SlideShare una empresa de Scribd logo
1 de 11
Descargar para leer sin conexión
10 Fourier Series 
10.1 Introduction 
When the French mathematician Joseph Fourier (1768-1830) was trying to 
study the flow of heat in a metal plate, he had the idea of expressing the 
heat source as an infinite series of sine and cosine functions. Although the 
original motivation was to solve the heat equation, it later became obvious 
that the same techniques could be applied to a wide array of mathematical 
and physical problems. 
In this course, we will learn how to find Fourier series to represent periodic 
functions as an infinite series of sine and cosine terms. 
A function f(x) is said to be periodic with period T, if 
f(x + T) = f(x), for all x. 
The period of the function f(t) is the interval between two successive repe-titions. 
10.2 Definition of a Fourier Series 
Let f be a bounded function defined on the [−, ] with at most a finite 
number of maxima and minima and at most a finite number of discontinuities 
in the interval. Then the Fourier series of f is the series 
f(x) = 
1 
2 
a0 + a1 cos x + a2 cos 2x + a3 cos 3x + ... 
+ b1 sin x + b2 sin 2x + b3 sin 3x + ... 
where the coefficients an and bn are given by the formulae 
a0 = 
1 
 
Z  
− 
f(x)dx 
an = 
1 
 
Z  
− 
f(x)cos(nx)dx 
bn = 
1 
 
Z  
− 
f(x)sin(nx)dx
10.3 Why the coefficients are found as they are 
We want to derive formulas for a0, an and bn. To do this we take advantage 
of some properies of sinusoidal signals. We use the following orthogonality 
conditions: 
Orthogonality conditions 
(i) The average value of cos(nx) and sin(nx) over a period is zero. 
Z  
− 
cos(nx)dx = 0 
Z  
− 
sin(nx)dx = 0 
(ii) The average value of sin(mx)cos(nx) over a period is zero. 
Z  
− 
sin(mx)cos(nx)dx = 0 
(iii) The average value of sin(mx)sin(nx) over a period, 
Z  
− 
sin(mx)sin(nx)dx = 
 
 if m = n6= 0 
0 otherwise 
(iv) The average value of cos(mx)cos(nx) over a period, 
Z  
− 
cos(mx)cos(nx)dx = 
8 
: 
2 if m = n = 0 
 if m = n6= 0 
0 if m6= n 
Remark. The following trigonometric identities are useful to prove the 
above orthogonality conditions: 
cosAcosB = 
1 
2 
[cos(A − B) + cos(A + B)] 
sinAsinB = 
1 
2 
[cos(A − B) − cos(A + B)] 
sinAcosB = 
1 
2 
[sin(A − B) + sin(A + B)]
Finding an 
We assume that we can represent the function by 
f(x) = 
1 
2 
a0 + a1 cos x + a2 cos 2x + a3 cos 3x + ... 
+ b1 sin x + b2 sin 2x + b3 sin 3x + ... (1) 
Multiply (1) by cos(nx), n  1 and integrate from − to  and assume it is 
permissible to integrate the series term by term. 
Z  
− 
f(x) cos(nx)dx = 
1 
2 
a0 
Z  
− 
cos(nx) + a1 
Z  
− 
cos x cos(nx)dx + a2 
Z  
− 
cos 2x cos(nx)dx + ... 
+ b1 
Z  
− 
sin x cos(nx)dx + b2 
Z  
− 
sin 2x cos(nx)dx + ... 
= an, because of the above orthogonality conditions 
an = 
1 
 
Z  
− 
f(x) cos(nx)dx 
Similarly if we multiply (1) by sin(nx), n  1 and integrate from − to 
, we can find the formula for the coefficient bn. To find a0, simply integrate 
(1) from − to .
10.4 Fourier Series 
Definition. Let f(x) be a 2-periodic function which is integrable on [−, ]. 
Set 
a0 = 
1 
 
Z  
− 
f(x)dx 
an = 
1 
 
Z  
− 
f(x)cos(nx)dx 
bn = 
1 
 
Z  
− 
f(x)sin(nx)dx 
then the trigonometric series 
1 
2 
a0 + 
X1 
n=1 
(ancos(nx) + bnsin(nx)) 
is called the Fourier series associated to the function f(x). 
Remark. Notice that we are not saying f(x) is equal to its Fourier Series. 
Later we will discuss conditions under which that is actually true. 
Example. Find the Fourier coefficients and the Fourier series of the square-wave 
function f defined by 
f(x) = 
 
0 if −  x  0 
1 if 0  x   
and f(x + 2) = f(x) 
Solution: So f is periodic with period 2 and its graph is:
Using the formulas for the Fourier coefficients we have 
a0 = 
1 
 
Z  
− 
f(x)dx 
= 
1 
 
Z 0 
( 
− 
0dx + 
1 
 
Z  
0 
1dx) 
= 
1 
 
() 
= 1 
an = 
1 
 
Z  
− 
f(x) cos(nx)dx 
= 
1 
 
Z 0 
( 
− 
0dx + 
1 
 
Z  
0 
cos(nx)dx) 
= 
1 
 
(0 + 
sin(nx) 
n 
 
0 ) 
= 
1 
n 
(sin n − sin 0) 
= 0 
bn = 
1 
 
Z  
− 
f(x) sin(nx)dx 
= 
1 
 
Z 0 
( 
− 
0dx + 
1 
 
Z  
0 
sin(nx)dx) 
= 
1 
 
cos(nx) 
(− 
n 
 
0 ) 
= − 
1 
n 
(cos n − cos 0) 
= − 
1 
n 
((−1)n − 1), since cos n = (−1)n. 
= 
( 
0, if n is even 
2 
, if n is odd 
n
The Fourier Series of f is therefore 
f(x) = 
1 
2 
a0 + a1 cos x + a2 cos 2x + a3 cos 3x + ... 
+ b1 sin x + b2 sin 2x + b3 sin 3x + ... 
= 
1 
2 
+ 0 + 0 + 0 + ... 
+ 
2 
 
sin x + 0 sin 2x + 
2 
3 
sin 3x + 0 sin 4x + 
2 
5 
sin 5x + ... 
= 
1 
2 
+ 
2 
 
sin x + 
2 
3 
sin 3x + 
2 
5 
sin 5x + ... 
Remark. In the above example we have found the Fourier Series of the 
square-wave function, but we don’t know yet whether this function is equal 
to its Fourier series. If we plot 
1 
2 
+ 
sin x 
2 
 
1 
2 
+ 
2 
 
sin x + 
2 
3 
sin 3x 
1 
2 
+ 
2 
 
sin x + 
2 
3 
sin 3x + 
2 
5 
sin 5x+ 
1 
2 
+ 
2 
 
sin x + 
2 
3 
sin 3x + 
2 
5 
sin 5x + 
2 
7 
sin 7x+ 
1 
2 
+ 
2 
 
sin x + 
2 
3 
sin 3x + 
2 
5 
sin 5x + 
2 
7 
sin 7x + 
2 
9 
sin 9x + 
2 
11 
sin 11x + 
2 
13 
sin 13x, 
we see that as we take more terms, we get a better approximation to the 
square-wave function. The following theorem tells us that for almost all 
points (except at the discontinuities), the Fourier series equals the function. 
Theorem (Fourier Convergence Theorem) If f is a periodic func-tion 
with period 2 and f and f0 are piecewise continuous on [−, ], then 
the Fourier series is convergent. The sum of the Fourier series is equal to 
f(x) at all numbers x where f is continuous. At the numbers x where f is 
discontinuous, the sum of the Fourier series is the average value. i.e. 
1 
2 
[f(x+) + f(x−)].
Remark. If we apply this result to the example above, the Fourier Series 
is equal to the function at all points except −, 0, . At the discontinuity 0, 
observe that 
f(0+) = 1 and f(0−) = 0 
The average value is 1/2. 
Therefore the series equals to 1/2 at the discontinuities.
10.5 Odd and even functions 
Definition. A function is said to be even if 
f(−x) = f(x) for all real numbers x. 
A function is said to be odd if 
f(−x) = −f(x) for all real numbers x. 
Example. cos x, x2, |x| are examples of even functions. sin x, x, x3 are 
examples of odd functions. The product of two even functions is even, the 
product of two odd functions is also even. The product of an even and odd 
function is odd. 
Remark. If f is an odd function then 
Z  
− 
f(x)dx = 0, 
while if f is an even function then 
Z  
− 
f(x)dx = 2 
Z  
0 
f(x)dx. 
(i) If f(x) is odd, then 
• f(x)cos(nx) is odd hence an = 0 and 
• f(x)sin(nx) is even hence bn = 2 
 
R  
0 f(x)sin(nx)dx 
(ii) If f(x) is even, then 
• f(x)cos(nx) is even hence an = 2 
 
R  
0 f(x)cos(nx)dx and 
• f(x)sin(nx) is even hence bn = 0
Example. 1. Let f be a periodic function of period 2 such that 
f(x) = x for −   x   
Find the Fourier series associated to f. 
Solution: So f is periodic with period 2 and its graph is: 
We first check if f is even or odd. 
f(−x) = −x = −f(x), so f(x) is odd. 
Therefore, 
an = 0 
bn = 
2 
 
Z  
0 
f(x)sin(nx)dx 
Using the formulas for the Fourier coefficients we have 
bn = 
2 
 
Z  
0 
x sin(nx)dx 
= 
2 
 
 
− x 
( 
cos nx 
n 
 
0 
− 
Z  
0 
(− 
cos nx 
n 
)dx) 
= 
2 
 
( 
1 
n 
[− cos n] + [ 
sin nx 
n2 ]0 
) 
= − 
2 
n 
cos n 
= 
 
−2/n if n is even 
2/n if n is odd 
The Fourier Series of f is therefore 
f(x) = b1 sin x + b2 sin 2x + b3 sin 3x + ... 
= 2 sin x − 
2 
2 
sin 2x + 
2 
3 
sin 3x − 
2 
4 
sin 4x + 
2 
5 
sin 5x + ...
2. Let f be a periodic function of period 2 such that 
f(x) = 2 − x2 for −   x   
Solution: So f is periodic with period 2 and its graph is: 
We first check if f is even or odd. 
f(−x) = 2 − (−x)2 = 2 − x2 = f(x), so f(x) is even. 
Since f is even, 
bn = 0 
an = 
2 
 
Z  
0 
f(x) cos(nx)dx
Using the formulas for the Fourier coefficients we have 
an = 
2 
 
Z  
0 
f(x) cos(nx)dx 
= 
2 
 
Z  
0 
(2 − x2) cos(nx)dx 
= 
2 
 
 
(2 − x2) 
( 
sin nx 
n 
 
0 
− 
Z  
0 
−2x 
sin nx 
n 
dx) 
= 
2 
 
([(2 − 2) 
sin n 
n 
− (2 − 0) 
sin 0 
n 
] + 
Z  
0 
2x 
sin nx 
n 
dx) 
= 
2 
 
2 
n 
Z  
0 
x sin(nx)dx 
= 
2 
 
2 
n 
 
− x 
( 
cos nx 
n 
 
0 
− 
Z  
0 
(− 
cos nx 
n 
)dx) 
= 
2 
 
2 
n 
1 
n 
([− cos n] + [ 
sin nx 
n2 ]0 
) 
= − 
4 
n2 cos n 
= 
 
−4/n2 if n is even 
4/n2 if n is odd 
It remains to calculate a0. 
a0 = 
2 
 
Z  
0 
(2 − x2)dx 
= 
2 
 
[2x − 
x3 
3 
]0 
= 
42 
3 
The Fourier Series of f is therefore 
f(x) = 
1 
2 
a0 + a1 cos x + a2 cos 2x + a3 cos 3x + ... 
b1 sin x + b2 sin 2x + b3 sin 3x + ... 
= 
22 
3 
+ 4(cos x − 
1 
4 
cos 2x + 
1 
9 
cos 3x − 
1 
16 
cos 4x + 
1 
25 
cos 5x + ....)

Más contenido relacionado

La actualidad más candente

Fourier series and its applications by md nazmul islam
Fourier series and its applications by md nazmul islamFourier series and its applications by md nazmul islam
Fourier series and its applications by md nazmul islamMd Nazmul Islam
 
Partial differential equations
Partial differential equationsPartial differential equations
Partial differential equationsaman1894
 
Partial Differentiation & Application
Partial Differentiation & Application Partial Differentiation & Application
Partial Differentiation & Application Yana Qlah
 
Partial Differential Equation - Notes
Partial Differential Equation - NotesPartial Differential Equation - Notes
Partial Differential Equation - NotesDr. Nirav Vyas
 
Partial differentiation
Partial differentiationPartial differentiation
Partial differentiationTanuj Parikh
 
Partial differentiation B tech
Partial differentiation B techPartial differentiation B tech
Partial differentiation B techRaj verma
 
Linear approximations and_differentials
Linear approximations and_differentialsLinear approximations and_differentials
Linear approximations and_differentialsTarun Gehlot
 
Limits And Derivative
Limits And DerivativeLimits And Derivative
Limits And DerivativeAshams kurian
 
CHAIN RULE AND IMPLICIT FUNCTION
CHAIN RULE AND IMPLICIT FUNCTIONCHAIN RULE AND IMPLICIT FUNCTION
CHAIN RULE AND IMPLICIT FUNCTIONNikhil Pandit
 
Newton-Raphson Method
Newton-Raphson MethodNewton-Raphson Method
Newton-Raphson MethodJigisha Dabhi
 
Continuity of functions by graph (exercises with detailed solutions)
Continuity of functions by graph   (exercises with detailed solutions)Continuity of functions by graph   (exercises with detailed solutions)
Continuity of functions by graph (exercises with detailed solutions)Tarun Gehlot
 

La actualidad más candente (20)

Chapter 2 (maths 3)
Chapter 2 (maths 3)Chapter 2 (maths 3)
Chapter 2 (maths 3)
 
Fourier series
Fourier seriesFourier series
Fourier series
 
Fourier series and its applications by md nazmul islam
Fourier series and its applications by md nazmul islamFourier series and its applications by md nazmul islam
Fourier series and its applications by md nazmul islam
 
Fourier series
Fourier series Fourier series
Fourier series
 
Chapter 4 (maths 3)
Chapter 4 (maths 3)Chapter 4 (maths 3)
Chapter 4 (maths 3)
 
Fourier series 2
Fourier series 2Fourier series 2
Fourier series 2
 
Partial differential equations
Partial differential equationsPartial differential equations
Partial differential equations
 
Jacobians new
Jacobians newJacobians new
Jacobians new
 
Partial Differentiation & Application
Partial Differentiation & Application Partial Differentiation & Application
Partial Differentiation & Application
 
Partial Differential Equation - Notes
Partial Differential Equation - NotesPartial Differential Equation - Notes
Partial Differential Equation - Notes
 
Partial differentiation
Partial differentiationPartial differentiation
Partial differentiation
 
Fourier series
Fourier seriesFourier series
Fourier series
 
Partial differentiation B tech
Partial differentiation B techPartial differentiation B tech
Partial differentiation B tech
 
Linear approximations and_differentials
Linear approximations and_differentialsLinear approximations and_differentials
Linear approximations and_differentials
 
Limits And Derivative
Limits And DerivativeLimits And Derivative
Limits And Derivative
 
CHAIN RULE AND IMPLICIT FUNCTION
CHAIN RULE AND IMPLICIT FUNCTIONCHAIN RULE AND IMPLICIT FUNCTION
CHAIN RULE AND IMPLICIT FUNCTION
 
Fourier series
Fourier seriesFourier series
Fourier series
 
Newton-Raphson Method
Newton-Raphson MethodNewton-Raphson Method
Newton-Raphson Method
 
Continuity of functions by graph (exercises with detailed solutions)
Continuity of functions by graph   (exercises with detailed solutions)Continuity of functions by graph   (exercises with detailed solutions)
Continuity of functions by graph (exercises with detailed solutions)
 
Ft3 new
Ft3 newFt3 new
Ft3 new
 

Destacado

Signal Processing Introduction using Fourier Transforms
Signal Processing Introduction using Fourier TransformsSignal Processing Introduction using Fourier Transforms
Signal Processing Introduction using Fourier TransformsArvind Devaraj
 
Introduction to Communication Systems 2
Introduction to Communication Systems 2Introduction to Communication Systems 2
Introduction to Communication Systems 2slmnsvn
 
Fourier transforms
Fourier transformsFourier transforms
Fourier transformsIffat Anjum
 
Application of fourier series
Application of fourier seriesApplication of fourier series
Application of fourier seriesGirish Dhareshwar
 
Signal & systems
Signal & systemsSignal & systems
Signal & systemsAJAL A J
 

Destacado (7)

Signal Processing Introduction using Fourier Transforms
Signal Processing Introduction using Fourier TransformsSignal Processing Introduction using Fourier Transforms
Signal Processing Introduction using Fourier Transforms
 
Introduction to Communication Systems 2
Introduction to Communication Systems 2Introduction to Communication Systems 2
Introduction to Communication Systems 2
 
Fourier transforms
Fourier transformsFourier transforms
Fourier transforms
 
Application of fourier series
Application of fourier seriesApplication of fourier series
Application of fourier series
 
Fourier series and transforms
Fourier series and transformsFourier series and transforms
Fourier series and transforms
 
Fourier transform
Fourier transformFourier transform
Fourier transform
 
Signal & systems
Signal & systemsSignal & systems
Signal & systems
 

Similar a Fourier 3

Math 1102-ch-3-lecture note Fourier Series.pdf
Math 1102-ch-3-lecture note Fourier Series.pdfMath 1102-ch-3-lecture note Fourier Series.pdf
Math 1102-ch-3-lecture note Fourier Series.pdfhabtamu292245
 
Rvtvtbthtbrvrvthrbrhtjrhrjtjrgtjtgrhrpde.ppt
Rvtvtbthtbrvrvthrbrhtjrhrjtjrgtjtgrhrpde.pptRvtvtbthtbrvrvthrbrhtjrhrjtjrgtjtgrhrpde.ppt
Rvtvtbthtbrvrvthrbrhtjrhrjtjrgtjtgrhrpde.pptshivamvadgama50
 
Interpolation techniques - Background and implementation
Interpolation techniques - Background and implementationInterpolation techniques - Background and implementation
Interpolation techniques - Background and implementationQuasar Chunawala
 
Famous problem IMO 1988 Q6.pdf
Famous problem IMO 1988 Q6.pdfFamous problem IMO 1988 Q6.pdf
Famous problem IMO 1988 Q6.pdfAbdulHannif2
 
Dynamical systems solved ex
Dynamical systems solved exDynamical systems solved ex
Dynamical systems solved exMaths Tutoring
 
Applications of Differential Calculus in real life
Applications of Differential Calculus in real life Applications of Differential Calculus in real life
Applications of Differential Calculus in real life OlooPundit
 
03 truncation errors
03 truncation errors03 truncation errors
03 truncation errorsmaheej
 
Convexity in the Theory of the Gamma Function.pdf
Convexity in the Theory of the Gamma Function.pdfConvexity in the Theory of the Gamma Function.pdf
Convexity in the Theory of the Gamma Function.pdfPeterEsnayderBarranz
 
Bahan ajar kalkulus integral
Bahan ajar kalkulus integralBahan ajar kalkulus integral
Bahan ajar kalkulus integralgrand_livina_good
 
fourierseries-121010133546-phpapp02.pdf
fourierseries-121010133546-phpapp02.pdffourierseries-121010133546-phpapp02.pdf
fourierseries-121010133546-phpapp02.pdfdiehardgamer1
 
1.1 Elementary Concepts.pdf
1.1 Elementary Concepts.pdf1.1 Elementary Concepts.pdf
1.1 Elementary Concepts.pdfd00a7ece
 
1.1 elementary concepts
1.1 elementary concepts1.1 elementary concepts
1.1 elementary conceptsd00a7ece
 

Similar a Fourier 3 (20)

Math 1102-ch-3-lecture note Fourier Series.pdf
Math 1102-ch-3-lecture note Fourier Series.pdfMath 1102-ch-3-lecture note Fourier Series.pdf
Math 1102-ch-3-lecture note Fourier Series.pdf
 
maths ppt.pdf
maths ppt.pdfmaths ppt.pdf
maths ppt.pdf
 
maths ppt.pdf
maths ppt.pdfmaths ppt.pdf
maths ppt.pdf
 
Rvtvtbthtbrvrvthrbrhtjrhrjtjrgtjtgrhrpde.ppt
Rvtvtbthtbrvrvthrbrhtjrhrjtjrgtjtgrhrpde.pptRvtvtbthtbrvrvthrbrhtjrhrjtjrgtjtgrhrpde.ppt
Rvtvtbthtbrvrvthrbrhtjrhrjtjrgtjtgrhrpde.ppt
 
Imc2017 day2-solutions
Imc2017 day2-solutionsImc2017 day2-solutions
Imc2017 day2-solutions
 
Interpolation techniques - Background and implementation
Interpolation techniques - Background and implementationInterpolation techniques - Background and implementation
Interpolation techniques - Background and implementation
 
AEM Fourier series
 AEM Fourier series AEM Fourier series
AEM Fourier series
 
Famous problem IMO 1988 Q6.pdf
Famous problem IMO 1988 Q6.pdfFamous problem IMO 1988 Q6.pdf
Famous problem IMO 1988 Q6.pdf
 
Math report
Math reportMath report
Math report
 
Dynamical systems solved ex
Dynamical systems solved exDynamical systems solved ex
Dynamical systems solved ex
 
Applications of Differential Calculus in real life
Applications of Differential Calculus in real life Applications of Differential Calculus in real life
Applications of Differential Calculus in real life
 
03 truncation errors
03 truncation errors03 truncation errors
03 truncation errors
 
Convexity in the Theory of the Gamma Function.pdf
Convexity in the Theory of the Gamma Function.pdfConvexity in the Theory of the Gamma Function.pdf
Convexity in the Theory of the Gamma Function.pdf
 
Cse41
Cse41Cse41
Cse41
 
Derivatives
DerivativesDerivatives
Derivatives
 
Bahan ajar kalkulus integral
Bahan ajar kalkulus integralBahan ajar kalkulus integral
Bahan ajar kalkulus integral
 
fourierseries-121010133546-phpapp02.pdf
fourierseries-121010133546-phpapp02.pdffourierseries-121010133546-phpapp02.pdf
fourierseries-121010133546-phpapp02.pdf
 
1.1 Elementary Concepts.pdf
1.1 Elementary Concepts.pdf1.1 Elementary Concepts.pdf
1.1 Elementary Concepts.pdf
 
1.1 elementary concepts
1.1 elementary concepts1.1 elementary concepts
1.1 elementary concepts
 
Fourier series
Fourier seriesFourier series
Fourier series
 

Último

70 POWER PLANT IAE V2500 technical training
70 POWER PLANT IAE V2500 technical training70 POWER PLANT IAE V2500 technical training
70 POWER PLANT IAE V2500 technical trainingGladiatorsKasper
 
Analysis and Evaluation of Dal Lake Biomass for Conversion to Fuel/Green fert...
Analysis and Evaluation of Dal Lake Biomass for Conversion to Fuel/Green fert...Analysis and Evaluation of Dal Lake Biomass for Conversion to Fuel/Green fert...
Analysis and Evaluation of Dal Lake Biomass for Conversion to Fuel/Green fert...arifengg7
 
Guardians of E-Commerce: Harnessing NLP and Machine Learning Approaches for A...
Guardians of E-Commerce: Harnessing NLP and Machine Learning Approaches for A...Guardians of E-Commerce: Harnessing NLP and Machine Learning Approaches for A...
Guardians of E-Commerce: Harnessing NLP and Machine Learning Approaches for A...IJAEMSJORNAL
 
Turn leadership mistakes into a better future.pptx
Turn leadership mistakes into a better future.pptxTurn leadership mistakes into a better future.pptx
Turn leadership mistakes into a better future.pptxStephen Sitton
 
US Department of Education FAFSA Week of Action
US Department of Education FAFSA Week of ActionUS Department of Education FAFSA Week of Action
US Department of Education FAFSA Week of ActionMebane Rash
 
FUNCTIONAL AND NON FUNCTIONAL REQUIREMENT
FUNCTIONAL AND NON FUNCTIONAL REQUIREMENTFUNCTIONAL AND NON FUNCTIONAL REQUIREMENT
FUNCTIONAL AND NON FUNCTIONAL REQUIREMENTSneha Padhiar
 
TEST CASE GENERATION GENERATION BLOCK BOX APPROACH
TEST CASE GENERATION GENERATION BLOCK BOX APPROACHTEST CASE GENERATION GENERATION BLOCK BOX APPROACH
TEST CASE GENERATION GENERATION BLOCK BOX APPROACHSneha Padhiar
 
CS 3251 Programming in c all unit notes pdf
CS 3251 Programming in c all unit notes pdfCS 3251 Programming in c all unit notes pdf
CS 3251 Programming in c all unit notes pdfBalamuruganV28
 
Virtual memory management in Operating System
Virtual memory management in Operating SystemVirtual memory management in Operating System
Virtual memory management in Operating SystemRashmi Bhat
 
Python Programming for basic beginners.pptx
Python Programming for basic beginners.pptxPython Programming for basic beginners.pptx
Python Programming for basic beginners.pptxmohitesoham12
 
STATE TRANSITION DIAGRAM in psoc subject
STATE TRANSITION DIAGRAM in psoc subjectSTATE TRANSITION DIAGRAM in psoc subject
STATE TRANSITION DIAGRAM in psoc subjectGayathriM270621
 
Curve setting (Basic Mine Surveying)_MI10412MI.pptx
Curve setting (Basic Mine Surveying)_MI10412MI.pptxCurve setting (Basic Mine Surveying)_MI10412MI.pptx
Curve setting (Basic Mine Surveying)_MI10412MI.pptxRomil Mishra
 
Artificial Intelligence in Power System overview
Artificial Intelligence in Power System overviewArtificial Intelligence in Power System overview
Artificial Intelligence in Power System overviewsandhya757531
 
Javier_Fernandez_CARS_workshop_presentation.pptx
Javier_Fernandez_CARS_workshop_presentation.pptxJavier_Fernandez_CARS_workshop_presentation.pptx
Javier_Fernandez_CARS_workshop_presentation.pptxJavier Fernández Muñoz
 
Comprehensive energy systems.pdf Comprehensive energy systems.pdf
Comprehensive energy systems.pdf Comprehensive energy systems.pdfComprehensive energy systems.pdf Comprehensive energy systems.pdf
Comprehensive energy systems.pdf Comprehensive energy systems.pdfalene1
 
Stork Webinar | APM Transformational planning, Tool Selection & Performance T...
Stork Webinar | APM Transformational planning, Tool Selection & Performance T...Stork Webinar | APM Transformational planning, Tool Selection & Performance T...
Stork Webinar | APM Transformational planning, Tool Selection & Performance T...Stork
 
SOFTWARE ESTIMATION COCOMO AND FP CALCULATION
SOFTWARE ESTIMATION COCOMO AND FP CALCULATIONSOFTWARE ESTIMATION COCOMO AND FP CALCULATION
SOFTWARE ESTIMATION COCOMO AND FP CALCULATIONSneha Padhiar
 
Immutable Image-Based Operating Systems - EW2024.pdf
Immutable Image-Based Operating Systems - EW2024.pdfImmutable Image-Based Operating Systems - EW2024.pdf
Immutable Image-Based Operating Systems - EW2024.pdfDrew Moseley
 
Katarzyna Lipka-Sidor - BIM School Course
Katarzyna Lipka-Sidor - BIM School CourseKatarzyna Lipka-Sidor - BIM School Course
Katarzyna Lipka-Sidor - BIM School Coursebim.edu.pl
 
Robotics-Asimov's Laws, Mechanical Subsystems, Robot Kinematics, Robot Dynami...
Robotics-Asimov's Laws, Mechanical Subsystems, Robot Kinematics, Robot Dynami...Robotics-Asimov's Laws, Mechanical Subsystems, Robot Kinematics, Robot Dynami...
Robotics-Asimov's Laws, Mechanical Subsystems, Robot Kinematics, Robot Dynami...Sumanth A
 

Último (20)

70 POWER PLANT IAE V2500 technical training
70 POWER PLANT IAE V2500 technical training70 POWER PLANT IAE V2500 technical training
70 POWER PLANT IAE V2500 technical training
 
Analysis and Evaluation of Dal Lake Biomass for Conversion to Fuel/Green fert...
Analysis and Evaluation of Dal Lake Biomass for Conversion to Fuel/Green fert...Analysis and Evaluation of Dal Lake Biomass for Conversion to Fuel/Green fert...
Analysis and Evaluation of Dal Lake Biomass for Conversion to Fuel/Green fert...
 
Guardians of E-Commerce: Harnessing NLP and Machine Learning Approaches for A...
Guardians of E-Commerce: Harnessing NLP and Machine Learning Approaches for A...Guardians of E-Commerce: Harnessing NLP and Machine Learning Approaches for A...
Guardians of E-Commerce: Harnessing NLP and Machine Learning Approaches for A...
 
Turn leadership mistakes into a better future.pptx
Turn leadership mistakes into a better future.pptxTurn leadership mistakes into a better future.pptx
Turn leadership mistakes into a better future.pptx
 
US Department of Education FAFSA Week of Action
US Department of Education FAFSA Week of ActionUS Department of Education FAFSA Week of Action
US Department of Education FAFSA Week of Action
 
FUNCTIONAL AND NON FUNCTIONAL REQUIREMENT
FUNCTIONAL AND NON FUNCTIONAL REQUIREMENTFUNCTIONAL AND NON FUNCTIONAL REQUIREMENT
FUNCTIONAL AND NON FUNCTIONAL REQUIREMENT
 
TEST CASE GENERATION GENERATION BLOCK BOX APPROACH
TEST CASE GENERATION GENERATION BLOCK BOX APPROACHTEST CASE GENERATION GENERATION BLOCK BOX APPROACH
TEST CASE GENERATION GENERATION BLOCK BOX APPROACH
 
CS 3251 Programming in c all unit notes pdf
CS 3251 Programming in c all unit notes pdfCS 3251 Programming in c all unit notes pdf
CS 3251 Programming in c all unit notes pdf
 
Virtual memory management in Operating System
Virtual memory management in Operating SystemVirtual memory management in Operating System
Virtual memory management in Operating System
 
Python Programming for basic beginners.pptx
Python Programming for basic beginners.pptxPython Programming for basic beginners.pptx
Python Programming for basic beginners.pptx
 
STATE TRANSITION DIAGRAM in psoc subject
STATE TRANSITION DIAGRAM in psoc subjectSTATE TRANSITION DIAGRAM in psoc subject
STATE TRANSITION DIAGRAM in psoc subject
 
Curve setting (Basic Mine Surveying)_MI10412MI.pptx
Curve setting (Basic Mine Surveying)_MI10412MI.pptxCurve setting (Basic Mine Surveying)_MI10412MI.pptx
Curve setting (Basic Mine Surveying)_MI10412MI.pptx
 
Artificial Intelligence in Power System overview
Artificial Intelligence in Power System overviewArtificial Intelligence in Power System overview
Artificial Intelligence in Power System overview
 
Javier_Fernandez_CARS_workshop_presentation.pptx
Javier_Fernandez_CARS_workshop_presentation.pptxJavier_Fernandez_CARS_workshop_presentation.pptx
Javier_Fernandez_CARS_workshop_presentation.pptx
 
Comprehensive energy systems.pdf Comprehensive energy systems.pdf
Comprehensive energy systems.pdf Comprehensive energy systems.pdfComprehensive energy systems.pdf Comprehensive energy systems.pdf
Comprehensive energy systems.pdf Comprehensive energy systems.pdf
 
Stork Webinar | APM Transformational planning, Tool Selection & Performance T...
Stork Webinar | APM Transformational planning, Tool Selection & Performance T...Stork Webinar | APM Transformational planning, Tool Selection & Performance T...
Stork Webinar | APM Transformational planning, Tool Selection & Performance T...
 
SOFTWARE ESTIMATION COCOMO AND FP CALCULATION
SOFTWARE ESTIMATION COCOMO AND FP CALCULATIONSOFTWARE ESTIMATION COCOMO AND FP CALCULATION
SOFTWARE ESTIMATION COCOMO AND FP CALCULATION
 
Immutable Image-Based Operating Systems - EW2024.pdf
Immutable Image-Based Operating Systems - EW2024.pdfImmutable Image-Based Operating Systems - EW2024.pdf
Immutable Image-Based Operating Systems - EW2024.pdf
 
Katarzyna Lipka-Sidor - BIM School Course
Katarzyna Lipka-Sidor - BIM School CourseKatarzyna Lipka-Sidor - BIM School Course
Katarzyna Lipka-Sidor - BIM School Course
 
Robotics-Asimov's Laws, Mechanical Subsystems, Robot Kinematics, Robot Dynami...
Robotics-Asimov's Laws, Mechanical Subsystems, Robot Kinematics, Robot Dynami...Robotics-Asimov's Laws, Mechanical Subsystems, Robot Kinematics, Robot Dynami...
Robotics-Asimov's Laws, Mechanical Subsystems, Robot Kinematics, Robot Dynami...
 

Fourier 3

  • 1. 10 Fourier Series 10.1 Introduction When the French mathematician Joseph Fourier (1768-1830) was trying to study the flow of heat in a metal plate, he had the idea of expressing the heat source as an infinite series of sine and cosine functions. Although the original motivation was to solve the heat equation, it later became obvious that the same techniques could be applied to a wide array of mathematical and physical problems. In this course, we will learn how to find Fourier series to represent periodic functions as an infinite series of sine and cosine terms. A function f(x) is said to be periodic with period T, if f(x + T) = f(x), for all x. The period of the function f(t) is the interval between two successive repe-titions. 10.2 Definition of a Fourier Series Let f be a bounded function defined on the [−, ] with at most a finite number of maxima and minima and at most a finite number of discontinuities in the interval. Then the Fourier series of f is the series f(x) = 1 2 a0 + a1 cos x + a2 cos 2x + a3 cos 3x + ... + b1 sin x + b2 sin 2x + b3 sin 3x + ... where the coefficients an and bn are given by the formulae a0 = 1 Z − f(x)dx an = 1 Z − f(x)cos(nx)dx bn = 1 Z − f(x)sin(nx)dx
  • 2. 10.3 Why the coefficients are found as they are We want to derive formulas for a0, an and bn. To do this we take advantage of some properies of sinusoidal signals. We use the following orthogonality conditions: Orthogonality conditions (i) The average value of cos(nx) and sin(nx) over a period is zero. Z − cos(nx)dx = 0 Z − sin(nx)dx = 0 (ii) The average value of sin(mx)cos(nx) over a period is zero. Z − sin(mx)cos(nx)dx = 0 (iii) The average value of sin(mx)sin(nx) over a period, Z − sin(mx)sin(nx)dx = if m = n6= 0 0 otherwise (iv) The average value of cos(mx)cos(nx) over a period, Z − cos(mx)cos(nx)dx = 8 : 2 if m = n = 0 if m = n6= 0 0 if m6= n Remark. The following trigonometric identities are useful to prove the above orthogonality conditions: cosAcosB = 1 2 [cos(A − B) + cos(A + B)] sinAsinB = 1 2 [cos(A − B) − cos(A + B)] sinAcosB = 1 2 [sin(A − B) + sin(A + B)]
  • 3. Finding an We assume that we can represent the function by f(x) = 1 2 a0 + a1 cos x + a2 cos 2x + a3 cos 3x + ... + b1 sin x + b2 sin 2x + b3 sin 3x + ... (1) Multiply (1) by cos(nx), n 1 and integrate from − to and assume it is permissible to integrate the series term by term. Z − f(x) cos(nx)dx = 1 2 a0 Z − cos(nx) + a1 Z − cos x cos(nx)dx + a2 Z − cos 2x cos(nx)dx + ... + b1 Z − sin x cos(nx)dx + b2 Z − sin 2x cos(nx)dx + ... = an, because of the above orthogonality conditions an = 1 Z − f(x) cos(nx)dx Similarly if we multiply (1) by sin(nx), n 1 and integrate from − to , we can find the formula for the coefficient bn. To find a0, simply integrate (1) from − to .
  • 4. 10.4 Fourier Series Definition. Let f(x) be a 2-periodic function which is integrable on [−, ]. Set a0 = 1 Z − f(x)dx an = 1 Z − f(x)cos(nx)dx bn = 1 Z − f(x)sin(nx)dx then the trigonometric series 1 2 a0 + X1 n=1 (ancos(nx) + bnsin(nx)) is called the Fourier series associated to the function f(x). Remark. Notice that we are not saying f(x) is equal to its Fourier Series. Later we will discuss conditions under which that is actually true. Example. Find the Fourier coefficients and the Fourier series of the square-wave function f defined by f(x) = 0 if − x 0 1 if 0 x and f(x + 2) = f(x) Solution: So f is periodic with period 2 and its graph is:
  • 5. Using the formulas for the Fourier coefficients we have a0 = 1 Z − f(x)dx = 1 Z 0 ( − 0dx + 1 Z 0 1dx) = 1 () = 1 an = 1 Z − f(x) cos(nx)dx = 1 Z 0 ( − 0dx + 1 Z 0 cos(nx)dx) = 1 (0 + sin(nx) n 0 ) = 1 n (sin n − sin 0) = 0 bn = 1 Z − f(x) sin(nx)dx = 1 Z 0 ( − 0dx + 1 Z 0 sin(nx)dx) = 1 cos(nx) (− n 0 ) = − 1 n (cos n − cos 0) = − 1 n ((−1)n − 1), since cos n = (−1)n. = ( 0, if n is even 2 , if n is odd n
  • 6. The Fourier Series of f is therefore f(x) = 1 2 a0 + a1 cos x + a2 cos 2x + a3 cos 3x + ... + b1 sin x + b2 sin 2x + b3 sin 3x + ... = 1 2 + 0 + 0 + 0 + ... + 2 sin x + 0 sin 2x + 2 3 sin 3x + 0 sin 4x + 2 5 sin 5x + ... = 1 2 + 2 sin x + 2 3 sin 3x + 2 5 sin 5x + ... Remark. In the above example we have found the Fourier Series of the square-wave function, but we don’t know yet whether this function is equal to its Fourier series. If we plot 1 2 + sin x 2 1 2 + 2 sin x + 2 3 sin 3x 1 2 + 2 sin x + 2 3 sin 3x + 2 5 sin 5x+ 1 2 + 2 sin x + 2 3 sin 3x + 2 5 sin 5x + 2 7 sin 7x+ 1 2 + 2 sin x + 2 3 sin 3x + 2 5 sin 5x + 2 7 sin 7x + 2 9 sin 9x + 2 11 sin 11x + 2 13 sin 13x, we see that as we take more terms, we get a better approximation to the square-wave function. The following theorem tells us that for almost all points (except at the discontinuities), the Fourier series equals the function. Theorem (Fourier Convergence Theorem) If f is a periodic func-tion with period 2 and f and f0 are piecewise continuous on [−, ], then the Fourier series is convergent. The sum of the Fourier series is equal to f(x) at all numbers x where f is continuous. At the numbers x where f is discontinuous, the sum of the Fourier series is the average value. i.e. 1 2 [f(x+) + f(x−)].
  • 7. Remark. If we apply this result to the example above, the Fourier Series is equal to the function at all points except −, 0, . At the discontinuity 0, observe that f(0+) = 1 and f(0−) = 0 The average value is 1/2. Therefore the series equals to 1/2 at the discontinuities.
  • 8. 10.5 Odd and even functions Definition. A function is said to be even if f(−x) = f(x) for all real numbers x. A function is said to be odd if f(−x) = −f(x) for all real numbers x. Example. cos x, x2, |x| are examples of even functions. sin x, x, x3 are examples of odd functions. The product of two even functions is even, the product of two odd functions is also even. The product of an even and odd function is odd. Remark. If f is an odd function then Z − f(x)dx = 0, while if f is an even function then Z − f(x)dx = 2 Z 0 f(x)dx. (i) If f(x) is odd, then • f(x)cos(nx) is odd hence an = 0 and • f(x)sin(nx) is even hence bn = 2 R 0 f(x)sin(nx)dx (ii) If f(x) is even, then • f(x)cos(nx) is even hence an = 2 R 0 f(x)cos(nx)dx and • f(x)sin(nx) is even hence bn = 0
  • 9. Example. 1. Let f be a periodic function of period 2 such that f(x) = x for − x Find the Fourier series associated to f. Solution: So f is periodic with period 2 and its graph is: We first check if f is even or odd. f(−x) = −x = −f(x), so f(x) is odd. Therefore, an = 0 bn = 2 Z 0 f(x)sin(nx)dx Using the formulas for the Fourier coefficients we have bn = 2 Z 0 x sin(nx)dx = 2 − x ( cos nx n 0 − Z 0 (− cos nx n )dx) = 2 ( 1 n [− cos n] + [ sin nx n2 ]0 ) = − 2 n cos n = −2/n if n is even 2/n if n is odd The Fourier Series of f is therefore f(x) = b1 sin x + b2 sin 2x + b3 sin 3x + ... = 2 sin x − 2 2 sin 2x + 2 3 sin 3x − 2 4 sin 4x + 2 5 sin 5x + ...
  • 10. 2. Let f be a periodic function of period 2 such that f(x) = 2 − x2 for − x Solution: So f is periodic with period 2 and its graph is: We first check if f is even or odd. f(−x) = 2 − (−x)2 = 2 − x2 = f(x), so f(x) is even. Since f is even, bn = 0 an = 2 Z 0 f(x) cos(nx)dx
  • 11. Using the formulas for the Fourier coefficients we have an = 2 Z 0 f(x) cos(nx)dx = 2 Z 0 (2 − x2) cos(nx)dx = 2 (2 − x2) ( sin nx n 0 − Z 0 −2x sin nx n dx) = 2 ([(2 − 2) sin n n − (2 − 0) sin 0 n ] + Z 0 2x sin nx n dx) = 2 2 n Z 0 x sin(nx)dx = 2 2 n − x ( cos nx n 0 − Z 0 (− cos nx n )dx) = 2 2 n 1 n ([− cos n] + [ sin nx n2 ]0 ) = − 4 n2 cos n = −4/n2 if n is even 4/n2 if n is odd It remains to calculate a0. a0 = 2 Z 0 (2 − x2)dx = 2 [2x − x3 3 ]0 = 42 3 The Fourier Series of f is therefore f(x) = 1 2 a0 + a1 cos x + a2 cos 2x + a3 cos 3x + ... b1 sin x + b2 sin 2x + b3 sin 3x + ... = 22 3 + 4(cos x − 1 4 cos 2x + 1 9 cos 3x − 1 16 cos 4x + 1 25 cos 5x + ....)