kami membuka kursus Matlab untuk pemula dan mahasiswa atau insinyur yang ingin memperdalam Matlab dan menerapkan dalam bidang teknikal, engineering, rekayasa, dsb. Format bimbingannya tugas-tugas yang bisa membantu Skripsi, Tesis, DISERTASI
Bimbingan dilakukan secara online bisa lewat WA atau email Dijamin Bisa, atau bisa mengulang kembali. Kami juga dapat membantumembuatkan aplikasi atau program matlab/lainnya. Anda akan dilatih oleh Tim Profesional - HUBUNGI MASTER ENGINEERING EXPERT (MEE) 081219449060. Email: kursusmatlab@gmail.com % Each script is written with different
% efficiency goals in mind and timed
with
% the cputime function (the
calculated
% results are ignored).
|
%
% This script is written with no
mind
% to efficiencies. Nor does it
employ
% Matlab's implicit vectorisation
%
% Author: Spike
% Date: 19/3/1999
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
tic;
before=cputime;
velocity=800.0;
angle=45.0;
gravity=9.81;
for frames=1:30000
seconds=frames/100;
x(frames)=velocity*seconds*cos(angle*pi/180);
y(frames)=velocity*seconds*sin(angle*pi/180)-...
|
0.5*gravity*seconds^2;
end;
fprintf('shell1 took
%6.2f cpu seconds to complete\n',cputime-before);
toc;
23.19 Shell2 – Looping Efficiently
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% shell2.m
% Calculate the flight of a shell at
0.01
% second intervals for 300 seconds.
The shell
% has a muzzle velocity of 800
metres/sec
% and was fired at an angle of 45
degrees.
|
Kami ada di Jakarta Selatan. KAMI MEMBERIKAN KURSUS MATLAB ONLINE - HUBUNGI MASTER ENGINEERING EXPERT (MEE) 081219449060. Kami membuka kursus Matlab untuk pemula dan mahasiswa atau insinyur yang ingin memperdalam Matlab dan menerapkan dalam bidang teknikal, engineering, rekayasa, dsb. Format bimbingannya tugas-tugas yang bisa membantu Skripsi, Tesis, DISERTASI
Bimbingan dilakukan secara online bisa lewat WA atau email Dijamin Bisa, atau bisa mengulang kembali. Kami juga dapat membantumembuatkan aplikasi atau program matlab/lainnya. Anda akan dilatih oleh Tim Profesional - HUBUNGI MASTER ENGINEERING EXPERT (MEE) 081219449060. Email: kursusmatlab@gmail.com %
% This M-file is one of three
"sister"
% scripts all solving the same
problem.
|
%
% This script is written with
efficiencies
% in mind, but without employing
Matlab's
% implicit vectorisation.
|
%
% Author: Spike
% Date: 19/3/1999
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
tic;
before=cputime;
velocity=800.0;
angle=45.0;
gravity=9.81;
x=zeros(1,30000);
y=zeros(1,30000);
currentX=0;
currentY=0;
xDelta=velocity*cos(angle*pi/180)*0.01;
yMult=velocity*sin(angle*pi/180);
gravConst=0.5*gravity;
seconds=0;
for frames=1:30000
seconds=seconds+0.01;
currentX=currentX+xDelta;
x(frames)=currentX;
y(frames)=yMult*seconds-gravConst*seconds*seconds;
end;
fprintf('shell2 took
%6.2f cpu seconds to complete\n',cputime-before);
toc;
23.20 Shell3 – Implicit Vectorisation
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% shell3.m
% Calculate the flight of a shell at
0.01
% second intervals for 300 seconds. The
shell
% has a muzzle velocity of 800
metres/sec
% and was fired at an angle of 45
degrees.
|
%
% This M-file is one of three
"sister"
% scripts all solving the same
problem.
|
% Each script is written with
different
% efficiency goals in mind and timed
with
% the cputime function (the
calculated
% results are ignored).
|
Kami ada di Jakarta Selatan. KAMI MEMBERIKAN KURSUS MATLAB ONLINE - HUBUNGI MASTER ENGINEERING EXPERT (MEE) 081219449060. Kami membuka kursus Matlab untuk pemula dan mahasiswa atau insinyur yang ingin memperdalam Matlab dan menerapkan dalam bidang teknikal, engineering, rekayasa, dsb. Format bimbingannya tugas-tugas yang bisa membantu Skripsi, Tesis, DISERTASI
Bimbingan dilakukan secara online bisa lewat WA atau email Dijamin Bisa, atau bisa mengulang kembali. Kami juga dapat membantumembuatkan aplikasi atau program matlab/lainnya. Anda akan dilatih oleh Tim Profesional - HUBUNGI MASTER ENGINEERING EXPERT (MEE) 081219449060. Email: kursusmatlab@gmail.com %
% This script is written with no
mind
% to efficiencies. Nor does it
employ
% Matlab's implicit vectorisation
%
% Author: Spike
% Date: 19/3/1999
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
tic;
before=cputime;
velocity=800.0;
angle=45.0;
gravity=9.81;
seconds=0.01:0.01:300;
x=velocity*seconds*cos(angle*pi/180);
y=velocity*seconds*sin(angle*pi/180)-...
|
Hasil pencarian lain
www.teknikinformatika.net/2014/01/17/array-pada-matlab Cached
Array
merupakan bentuk penyimpanan data yang dapat menyimpan sekumpulan data yang
mempunyai tipe sama dalam sebuah nama. Sekalipun nama -nya sama, akan tetapi
data 1 ...
www.teknikinformatika.net/.../pengulangan-pada-matlab-part-1 Cached
i
= 1; while i <= 10 disp('Teknik Informatika') i = i + 1; end.
Pengulangan for Pengulangan for biasanya digunakan untuk kasus dimana banyaknya
pengulangan yang akan ...
teknikinformatika-esti.blogspot.com/...program-matlab.html Cached
May
05, 2011 · MATLAB adalah sebuah bahasa dengan
(high-performance) kinerja tinggi untuk komputasi masalah teknik. Matlab
mengintegrasikan komputasi, visualisasi, dan ...
teknikinformatika-esti.blogspot.com/.../tutorial-matlab.html Cached
Jun
06, 2011 · Teknik Informatika. Ikatlah Ilmu Dengan
Menuliskannya. Laman. ... Buat yang mau belajar Matlab, Nich aku buatin
tutorial sederhananya : Tutorial 1. Tutorial 2.
www.academia.edu/People/Matlab?page=721 Cached
Matlab. People 18,410. Documents 649. Journals 0. Jobs 0. Related
Research Interests. Matlab Programming. 3,102. Matlab &
Simulink programming. 1,396. Modeling and ...
www.pdfsdocuments2.com/m/30/matlab-code-for-sapi-speech...
Title:
Matlab Code For Sapi Speech To Text Keywords: Matlab Code For
Sapi Speech To Text Created Date: 11/3/2014 2:13:16 PM
jasapembuatantesisilmukomputer.blogspot.com/2013/07/ide... Cached
Jul
18, 2013 · Ide Judul Tesis Skripsi Informatika Teknik kompilasi. An
efficient probabilistic context-free parsing algorithm that computes prefix
probabilities
viplab.if.its.ac.id/index.php/category/tutorial-matlab Cached
Research
Group of Vision and Image Processing "Laboratory of Vision, Image
Processing, and Graphics, Jurusan Teknik Informatika, ITS,
Surabaya"
www.kaskus.co.id/.../teknik-informatika--informatics.../245 Cached
ada
yang tau cara membuat fuzzy momdani mom dgan program matlab or java ?
www.skripsitesisinformatika.blog.com/list-judul Cached
Implementasi
Teknologi VRML untuk Pemodelan Gerhana Matahari Menggunakan Matlab; ...
Jasa tugas akhir , skripsi , tesis teknik informatika no 1 di Indonesia
.
cutfadliyati.blogspot.com/2013/05/belajar-matlab.html Cached
Teknik
Informatika (10) Blog ... coding matlab
untuk mencari metode bisection..nie dya sya share untuk memudahkan teman2 dalam
belajar bahasa pemograman Matlab ...
simanmendrofa.blogspot.com/2013/01/profil-dan-biodata... Cached
Jan
15, 2013 · Matlab (2) Motivasi (4) Novel (5) ... Profil dan
Biodata, Serta Foto Seksi dan Cantik Lee Da Hae ... 2013 Teknik Informatika.
en.pudn.com/downloads255/sourcecode/math/detail1177695... Cached
[watermark_matlab.rar]
- digital watermarking algorithm to achieve the Matlab code debugging through
the use of Matlab, ... Database kamus Teknik Informatika
icha-naibaho.blogspot.com/...skripsi-teknik-informatika... Cached
Jan
14, 2011 · ... [Daubechies, molet, meyer] using matlab ... #
Simulasi sistem pengarsipan [dokumentasi skripsi] di Teknik Informatika
Universitas Sanata Dharma
booksreadr.org/doc/matlab-code-tabu-search Cached
Matlab Code Tabu Search downloads at Booksreadr.org - Download
free doc files,ebooks and documents - CEE 509/COM S 574 Heuristic Methods for
Optimization
www.scribd.com/doc/217914724/49649010-Tugas...
49649010
Tugas Program Perceptron Dengan Matlab - Download as PDF File (.pdf),
Text file (.txt) or read online.
juduljudulskripsi.blogspot.com/2009/...teknik-informatika...
Dec
25, 2009 · 10 Judul Skripsi Teknik Informatika tentang
Aplikasi Portal dan SMS Gateway ... 9.APLIKASI PENGENALAN WAJAH MENGGUNAKAN
FUNGSI JARAK EUCLIDEAN PADA MATLAB
simanmendrofa.blogspot.com/2013/05/contoh-aplikasi... Cached
May
09, 2013 · Contoh Aplikasi Program Algoritma Kriptografi Vigenere
Cipher dan Transposisi Menggunakan Matlab 7.4 Dengan GUI
seehow.co/numerical-integration-matlab-code-3.htm
Looking
for numerical integration matlab code? Simply read on. At last, you will
find out result for numerical integration matlab code. Get It Now!
rianfartawijaya.blogspot.com/2014/05/...matlab-r2010a.html Cached
May
10, 2014 · Oke langsung saja kita mulai melakukan penginstalan matlab
R2010a ini, dan pastikan kamu telah terlebih daulu memiki cd installer atau
software matlabnya.
www.pdfsdocuments.com/...multicast-routing-matlab-code.pdf
Title:
Aco Algorithm For Multicast Routing Matlab Code Keywords: Aco Algorithm
For Multicast Routing Matlab Code Created Date: 9/5/2014 1:22:58 PM
jasapembuatantesisilmukomputer.blogspot.com/...skripsi... Cached
Ide
Judul Tesis Skripsi Mobile Application. Make Gravity Visible : A social
movement to challenge our society to move more. Community based testing
viplab.if.its.ac.id/index.php/category/tutorial Cached
Research
Group of Vision and Image Processing "Laboratory of Vision, Image
Processing, and Graphics, Jurusan Teknik Informatika, ITS,
Surabaya"
www.pdfsdocuments.com/subtractive-clustering-matlab-code.pdf
Subtractive
Clustering Matlab Code.pdf ...
http://chosjulianto.files.wordpress.com/2012/12/free-download-skripsi-teknik-informatika.doc
Pemetaan Hierarki ...
news-taxes.rhcloud.com/read/matlab-programs-upt
Matlab programs chapter 16 161 introduction matlab stands
for matrix laboratory it is a technical ... Read more on Jurusan teknik
informatika institut teknologi ...
lecturer.d3ti.mipa.uns.ac.id/hartatik/files/2014/02/bab...
KOMPUTASI
MATEMATIKA MATHEMATICA DAN MATLAB Hartatik,M.Si dan Tim DIII TEKNIK
INFORMATIKA | FMIPA UNS 3 1.2 Memulai Program Komputasi (Mathematica dan Matlab)
skripsi-teknik-infomatika.blogspot.com/2011/02/contoh... Cached
Feb
09, 2011 · Contoh Skripsi Teknik InformatikaSkripsi Teknik
InformatikaContoh Skripsi Teknik Informatika. ... JavaScript JScript
Lingo MATLAB Perl PHP PostScript ...
booksreadr.org/doc/code-matlab-tabu-search Cached
Code
Matlab Tabu Search downloads at Booksreadr.org - Download free doc
files,ebooks and documents - CEE 509/COM S 574 Heuristic Methods for
Optimization
pdfcrop.in/ebook/title/letters-of-iqbal-to-jinnah-by... Cached
matlab mobile on ipad pdf; 2006 gmc c5500 tcm location pdf; ...
jurnal manajemen teknik informatika pdf; mr slim p5 error code pdf; next
to you pdf; is guvenligi pdf;
kuliah2008.blogspot.com/...mathworks-matlab-r2011a-full.html Cached
KULIAH
TEKNIK INFORMATIKA 2008. Beranda; Jasa; Tutorial; Source Code; ...
Mathworks Matlab R2011a ini adalah software yang digunakan untuk
menyelesaikan masalah ...
kailuccanoenoe.blogspot.com Cached
Tugas
Konsep Sistem Informasi Nama : Rizka Fajar Nugraha Kelas : TI 08 B NIM :
08.11.0723 “STIMIK AMIKOM” Purwokerto 2009 Dasar-dasar Pemrograman Matlab
www.linkedin.com/pub/mery-89/40/b3b/850
Teknik
Informatika. 2007 – 2011. View full profile.
Background. Summary. Skill IT : Pemrograman Aplikasi • Java • C#.net • C / C++
• Matlab Pemrograman Web ...
teknikinformatika-joglosmart.blogspot.com Cached
Teknik
Informatika Minggu, 25 Januari 2009. kalkulus
1. ... (Matlab) 1 (0-1) A.2.5.1021. TEKNIK KOMPILASI. 3 (3-0)
A.2.5.1022. RISET TEKNOLOGI INFORMASI. 2 (2-0) A.2 ...
ml.scribd.com/doc/252296308/Firman-Dasarmatlab Cached
Matlab merupakan bahasa pemrograman yang hadir dengan fungsi dan
... Teknik Informatika-Institut Teknologi Sepuluh Nopember Surabaya pada
tahun ...
jasatugasakhirinformatika.blogspot.com
› tesis
Feb
05, 2013 · Contoh Tugas Akhir Teknik Informatika Skripsi atau
tugas akhir (TA) merupakan salah satu syarat wajib yang harus ditempuh oleh
semua mahasiswa supaya bisa ...
www.linkedin.com/in/ilhammuhfirdaus21
Matlab; Event Planning; AutoCAD; Time Management; See 6+ See less;
Education. Universitas Teknologi Yogyakarta sarjana teknik, Teknik
Informatika 2013 – 2020 (expected)
jasaskripsiinformatika.blogspot.com
› tugas akhir
Feb
23, 2013 · Anda bisa melakukan konsultasi tentang Makalah Skripsi Teknik
Informatika melalui ... J2ME JavaScript JScript Lingo MATLAB Perl
PHP PostScript Python ...
heruprabowo23.blogspot.com Cached
Teknik
Informatika Rabu, 23 Mei 2012. ENKRIPSI
(Encryption) ... Pada MATLAB terdapat banyak sekali cara yang dapat
dilakukan untuk melakukan proses konvolusi.
www.lintasinformatika.com
› … › Ebook › Materi Kuliah
Kumpulan
Modul Belajar MatLab Lengkap Lintas Informatika - Pada mata kuliah saya
Alajabar Linier dan Data Mining, ada aplikasi yang sering digunakan oleh
mahasiswa ...
belajar-ilmu-komputer.blogspot.com/...matlab-7-pada...7.html Cached
Saya
sendiri belum begitu tahu manfaat dan fungsi dari Matlab itu sendiri,
namun saya diminta tolong oleh salah seorang mahasiswa Teknik Informatika
untuk menginstall ...
kimzetty.blogspot.com/2013/01/cara-install-matlab-7-pada... Cached
Jan
09, 2013 · Saya sendiri belum begitu tahu manfaat dan fungsi dari Matlab
itu sendiri, namun saya diminta tolong oleh salah seorang mahasiswa Teknik
Informatika untuk ...
socs.binus.ac.id/2012/05/08/matlab-and-simulink-webinar Cached
MATLAB and Simulink Webinar. May 8, 2012 ... Tutoring Kalkulus II
di SAC (Jurusan Teknik Informatika) Kamis, 16-10-14, 15.20-17.00 di
Ruang 508 BINUS University.
kailuccanoenoe.blogspot.com/2009/03/konsep-sistem-inform... Cached
Mar
30, 2009 · Tugas Konsep Sistem Informasi Nama : Rizka Fajar Nugraha
Kelas : TI 08 B NIM : 08.11.0723 “STIMIK AMIKOM” Purwokerto 2009 Dasar-dasar
Pemrograman Matlab
jurnal.darmajaya.ac.id/index.php/JurnalInformatika/... Cached
PREDIKSI
DAYA SERAP PERUSAHAAN TERHADAP ALUMNI TEKNIK INFORMATIKA IBI DARMAJAYA
... This data will be put into the prediction system used Matlab v6.0.
The key ...
jasatugasakhirinformatika.blogspot.com
› tesis
Feb
01, 2013 · Contoh Tugas Akhir Teknik Informatika S1 #5 laporan
maranatha STUDI Universitas SDN 1 maranatha AKHIR Kandaga, yang Felix blog.
UNTUK untuk Program UPN ...
jasaskripsiinformatika.blogspot.com
› Pendidikan
Feb
23, 2012 · Download Contoh Skripsi TI (Teknik Informatika)
S1JASA SKRIPSI INFORMATIKADownload Contoh Skripsi TI (Teknik Informatika)
S1
sayfudinblogz.blogspot.com Cached
Luntas
Ilmu merupakan situs tempat belajar teknik informatika atau Teknologi
informasi masa kini.Dimana ... Matriks dan vector adalah jantung dari komputasi
matlab.
ti-dasar.lab.gunadarma.ac.id Cached
Penerimaan
Asisten Baru Laboratorium Informatika ATA 2014/2015 [ NEW ] Pengumuman Asisten
Baru LABTI Kalimalang 2014 [ NEW ] Batas Akhir Pengambilan KRS Ganjil (PTA ...
informatika.stei.itb.ac.id/~rinaldi.munir/MetNum/2004...
Matlab/Maple dapat digunakan dalam dua mode: mode command line
atau mode batch file. ... Departemen Teknik Informatika Author:
Departemen Teknik Informatika
www.sitestatr.com/...teknik-informatika-menggunakan-matlab Cached
Anda
ingin download daftar judul tesis dan skripsi terbaru dan lengkap silahkan klik
download Bagi Mahasiswa yang akan menyusun skripsi tesis atau pun mendapat …
Also
Try
Tidak ada komentar:
Posting Komentar