4. Approximation of continuous time systems with discrete time systems

Size: px
Start display at page:

Download "4. Approximation of continuous time systems with discrete time systems"

Transcription

1 4. Approximtion of continuous time systems with iscrete time systems A. heory (it helps if you re the course too) he continuous-time systems re replce by iscrete-time systems even for the processing of continuous-time signls. ) Impulse Invrince Metho: [ ] h n h n he reltion between the Z trnsform n the Lplce trnsform: kπ ( ) s e s j k he frequency response of the igitl system is the sme with the frequency response of the nlog system of limite bn for frequency less thn hlf of smpling frequency π π ( ω) ( Ω) Ω ω; ω n ω M

2 he reltion between the s n plnes σ jω s jω σ jω r e s σ + jω ; re x+ jy ; e re e e. ω Ω+ k π ) Step invrince metho: [ ] s n s n 3) he metho of the ifferentil eqution pproximtion Differentil eq: N k M k y( t) x( t) k b k k k t t k 0 k 0 Use pproximtion: y t y n y n y n y n t n t () [ ] [ ] Finite ifference eq: N k M k p p p p k k k k C y n p b C k k x n p [ ] [ ] k 0 p 0 k 0 p 0 he reltion between the Z trnsform n the Lplce trnsform: s s he reltion between the s n plnes s

3 4) Biliner rnsformtion Metho + s s + B. Problems Problem. We hve liner time-invrint system with the trnsfer function: s +. s+ ( s+ 3) ) Determine its impulse response, h ( t ) ; b) Fin the impulse response of the igitl system equivlent to the nlog one, using the impulse response invrince metho [ ], the Z trnsform of h [ n ] ; c) Fin h n ; 3

4 Solution. ) o etermine the impulse response from the trnsfer function, we ecompose it into simple frctions: A B C + + s+ s+ ( s + ) 3 s + B ( s+ ) B s s + 3 s s + C ( s+ 3) s 3 s3 4 ( s + ) s+ s+ 3s A ( s+ ) s s s s 3 s s + s + 4 ( 3) We ientify ech term using the tbles of Lplce trnsforms: ( s + ) + s+ ( s + ) s etσ 3 4 ( t) e t 4 s s + tσ X s x() t X x t t e t tx t e tx t s s ( s ) st st () () L{ ()} t t L { te σ () t } te σ t + + ( s ) () h t e t te t e t 4 4 t t 3t () σ () + σ () σ () n n 3n b) h [ n] h ( n ) e σ[ n] + ne σ[ n] e σ [ n] 4 4 c) We ientify ech term using the tbles of Z trnsforms: n n /4 e σ[ n] ( e ) σ [ n] 4 4 e n 3n 3 /4 e σ[ n] ( e ) σ [ n] e n n e ne σ[ n] n( e ) σ [ n] ( e ) 4

5 /4 e /4 ( ) + 3 e ( e ) e Problem. We hve s the trnsfer function of n nlog system, liner n timeinvrint, ( s+ )( s+ ) ) Determine its impulse response, h ( t ) ; b) Compute the impulse response for the igitl system in the cse of the impulse invrince metho. c) he step response is () s t. For the igitl system, its impulse response is h [ n ] n its step response is s [ n ]. Assume we use the impulse response invrince metho to pproximte the nlog system with igitl one: h [ n] h ( n), Verify tht the reltion: n [ ] s n h k k is true. ) Compute the step response of the igitl system, using the step response invrince metho: [ ] s n s n. Solution. ) o etermine the impulse response from the trnsfer function, we ecompose it into simple frctions n we ientify ech term using the tbles of Lplce trnsforms: A B C s+ ( s+ )( s+ ) s ( s+ ) A s+ s ( s + ) s s B ( s+ ) s s + s C s+ s s s s + s s s s ( + ) > h () t e t σ ( t) te t σ ( t) e t σ ( t) b) Impulse invrince metho he impulse response of the equivlent igitl system is:. 5

6 [ ] n [ ] n σ σ[ ] n σ [ ] h n h nt e n n e n e n c) Impulse invrince metho he step response in iscrete-time is the response to the unit step: [ ] σ[ ] [ ] [ ] σ [ ] [ ] s n n h n h k n k h k h k k k k s n h k k Which mens tht: [ ] n. he reltion is true in the generl cse. ) Step invrince metho S s be the Lplce trnsform of the step response for the nlog system: Let S s A B C D s s( s+ )( s+ ) s s+ ( s + ) s+ We compute the step response of the nlog system using the ecomposition in simple frctions n tbles of Lplce trnsforms: A ss s 0 s 0 4 ( s+ )( s+ ) B ( s+ ) S s s s s+ C ( s+ ) S s s s+ s s+ 3 D ( s+ ) S 3/4 s s s s s s s s s t t 3 t > s () t σ () t e σ () t + e σ () t + e σ () t 4 4 n n 3 n s n s n σ n e σ n + e σ n + e σ n 4 4 [ ] [ ] [ ] [ ] [ ] n Problem 3. We hve n nlog, liner time-invrint system with the impulse response h () t n igitl liner time-invrint system h [ n ] equivlent to the nlog system with the impulse response invrince metho. ) If h () t e t σ () t fin the trnsfer function of the nlog system; b) Fin the trnsfer function of the igitl system; c) Sketch the mplitue-frequency chrcteristic of the nlog system n the iscrete-time system. Solution. 6

7 { } s + ) ( s t ) L e σ ( t) n b) h [ n] h ( n) e σ [ n] ( ) c) e Ω e j e Ω + jω + ω Ω e cosω+ je sin Ω ) ( ω) ( ω) + e e cosω e cos e sin Ω + Ω For 0. the mplitue-chrcteristic re lmost the sme for frequency less thn hlf of smpling frequency π/0π3.4. (ω) (Ω) Ωω s Plot it using Mtlb. Wht o you notice when the frequency is higher thn hlf of the smpling frequency? 7

8 >> w-00*pi:0.0*pi:00*pi; >>./sqrt(+w.^); >> 0../sqrt(+exp(-0.)-*exp(-0.).*cos(0.*w)); >> plot(w,,w,); gri on Problem 4 Consier the nlog, liner time-invrint system with the ifferentil eqution: y y () t x() t t + ) Fin the impulse response n the trnsfer function of iscrete-time system tht pproximtes the nlog system using the impulse invrince metho; b) Fin gin the impulse response n the trnsfer function if we use the ifferentil eqution pproximtion; c) he sme for the biliner trnsformtion metho. ) Give implementtion forms for the systems from ), b) n c) ; e) For x() t sin t n smpling perio of 0, fin the mplitue of the output signl for the nlog system n for the igitl system, for the three cses, respectively. Solution. ) impulse invrince metho y Y + y( t) x( t) sy + Y X t X s + t n h () t e σ () t h [ n] e σ [ n] ( ) n e b) ifferentil eqution pproximtion s s n he impulse response of the igitl system is: h [ n] σ [ n] + + c) biliner trnsformtion metho s ( ) s + + s + s ( ) 8

9 ( ) + + ( + ) ( ) + ( ) + ( ) + ( ) + ( ) ( )( + ) + he impulse response of the igitl system using the biliner trnsformtion: ) [ ] [ ] h n δ n + σ n + ( ) n [ ] x - y For the nlog system y + y x x[n] e - D y[n] For the igitl system from ) b ; ; e 0 0 [ ] [ ] [ ] y n e y n x n x[n] (+) - D y[n] For the igitl system from b) b ; + ; 0 0 ( + ) y[ n] y[ n ] x[ n] y[n-] 9

10 x[n] ( + ) D D -(-) y[n] For the igitl system from c) b ; b ; 0 0 ( + ); ( ) y[ n] y[ n ] xn [ ] xn [ ] e) For x() t sin t n smpling perio of 0, we hve to fin the mplitue of the output signl, in the nlog n in the igitl cse. () sin ; () () sin + rg{ } x t t y t t ( ω) () s jω + jω + j () ; rg{ () } rg{ + j} rctg 5 y() t sin trctg, mplitue 5 5 For the igitl systems we hve: n n xn [ ] sin ( 0,n) sin yn [ ] sin rg For the igitl system from ) ( ) e Ω j cos e Ω e Ω+ j e sin Ω ( Ω ) 4 4 e cosω + e sin Ω + e e cosω 0 0,4 0, + e e cos 0 0, ( e ) 0

11 For the igitl system from b) ( ) jω + Ω + e + cos Ω jsin Ω ( Ω ) + cosω + sinω cosΩ 0, 0, 0,083 0, 44, For the igitl system from c) + + e ( ) ( Ω ) +, 0, 9e jω ( cos ) sin (,0,9cosΩ ) + ( 0,9sinΩ) jω + Ω + Ω + cosω ( Ω ), + 0,8,98cosΩ Set the vlue of Ω to 0, (homework). Problem 5. Consier continuous-time system with the impulse response h () t n the s. he ifferentil eqution is: trnsfer function N k y N k k k 0 t k 0 k x bk k t We pproximte this system using iscrete-time system using metho similr to the metho of the ifferentil eqution pproximtion: x x[ n+ ] x[ n] t We efine the ifference of t n ( 0) { [ ]} [ ] [ ] [ ] orer 0: x n x n x n+ x n orer : { xn [ ]} ( k) { [ ]} orer k: x n ) Let s ( s + ) ( k) { { x[ n] }} ( ) ; b) Wht is the connection between. Fin the trnsfer function of the igitl equivlent system? s n

12 Solution We notice tht: { xn [ ]} [ + ] xn [ ] xn { [ ] xn [ + ] xn [ ] xn} { xn [ ] } + { xn [ ]} [ + ] [ + ] [ + ] [ ] x n x n x n x n xn [ + ] xn [ + ] + xn [ ] k + k { xn l [ ]} Cxn [ l ] k l 0 k l ) We hve to fin the trnsfer function of the igitl equivlent system metho similr to the ifferentil eqution pproximtion. s bs ( s ) s s s s b 0; b ; ; ; 0 0 he liner ifferentil eqution is: y t y t x t y() t + t + t t he finite ifference eqution is: mening tht: or: [ ] { [ ]} { [ ]} { [ ]} y n + y n + y n x n [ ] [ ] [ ] [ ] y n+ y n x n+ x n yn [ ] + + yn [ ] yn [ ] yn [ ] using y[ ] + ( y[ + ] y[ ]) + y[ + ] y[ + ] + y[ ] x[ + ] x[ ] n n n n n n n n

13 Applying in both sies the Z trnsform, we obtin: Y + ( ) Y + Y Y + Y X X or: ( ) Y X ( ) + + +? b) Wht is the connection between ( s ) n ( ) mening tht: ( ) ( ) ( + ) + ( ) We cn see tht: ( ) ( s ) s 3

Approximation of continuous-time systems with discrete-time systems

Approximation of continuous-time systems with discrete-time systems Approximtion of continuou-time ytem with icrete-time ytem he continuou-time ytem re replce by icrete-time ytem even for the proceing of continuou-time ignl.. Impule invrince metho 2. Step invrince metho

More information

Note 12. Introduction to Digital Control Systems

Note 12. Introduction to Digital Control Systems Note Introduction to Digitl Control Systems Deprtment of Mechnicl Engineering, University Of Ssktchewn, 57 Cmpus Drive, Ssktoon, SK S7N 5A9, Cnd . Introduction A digitl control system is one in which the

More information

State space systems analysis (continued) Stability. A. Definitions A system is said to be Asymptotically Stable (AS) when it satisfies

State space systems analysis (continued) Stability. A. Definitions A system is said to be Asymptotically Stable (AS) when it satisfies Stte spce systems nlysis (continued) Stbility A. Definitions A system is sid to be Asymptoticlly Stble (AS) when it stisfies ut () = 0, t > 0 lim xt () 0. t A system is AS if nd only if the impulse response

More information

Jim Lambers MAT 169 Fall Semester Lecture 4 Notes

Jim Lambers MAT 169 Fall Semester Lecture 4 Notes Jim Lmbers MAT 169 Fll Semester 2009-10 Lecture 4 Notes These notes correspond to Section 8.2 in the text. Series Wht is Series? An infinte series, usully referred to simply s series, is n sum of ll of

More information

log dx a u = log a e du

log dx a u = log a e du Formuls from Trigonometry: sin A cos A = cosa ± B = cos A cos B sin A sin B sin A = sin A cos A tn A = tn A tn A sina ± B = sin A cos B ± cos A sin B tn A±tn B tna ± B = tn A tn B cos A = cos A sin A sin

More information

Math 211A Homework. Edward Burkard. = tan (2x + z)

Math 211A Homework. Edward Burkard. = tan (2x + z) Mth A Homework Ewr Burkr Eercises 5-C Eercise 8 Show tht the utonomous system: 5 Plne Autonomous Systems = e sin 3y + sin cos + e z, y = sin ( + 3y, z = tn ( + z hs n unstble criticl point t = y = z =

More information

Chapter 1. Chapter 1 1

Chapter 1. Chapter 1 1 Chpter Chpter : Signls nd Systems... 2. Introduction... 2.2 Signls... 3.2. Smpling... 4.2.2 Periodic Signls... 0.2.3 Discrete-Time Sinusoidl Signls... 2.2.4 Rel Exponentil Signls... 5.2.5 Complex Exponentil

More information

Bernoulli Numbers Jeff Morton

Bernoulli Numbers Jeff Morton Bernoulli Numbers Jeff Morton. We re interested in the opertor e t k d k t k, which is to sy k tk. Applying this to some function f E to get e t f d k k tk d k f f + d k k tk dk f, we note tht since f

More information

Instructor: Marios M. Fyrillas HOMEWORK ASSIGNMENT ON INTERPOLATION

Instructor: Marios M. Fyrillas HOMEWORK ASSIGNMENT ON INTERPOLATION AMAT 34 Numericl Methods Instructor: Mrios M. Fyrills Emil: m.fyrills@fit.c.cy Office Tel.: 34559/6 Et. 3 HOMEWORK ASSIGNMENT ON INTERPOATION QUESTION Using interpoltion by colloction-polynomil fit method

More information

Math 520 Final Exam Topic Outline Sections 1 3 (Xiao/Dumas/Liaw) Spring 2008

Math 520 Final Exam Topic Outline Sections 1 3 (Xiao/Dumas/Liaw) Spring 2008 Mth 520 Finl Exm Topic Outline Sections 1 3 (Xio/Dums/Liw) Spring 2008 The finl exm will be held on Tuesdy, My 13, 2-5pm in 117 McMilln Wht will be covered The finl exm will cover the mteril from ll of

More information

Chapter 4 Contravariance, Covariance, and Spacetime Diagrams

Chapter 4 Contravariance, Covariance, and Spacetime Diagrams Chpter 4 Contrvrince, Covrince, nd Spcetime Digrms 4. The Components of Vector in Skewed Coordintes We hve seen in Chpter 3; figure 3.9, tht in order to show inertil motion tht is consistent with the Lorentz

More information

z TRANSFORMS z Transform Basics z Transform Basics Transfer Functions Back to the Time Domain Transfer Function and Stability

z TRANSFORMS z Transform Basics z Transform Basics Transfer Functions Back to the Time Domain Transfer Function and Stability TRASFORS Trnsform Bsics Trnsfer Functions Bck to the Time Domin Trnsfer Function nd Stility DSP-G 6. Trnsform Bsics The definition of the trnsform for digitl signl is: -n X x[ n is complex vrile The trnsform

More information

Chapter Five - Eigenvalues, Eigenfunctions, and All That

Chapter Five - Eigenvalues, Eigenfunctions, and All That Chpter Five - Eigenvlues, Eigenfunctions, n All Tht The prtil ifferentil eqution methos escrie in the previous chpter is specil cse of more generl setting in which we hve n eqution of the form L 1 xux,tl

More information

f a L Most reasonable functions are continuous, as seen in the following theorem:

f a L Most reasonable functions are continuous, as seen in the following theorem: Limits Suppose f : R R. To sy lim f(x) = L x mens tht s x gets closer n closer to, then f(x) gets closer n closer to L. This suggests tht the grph of f looks like one of the following three pictures: f

More information

Jackson 2.26 Homework Problem Solution Dr. Christopher S. Baird University of Massachusetts Lowell

Jackson 2.26 Homework Problem Solution Dr. Christopher S. Baird University of Massachusetts Lowell Jckson 2.26 Homework Problem Solution Dr. Christopher S. Bird University of Msschusetts Lowell PROBLEM: The two-dimensionl region, ρ, φ β, is bounded by conducting surfces t φ =, ρ =, nd φ = β held t zero

More information

Section 6.1 INTRO to LAPLACE TRANSFORMS

Section 6.1 INTRO to LAPLACE TRANSFORMS Section 6. INTRO to LAPLACE TRANSFORMS Key terms: Improper Integrl; diverge, converge A A f(t)dt lim f(t)dt Piecewise Continuous Function; jump discontinuity Function of Exponentil Order Lplce Trnsform

More information

Linearly Similar Polynomials

Linearly Similar Polynomials Linerly Similr Polynomils rthur Holshouser 3600 Bullrd St. Chrlotte, NC, US Hrold Reiter Deprtment of Mthemticl Sciences University of North Crolin Chrlotte, Chrlotte, NC 28223, US hbreiter@uncc.edu stndrd

More information

a a a a a a a a a a a a a a a a a a a a a a a a In this section, we introduce a general formula for computing determinants.

a a a a a a a a a a a a a a a a a a a a a a a a In this section, we introduce a general formula for computing determinants. Section 9 The Lplce Expnsion In the lst section, we defined the determinnt of (3 3) mtrix A 12 to be 22 12 21 22 2231 22 12 21. In this section, we introduce generl formul for computing determinnts. Rewriting

More information

1.2. Linear Variable Coefficient Equations. y + b "! = a y + b " Remark: The case b = 0 and a non-constant can be solved with the same idea as above.

1.2. Linear Variable Coefficient Equations. y + b ! = a y + b  Remark: The case b = 0 and a non-constant can be solved with the same idea as above. 1 12 Liner Vrible Coefficient Equtions Section Objective(s): Review: Constnt Coefficient Equtions Solving Vrible Coefficient Equtions The Integrting Fctor Method The Bernoulli Eqution 121 Review: Constnt

More information

log dx a u = log a e du

log dx a u = log a e du Formuls from Trigonometry: sin A cos A = cosa ± B) = cos A cos B sin A sin B sin A = sin A cos A tn A = tn A tn A sina ± B) = sin A cos B ± cos A sin B tn A±tn B tna ± B) = tn A tn B cos A = cos A sin

More information

63. Representation of functions as power series Consider a power series. ( 1) n x 2n for all 1 < x < 1

63. Representation of functions as power series Consider a power series. ( 1) n x 2n for all 1 < x < 1 3 9. SEQUENCES AND SERIES 63. Representtion of functions s power series Consider power series x 2 + x 4 x 6 + x 8 + = ( ) n x 2n It is geometric series with q = x 2 nd therefore it converges for ll q =

More information

Here we consider the matrix transformation for a square matrix from a geometric point of view.

Here we consider the matrix transformation for a square matrix from a geometric point of view. Section. he Mgnifiction Fctor In Section.5 we iscusse the mtri trnsformtion etermine mtri A. For n m n mtri A the function f(c) = Ac provies corresponence etween vectors in R n n R m. Here we consier the

More information

Notes on the Eigenfunction Method for solving differential equations

Notes on the Eigenfunction Method for solving differential equations Notes on the Eigenfunction Metho for solving ifferentil equtions Reminer: Wereconsieringtheinfinite-imensionlHilbertspceL 2 ([, b] of ll squre-integrble functions over the intervl [, b] (ie, b f(x 2

More information

Homework Problem Set 1 Solutions

Homework Problem Set 1 Solutions Chemistry 460 Dr. Jen M. Stnr Homework Problem Set 1 Solutions 1. Determine the outcomes of operting the following opertors on the functions liste. In these functions, is constnt..) opertor: / ; function:

More information

SUPPLEMENTARY NOTES ON THE CONNECTION FORMULAE FOR THE SEMICLASSICAL APPROXIMATION

SUPPLEMENTARY NOTES ON THE CONNECTION FORMULAE FOR THE SEMICLASSICAL APPROXIMATION Physics 8.06 Apr, 2008 SUPPLEMENTARY NOTES ON THE CONNECTION FORMULAE FOR THE SEMICLASSICAL APPROXIMATION c R. L. Jffe 2002 The WKB connection formuls llow one to continue semiclssicl solutions from n

More information

1 The Riemann Integral

1 The Riemann Integral The Riemnn Integrl. An exmple leding to the notion of integrl (res) We know how to find (i.e. define) the re of rectngle (bse height), tringle ( (sum of res of tringles). But how do we find/define n re

More information

20 MATHEMATICS POLYNOMIALS

20 MATHEMATICS POLYNOMIALS 0 MATHEMATICS POLYNOMIALS.1 Introduction In Clss IX, you hve studied polynomils in one vrible nd their degrees. Recll tht if p(x) is polynomil in x, the highest power of x in p(x) is clled the degree of

More information

When a force f(t) is applied to a mass in a system, we recall that Newton s law says that. f(t) = ma = m d dt v,

When a force f(t) is applied to a mass in a system, we recall that Newton s law says that. f(t) = ma = m d dt v, Impulse Functions In mny ppliction problems, n externl force f(t) is pplied over very short period of time. For exmple, if mss in spring nd dshpot system is struck by hmmer, the ppliction of the force

More information

Calculus 2: Integration. Differentiation. Integration

Calculus 2: Integration. Differentiation. Integration Clculus 2: Integrtion The reverse process to differentition is known s integrtion. Differentition f() f () Integrtion As it is the opposite of finding the derivtive, the function obtined b integrtion is

More information

AM1 Mathematical Analysis 1 Oct Feb Exercises Lecture 3. sin(x + h) sin x h cos(x + h) cos x h

AM1 Mathematical Analysis 1 Oct Feb Exercises Lecture 3. sin(x + h) sin x h cos(x + h) cos x h AM Mthemticl Anlysis Oct. Feb. Dte: October Exercises Lecture Exercise.. If h, prove the following identities hold for ll x: sin(x + h) sin x h cos(x + h) cos x h = sin γ γ = sin γ γ cos(x + γ) (.) sin(x

More information

8 Laplace s Method and Local Limit Theorems

8 Laplace s Method and Local Limit Theorems 8 Lplce s Method nd Locl Limit Theorems 8. Fourier Anlysis in Higher DImensions Most of the theorems of Fourier nlysis tht we hve proved hve nturl generliztions to higher dimensions, nd these cn be proved

More information

The Dirac distribution

The Dirac distribution A DIRAC DISTRIBUTION A The Dirc distribution A Definition of the Dirc distribution The Dirc distribution δx cn be introduced by three equivlent wys Dirc [] defined it by reltions δx dx, δx if x The distribution

More information

any possibly dd stfdf Eds : already it state variable. xy + 7 ulliple ordinary differential equations through form that is a first-order differential

any possibly dd stfdf Eds : already it state variable. xy + 7 ulliple ordinary differential equations through form that is a first-order differential vlued Anlysis dynmicl systems : Dynmicl cn be described either systems ny ordinry differentil equtions ulliple sclr vribles in or in stte form tht is firstorder differentil dimensionl eqution vector in

More information

CHAPTER 9 BASIC CONCEPTS OF DIFFERENTIAL AND INTEGRAL CALCULUS

CHAPTER 9 BASIC CONCEPTS OF DIFFERENTIAL AND INTEGRAL CALCULUS CHAPTER 9 BASIC CONCEPTS OF DIFFERENTIAL AND INTEGRAL CALCULUS BASIC CONCEPTS OF DIFFERENTIAL AND INTEGRAL CALCULUS LEARNING OBJECTIVES After stuying this chpter, you will be ble to: Unerstn the bsics

More information

Special Relativity solved examples using an Electrical Analog Circuit

Special Relativity solved examples using an Electrical Analog Circuit 1-1-15 Specil Reltivity solved exmples using n Electricl Anlog Circuit Mourici Shchter mourici@gmil.com mourici@wll.co.il ISRAE, HOON 54-54855 Introduction In this pper, I develop simple nlog electricl

More information

How can we approximate the area of a region in the plane? What is an interpretation of the area under the graph of a velocity function?

How can we approximate the area of a region in the plane? What is an interpretation of the area under the graph of a velocity function? Mth 125 Summry Here re some thoughts I ws hving while considering wht to put on the first midterm. The core of your studying should be the ssigned homework problems: mke sure you relly understnd those

More information

Math Lecture 23

Math Lecture 23 Mth 8 - Lecture 3 Dyln Zwick Fll 3 In our lst lecture we delt with solutions to the system: x = Ax where A is n n n mtrix with n distinct eigenvlues. As promised, tody we will del with the question of

More information

1.1 Functions. 0.1 Lines. 1.2 Linear Functions. 1.3 Rates of change. 0.2 Fractions. 0.3 Rules of exponents. 1.4 Applications of Functions to Economics

1.1 Functions. 0.1 Lines. 1.2 Linear Functions. 1.3 Rates of change. 0.2 Fractions. 0.3 Rules of exponents. 1.4 Applications of Functions to Economics 0.1 Lines Definition. Here re two forms of the eqution of line: y = mx + b y = m(x x 0 ) + y 0 ( m = slope, b = y-intercept, (x 0, y 0 ) = some given point ) slope-intercept point-slope There re two importnt

More information

THE EXISTENCE-UNIQUENESS THEOREM FOR FIRST-ORDER DIFFERENTIAL EQUATIONS.

THE EXISTENCE-UNIQUENESS THEOREM FOR FIRST-ORDER DIFFERENTIAL EQUATIONS. THE EXISTENCE-UNIQUENESS THEOREM FOR FIRST-ORDER DIFFERENTIAL EQUATIONS RADON ROSBOROUGH https://intuitiveexplntionscom/picrd-lindelof-theorem/ This document is proof of the existence-uniqueness theorem

More information

MA 201: Partial Differential Equations Lecture - 12

MA 201: Partial Differential Equations Lecture - 12 Two dimensionl Lplce Eqution MA 201: Prtil Differentil Equtions Lecture - 12 The Lplce Eqution (the cnonicl elliptic eqution) Two dimensionl Lplce Eqution Two dimensionl Lplce Eqution 2 u = u xx + u yy

More information

We divide the interval [a, b] into subintervals of equal length x = b a n

We divide the interval [a, b] into subintervals of equal length x = b a n Arc Length Given curve C defined by function f(x), we wnt to find the length of this curve between nd b. We do this by using process similr to wht we did in defining the Riemnn Sum of definite integrl:

More information

lim P(t a,b) = Differentiate (1) and use the definition of the probability current, j = i (

lim P(t a,b) = Differentiate (1) and use the definition of the probability current, j = i ( PHYS851 Quntum Mechnics I, Fll 2009 HOMEWORK ASSIGNMENT 7 1. The continuity eqution: The probbility tht prticle of mss m lies on the intervl [,b] t time t is Pt,b b x ψx,t 2 1 Differentite 1 n use the

More information

Homework Assignment 5 Solution Set

Homework Assignment 5 Solution Set Homework Assignment 5 Solution Set PHYCS 44 3 Februry, 4 Problem Griffiths 3.8 The first imge chrge gurntees potentil of zero on the surfce. The secon imge chrge won t chnge the contribution to the potentil

More information

Sturm-Liouville Theory

Sturm-Liouville Theory LECTURE 1 Sturm-Liouville Theory In the two preceing lectures I emonstrte the utility of Fourier series in solving PDE/BVPs. As we ll now see, Fourier series re just the tip of the iceerg of the theory

More information

(4.1) D r v(t) ω(t, v(t))

(4.1) D r v(t) ω(t, v(t)) 1.4. Differentil inequlities. Let D r denote the right hnd derivtive of function. If ω(t, u) is sclr function of the sclrs t, u in some open connected set Ω, we sy tht function v(t), t < b, is solution

More information

Notes on length and conformal metrics

Notes on length and conformal metrics Notes on length nd conforml metrics We recll how to mesure the Eucliden distnce of n rc in the plne. Let α : [, b] R 2 be smooth (C ) rc. Tht is α(t) (x(t), y(t)) where x(t) nd y(t) re smooth rel vlued

More information

Math Fall 2006 Sample problems for the final exam: Solutions

Math Fall 2006 Sample problems for the final exam: Solutions Mth 42-5 Fll 26 Smple problems for the finl exm: Solutions Any problem my be ltered or replced by different one! Some possibly useful informtion Prsevl s equlity for the complex form of the Fourier series

More information

Definite integral. Mathematics FRDIS MENDELU

Definite integral. Mathematics FRDIS MENDELU Definite integrl Mthemtics FRDIS MENDELU Simon Fišnrová Brno 1 Motivtion - re under curve Suppose, for simplicity, tht y = f(x) is nonnegtive nd continuous function defined on [, b]. Wht is the re of the

More information

Best Approximation in the 2-norm

Best Approximation in the 2-norm Jim Lmbers MAT 77 Fll Semester 1-11 Lecture 1 Notes These notes correspond to Sections 9. nd 9.3 in the text. Best Approximtion in the -norm Suppose tht we wish to obtin function f n (x) tht is liner combintion

More information

Consequently, the temperature must be the same at each point in the cross section at x. Let:

Consequently, the temperature must be the same at each point in the cross section at x. Let: HW 2 Comments: L1-3. Derive the het eqution for n inhomogeneous rod where the therml coefficients used in the derivtion of the het eqution for homogeneous rod now become functions of position x in the

More information

Physics 116C Solution of inhomogeneous ordinary differential equations using Green s functions

Physics 116C Solution of inhomogeneous ordinary differential equations using Green s functions Physics 6C Solution of inhomogeneous ordinry differentil equtions using Green s functions Peter Young November 5, 29 Homogeneous Equtions We hve studied, especilly in long HW problem, second order liner

More information

GammaRegularized. Notations. Primary definition. Specific values. Traditional name. Traditional notation. Mathematica StandardForm notation

GammaRegularized. Notations. Primary definition. Specific values. Traditional name. Traditional notation. Mathematica StandardForm notation GmmRegulrized Nottions Trditionl nme Regulrized incomplete gmm function Trditionl nottion Q, z Mthemtic StndrdForm nottion GmmRegulrized, z Primry definition 06.08.0.000.0, z Q, z Specific vlues Specilized

More information

AQA Further Pure 2. Hyperbolic Functions. Section 2: The inverse hyperbolic functions

AQA Further Pure 2. Hyperbolic Functions. Section 2: The inverse hyperbolic functions Hperbolic Functions Section : The inverse hperbolic functions Notes nd Emples These notes contin subsections on The inverse hperbolic functions Integrtion using the inverse hperbolic functions Logrithmic

More information

4.5 JACOBI ITERATION FOR FINDING EIGENVALUES OF A REAL SYMMETRIC MATRIX. be a real symmetric matrix. ; (where we choose θ π for.

4.5 JACOBI ITERATION FOR FINDING EIGENVALUES OF A REAL SYMMETRIC MATRIX. be a real symmetric matrix. ; (where we choose θ π for. 4.5 JACOBI ITERATION FOR FINDING EIGENVALUES OF A REAL SYMMETRIC MATRIX Some reliminries: Let A be rel symmetric mtrix. Let Cos θ ; (where we choose θ π for Cos θ 4 purposes of convergence of the scheme)

More information

Definite integral. Mathematics FRDIS MENDELU. Simona Fišnarová (Mendel University) Definite integral MENDELU 1 / 30

Definite integral. Mathematics FRDIS MENDELU. Simona Fišnarová (Mendel University) Definite integral MENDELU 1 / 30 Definite integrl Mthemtics FRDIS MENDELU Simon Fišnrová (Mendel University) Definite integrl MENDELU / Motivtion - re under curve Suppose, for simplicity, tht y = f(x) is nonnegtive nd continuous function

More information

Reverse Engineering Gene Networks with Microarray Data

Reverse Engineering Gene Networks with Microarray Data Reverse Engineering Gene Networks with Microrry Dt Roert M Mllery Avisors: Dr Steve Cox n Dr Mrk Emree August 25, 2003 Astrct We consier the question of how to solve inverse prolems of the form e At x(0)

More information

Math 31S. Rumbos Fall Solutions to Assignment #16

Math 31S. Rumbos Fall Solutions to Assignment #16 Mth 31S. Rumbos Fll 2016 1 Solutions to Assignment #16 1. Logistic Growth 1. Suppose tht the growth of certin niml popultion is governed by the differentil eqution 1000 dn N dt = 100 N, (1) where N(t)

More information

ENGI 9420 Lecture Notes 7 - Fourier Series Page 7.01

ENGI 9420 Lecture Notes 7 - Fourier Series Page 7.01 ENGI 940 ecture Notes 7 - Fourier Series Pge 7.0 7. Fourier Series nd Fourier Trnsforms Fourier series hve multiple purposes, including the provision of series solutions to some liner prtil differentil

More information

Not for reproduction

Not for reproduction AREA OF A SURFACE OF REVOLUTION cut h FIGURE FIGURE πr r r l h FIGURE A surfce of revolution is formed when curve is rotted bout line. Such surfce is the lterl boundry of solid of revolution of the type

More information

APPENDIX. Precalculus Review D.1. Real Numbers and the Real Number Line

APPENDIX. Precalculus Review D.1. Real Numbers and the Real Number Line APPENDIX D Preclculus Review APPENDIX D.1 Rel Numers n the Rel Numer Line Rel Numers n the Rel Numer Line Orer n Inequlities Asolute Vlue n Distnce Rel Numers n the Rel Numer Line Rel numers cn e represente

More information

Before we can begin Ch. 3 on Radicals, we need to be familiar with perfect squares, cubes, etc. Try and do as many as you can without a calculator!!!

Before we can begin Ch. 3 on Radicals, we need to be familiar with perfect squares, cubes, etc. Try and do as many as you can without a calculator!!! Nme: Algebr II Honors Pre-Chpter Homework Before we cn begin Ch on Rdicls, we need to be fmilir with perfect squres, cubes, etc Try nd do s mny s you cn without clcultor!!! n The nth root of n n Be ble

More information

Note 16. Stokes theorem Differential Geometry, 2005

Note 16. Stokes theorem Differential Geometry, 2005 Note 16. Stokes theorem ifferentil Geometry, 2005 Stokes theorem is the centrl result in the theory of integrtion on mnifolds. It gives the reltion between exterior differentition (see Note 14) nd integrtion

More information

Conducting Ellipsoid and Circular Disk

Conducting Ellipsoid and Circular Disk 1 Problem Conducting Ellipsoid nd Circulr Disk Kirk T. McDonld Joseph Henry Lbortories, Princeton University, Princeton, NJ 08544 (September 1, 00) Show tht the surfce chrge density σ on conducting ellipsoid,

More information

Energy Bands Energy Bands and Band Gap. Phys463.nb Phenomenon

Energy Bands Energy Bands and Band Gap. Phys463.nb Phenomenon Phys463.nb 49 7 Energy Bnds Ref: textbook, Chpter 7 Q: Why re there insultors nd conductors? Q: Wht will hppen when n electron moves in crystl? In the previous chpter, we discussed free electron gses,

More information

Discrete-Time Signals and Systems. Introduction to Discrete-Time Systems. What is a Signal? What is a System? Analog and Digital Signals.

Discrete-Time Signals and Systems. Introduction to Discrete-Time Systems. What is a Signal? What is a System? Analog and Digital Signals. Discrete-Time Signls nd Systems Introduction to Discrete-Time Systems Dr. Deep Kundur University of Toronto Reference: Sections. -.4 of John G. Prokis nd Dimitris G. Mnolkis, Signl Processing: Principles,

More information

Section 6.1 INTRO to LAPLACE TRANSFORMS

Section 6.1 INTRO to LAPLACE TRANSFORMS Section 6. INTRO to LAPLACE TRANSFORMS Key terms: Improper Integrl; diverge, converge A A f(t)dt lim f(t)dt Piecewise Continuous Function; jump discontinuity Function of Exponentil Order Lplce Trnsform

More information

Summary: Method of Separation of Variables

Summary: Method of Separation of Variables Physics 246 Electricity nd Mgnetism I, Fll 26, Lecture 22 1 Summry: Method of Seprtion of Vribles 1. Seprtion of Vribles in Crtesin Coordintes 2. Fourier Series Suggested Reding: Griffiths: Chpter 3, Section

More information

g i fφdx dx = x i i=1 is a Hilbert space. We shall, henceforth, abuse notation and write g i f(x) = f

g i fφdx dx = x i i=1 is a Hilbert space. We shall, henceforth, abuse notation and write g i f(x) = f 1. Appliction of functionl nlysis to PEs 1.1. Introduction. In this section we give little introduction to prtil differentil equtions. In prticulr we consider the problem u(x) = f(x) x, u(x) = x (1) where

More information

SOLUTION OF QUADRATIC NONLINEAR PROBLEMS WITH MULTIPLE SCALES LINDSTEDT-POINCARE METHOD. Mehmet Pakdemirli and Gözde Sarı

SOLUTION OF QUADRATIC NONLINEAR PROBLEMS WITH MULTIPLE SCALES LINDSTEDT-POINCARE METHOD. Mehmet Pakdemirli and Gözde Sarı Mthemticl nd Computtionl Applictions, Vol., No., pp. 37-5, 5 http://dx.doi.org/.99/mc-5- SOLUTION OF QUADRATIC NONLINEAR PROBLEMS WITH MULTIPLE SCALES LINDSTEDT-POINCARE METHOD Mehmet Pkdemirli nd Gözde

More information

B.Sc. in Mathematics (Ordinary)

B.Sc. in Mathematics (Ordinary) R48/0 DUBLIN INSTITUTE OF TECHNOLOGY KEVIN STREET, DUBLIN 8 B.Sc. in Mthemtics (Ordinry) SUPPLEMENTAL EXAMINATIONS 01 Numericl Methods Dr. D. Mckey Dr. C. Hills Dr. E.A. Cox Full mrks for complete nswers

More information

Physical Properties as Tensors

Physical Properties as Tensors Phsicl Proerties s Tensors Proerties re either isotroic or nisotroic. Consier roert such s the ielectric suscetibilit, tht reltes the olrition (P) cuse b n electric fiel () in ielectric mteril. In isotroic

More information

Math Advanced Calculus II

Math Advanced Calculus II Mth 452 - Advnced Clculus II Line Integrls nd Green s Theorem The min gol of this chpter is to prove Stoke s theorem, which is the multivrible version of the fundmentl theorem of clculus. We will be focused

More information

ARITHMETIC OPERATIONS. The real numbers have the following properties: a b c ab ac

ARITHMETIC OPERATIONS. The real numbers have the following properties: a b c ab ac REVIEW OF ALGEBRA Here we review the bsic rules nd procedures of lgebr tht you need to know in order to be successful in clculus. ARITHMETIC OPERATIONS The rel numbers hve the following properties: b b

More information

Example Sheet 6. Infinite and Improper Integrals

Example Sheet 6. Infinite and Improper Integrals Sivkumr Exmple Sheet 6 Infinite nd Improper Integrls MATH 5H Mteril presented here is extrcted from Stewrt s text s well s from R. G. Brtle s The elements of rel nlysis. Infinite Integrls: These integrls

More information

1. Extend QR downwards to meet the x-axis at U(6, 0). y

1. Extend QR downwards to meet the x-axis at U(6, 0). y In the digrm, two stright lines re to be drwn through so tht the lines divide the figure OPQRST into pieces of equl re Find the sum of the slopes of the lines R(6, ) S(, ) T(, 0) Determine ll liner functions

More information

Matrix & Vector Basic Linear Algebra & Calculus

Matrix & Vector Basic Linear Algebra & Calculus Mtrix & Vector Bsic Liner lgebr & lculus Wht is mtrix? rectngulr rry of numbers (we will concentrte on rel numbers). nxm mtrix hs n rows n m columns M x4 M M M M M M M M M M M M 4 4 4 First row Secon row

More information

MATHEMATICAL METHODS (CAS) Written Examination 1

MATHEMATICAL METHODS (CAS) Written Examination 1 The Mthemticl Assocition of Victori Tril Exm 011 MATHEMATICAL METHODS (CAS) STUDENT NAME Written Exmintion 1 Reing time: 15 minutes Writing time: 1 hour QUESTION AND ANSWER BOOK Structure of book Number

More information

Orthogonal Polynomials

Orthogonal Polynomials Mth 4401 Gussin Qudrture Pge 1 Orthogonl Polynomils Orthogonl polynomils rise from series solutions to differentil equtions, lthough they cn be rrived t in vriety of different mnners. Orthogonl polynomils

More information

ME 501A Seminar in Engineering Analysis Page 1

ME 501A Seminar in Engineering Analysis Page 1 Phse-plne Anlsis of Ordinr November, 7 Phse-plne Anlsis of Ordinr Lrr Cretto Mechnicl Engineering 5A Seminr in Engineering Anlsis November, 7 Outline Mierm exm two weeks from tonight covering ODEs nd Lplce

More information

2. VECTORS AND MATRICES IN 3 DIMENSIONS

2. VECTORS AND MATRICES IN 3 DIMENSIONS 2 VECTORS AND MATRICES IN 3 DIMENSIONS 21 Extending the Theory of 2-dimensionl Vectors x A point in 3-dimensionl spce cn e represented y column vector of the form y z z-xis y-xis z x y x-xis Most of the

More information

Plates on elastic foundation

Plates on elastic foundation Pltes on elstic foundtion Circulr elstic plte, xil-symmetric lod, Winkler soil (fter Timoshenko & Woinowsky-Krieger (1959) - Chpter 8) Prepred by Enzo Mrtinelli Drft version ( April 016) Introduction Winkler

More information

( ) Same as above but m = f x = f x - symmetric to y-axis. find where f ( x) Relative: Find where f ( x) x a + lim exists ( lim f exists.

( ) Same as above but m = f x = f x - symmetric to y-axis. find where f ( x) Relative: Find where f ( x) x a + lim exists ( lim f exists. AP Clculus Finl Review Sheet solutions When you see the words This is wht you think of doing Find the zeros Set function =, fctor or use qudrtic eqution if qudrtic, grph to find zeros on clcultor Find

More information

I1 = I2 I1 = I2 + I3 I1 + I2 = I3 + I4 I 3

I1 = I2 I1 = I2 + I3 I1 + I2 = I3 + I4 I 3 2 The Prllel Circuit Electric Circuits: Figure 2- elow show ttery nd multiple resistors rrnged in prllel. Ech resistor receives portion of the current from the ttery sed on its resistnce. The split is

More information

ENGI 3424 Engineering Mathematics Five Tutorial Examples of Partial Fractions

ENGI 3424 Engineering Mathematics Five Tutorial Examples of Partial Fractions ENGI 44 Engineering Mthemtics Five Tutoril Exmples o Prtil Frctions 1. Express x in prtil rctions: x 4 x 4 x 4 b x x x x Both denomintors re liner non-repeted ctors. The cover-up rule my be used: 4 4 4

More information

Czechoslovak Mathematical Journal, 55 (130) (2005), , Abbotsford. 1. Introduction

Czechoslovak Mathematical Journal, 55 (130) (2005), , Abbotsford. 1. Introduction Czechoslovk Mthemticl Journl, 55 (130) (2005), 933 940 ESTIMATES OF THE REMAINDER IN TAYLOR S THEOREM USING THE HENSTOCK-KURZWEIL INTEGRAL, Abbotsford (Received Jnury 22, 2003) Abstrct. When rel-vlued

More information

Data Structures and Algorithms CMPSC 465

Data Structures and Algorithms CMPSC 465 Dt Structures nd Algorithms CMPSC 465 LECTURE 10 Solving recurrences Mster theorem Adm Smith S. Rskhodnikov nd A. Smith; bsed on slides by E. Demine nd C. Leiserson Review questions Guess the solution

More information

18.06 Problem Set 4 Due Wednesday, Oct. 11, 2006 at 4:00 p.m. in 2-106

18.06 Problem Set 4 Due Wednesday, Oct. 11, 2006 at 4:00 p.m. in 2-106 8. Problem Set Due Wenesy, Ot., t : p.m. in - Problem Mony / Consier the eight vetors 5, 5, 5,..., () List ll of the one-element, linerly epenent sets forme from these. (b) Wht re the two-element, linerly

More information

Partial Differential Equations

Partial Differential Equations Prtil Differentil Equtions Notes by Robert Piché, Tmpere University of Technology reen s Functions. reen s Function for One-Dimensionl Eqution The reen s function provides complete solution to boundry

More information

Appendix to Notes 8 (a)

Appendix to Notes 8 (a) Appendix to Notes 8 () 13 Comprison of the Riemnn nd Lebesgue integrls. Recll Let f : [, b] R be bounded. Let D be prtition of [, b] such tht Let D = { = x 0 < x 1

More information

The problems that follow illustrate the methods covered in class. They are typical of the types of problems that will be on the tests.

The problems that follow illustrate the methods covered in class. They are typical of the types of problems that will be on the tests. ADVANCED CALCULUS PRACTICE PROBLEMS JAMES KEESLING The problems tht follow illustrte the methods covered in clss. They re typicl of the types of problems tht will be on the tests. 1. Riemnn Integrtion

More information

Analytical Methods Exam: Preparatory Exercises

Analytical Methods Exam: Preparatory Exercises Anlyticl Methods Exm: Preprtory Exercises Question. Wht does it men tht (X, F, µ) is mesure spce? Show tht µ is monotone, tht is: if E F re mesurble sets then µ(e) µ(f). Question. Discuss if ech of the

More information

1 Linear Least Squares

1 Linear Least Squares Lest Squres Pge 1 1 Liner Lest Squres I will try to be consistent in nottion, with n being the number of dt points, nd m < n being the number of prmeters in model function. We re interested in solving

More information

Families of Solutions to Bernoulli ODEs

Families of Solutions to Bernoulli ODEs In the fmily of solutions to the differentil eqution y ry dx + = it is shown tht vrition of the initil condition y( 0 = cuses horizontl shift in the solution curve y = f ( x, rther thn the verticl shift

More information

Section 6.4 Graphs of the sine and cosine functions

Section 6.4 Graphs of the sine and cosine functions Section 6. Grphs of the sine nd cosine functions This is the grph of the sine function f() sin f() sin Domin All rel numbers (, ) Rnge [ 1,1] Amplitute 1 Period π This sine function hs Period of π mens

More information

Abstract inner product spaces

Abstract inner product spaces WEEK 4 Abstrct inner product spces Definition An inner product spce is vector spce V over the rel field R equipped with rule for multiplying vectors, such tht the product of two vectors is sclr, nd the

More information

STEP FUNCTIONS, DELTA FUNCTIONS, AND THE VARIATION OF PARAMETERS FORMULA. 0 if t < 0, 1 if t > 0.

STEP FUNCTIONS, DELTA FUNCTIONS, AND THE VARIATION OF PARAMETERS FORMULA. 0 if t < 0, 1 if t > 0. STEP FUNCTIONS, DELTA FUNCTIONS, AND THE VARIATION OF PARAMETERS FORMULA STEPHEN SCHECTER. The unit step function nd piecewise continuous functions The Heviside unit step function u(t) is given by if t

More information

Chapter 6. Infinite series

Chapter 6. Infinite series Chpter 6 Infinite series We briefly review this chpter in order to study series of functions in chpter 7. We cover from the beginning to Theorem 6.7 in the text excluding Theorem 6.6 nd Rbbe s test (Theorem

More information

Lecture 17. Integration: Gauss Quadrature. David Semeraro. University of Illinois at Urbana-Champaign. March 20, 2014

Lecture 17. Integration: Gauss Quadrature. David Semeraro. University of Illinois at Urbana-Champaign. March 20, 2014 Lecture 17 Integrtion: Guss Qudrture Dvid Semerro University of Illinois t Urbn-Chmpign Mrch 0, 014 Dvid Semerro (NCSA) CS 57 Mrch 0, 014 1 / 9 Tody: Objectives identify the most widely used qudrture method

More information

Calculus of Variations

Calculus of Variations Clculus of Vritions Com S 477/577 Notes) Yn-Bin Ji Dec 4, 2017 1 Introduction A functionl ssigns rel number to ech function or curve) in some clss. One might sy tht functionl is function of nother function

More information

The Solution of Volterra Integral Equation of the Second Kind by Using the Elzaki Transform

The Solution of Volterra Integral Equation of the Second Kind by Using the Elzaki Transform Applied Mthemticl Sciences, Vol. 8, 214, no. 11, 525-53 HIKARI Ltd, www.m-hikri.com http://dx.doi.org/1.12988/ms.214.312715 The Solution of Volterr Integrl Eqution of the Second Kind by Using the Elzki

More information