1it is said to be overdamped. When 1, the roots of

Size: px
Start display at page:

Download "1it is said to be overdamped. When 1, the roots of"

Transcription

1 Homework 3 AERE573 Fall 8 Due /8(M) SOLUTIO PROBLEM (4pt) Coider a D order uderdamped tem trafer fuctio H( ) ratio The deomiator i the tem characteritic polomial P( ) (a)(5pt) Ue the quadratic formula, to how that the root of thi polomial are: id where d for ( ) 4, ( i ( g with dampig () i Remark The frequec d i called the tem damped atural frequec, ad the frequec i called the tem udamped atural frequec The parameter i called the tem dampig ratio Whe the tem i aid to be criticall damped, ad whe it i aid to be overdamped Whe, the root of P( ) will be egative real umber I thi cae, the tem will exhibit o atural ocillatio Moreover, the value of become meaigle It i ol for that the umerical value of ha meaig I thi cae, the d atural ocillatio of the tem will occur at The will ol occur at ( ) o phical tem ha, for, if it did, it would be a perpetual motio tem Hece, i the phical world oe will ever oberve Clearl though, for, oe ca get a prett good idea of what it i d whe the tem ha zero dampig (b)(pt) Shade the regio i the left upper quadrat of complex plae (ie the -plae) i which the root id will lie uder each of the followig cotrait: [OTE: Be ure to iclude the mbolic value o the appropriate axi ad jutif our plot with math] (i) d (ii) (iii) (iv) i i i i / / Figure b(i) d Figure b(ii) Figure (b)(iii) Figure (b)(iv) Jutificatio: (i) cotat imagiar part ; (ii) cotat real part ; (iii) otig that ( ) (, it follow that co, or co (iv) From (iii) we foud that

2 (c)(5pt) Recall that H ( i) g 4 H( ir) The ue thi to how that r r ( ) H( ir) g g 4 Settig ( r ) i( r) r r ( ) d( f / g) / dx [ f ' g fg ']/ g f g i the tem Frequec Repoe Fuctio (FRF) Let H ( ir) r / Show that i a maximum whe d H ( ir) / dr ad uig the calculu reult, if we et thi equal to zero, the we have ' ' ad 4 f g fg Applig thi to re H ( ir) 4 3 g r r ( ) give: g d[ r r ( )]/ dr 4r 4 r( ) r ( ) Hece, r /, or with Remark The frequec re i called the tem reoat frequec It i ot the damped atural frequec Hece, while i the time domai we ee atural ocillatio occurrig at peak magitude at re d d, i the frequec domai we ee ol the (d)(5pt) Recall that for a iput u( t) u i( t), the tead tate output will be ( t) um ( )i[ t ( )], where M ( ) H( i) ad ( ) arg[ Hi ( )] Ue thi to explai wh the parameter i called the tem tatic gai Explaatio: For it i clear that M() g I thi cae, the iput i the cotat u() t u (ie it i tatic) Hece, the tead tate repoe ( t) um () i alo cotat, or tatic The ratio of cotat output to the cotat iput i therefore Hece, at frequecie cloe to zero the gai of the FRF i g Whe the frequec of the iput i exactl zero, the iput i M() g Thi i wh i called the tem tatic gai g g (e)(5pt) ow coider that cae where r / H ( i r) H ( ir) 4 (ie at high frequecie Defie H ( ir) log H ( ir) H ( ir) ha a lope of -4/decade) g H( ir) log H( ir) log 4 Hece, for r, r r ( ) g 4 H( ir) log 4 log g log r log g 4log r Similarl, r 4 Show that H ( i r) log g log ( r) log g 4log r 4log H( i r) 4

3 3 PROBLEM (4pt) Coider the followig ecod order tem: (a)(5pt) Ue the Matlab commad impule to obtai a plot of the tem impule repoe, Alo, obtai the amplig iterval, T, that wa ued [See (a)] The amplig time ued wa h(t) T 46ec Amplitude 5 5 H ( ) () 5 Impule Reoe from (a) (blue),from (b) (red) & Correlatio Fc (black) Time (ecod) Figure (a): (a) blue, (b) red, ad (g) correlatio black (b)(5pt) (i) Ue the table of Laplace traform that i poted i the Lecture Summar folder to obtai the expreio for h (t) (ii) Overla a plot of it o our plot i (a) ad (iii) commet (i) H( ) From 5 ( ) d ( ) 4 we obtai: t h( t) e co( d t) i(t ) (ii) The plot (red) i i Figure (a) (iii)the are exactl the ame Bode Diagram (c)(5pt) (i)ue the Matlab commad bode to obtai a plot of the tem frequec repoe fuctio (FRF) (ii) Take the maximum frequec to be the quit frequec, ad fid the correpodig amplig iterval, T (iii)compare thi value to the amplig period ued to obtai our plot i (a) (i) [See (c)] (ii) r / f 595Hz f 383Hz T 34ec (iii) Thi ample iterval i about /3 of that ued i (a) (d)(5pt) To imulate a radom proce, a white oie proce S ( ) H( i) u(t) (t) Magitude () Phae (deg) Frequec (rad/) Figure (c) FRF plot that ha a pd with the hape of the FRF magitude i (c), we will imulate Ue the geeral Wieer-Kichie relatio S x ( ) E[ X ( i) ] to how that c, where c i the (badlimited) white oie pd value S ( ) E[ Y ( i) ] E[ H( i) U( i) ] H( i) E[ U( i) ] H( i) S ( ) H( i) c (e)(pt) Recall R () S ( d ) c So, from (d): H( ) d (i) Ue the Matlab commad itegral to obtai the umerical value of H ( ) From thi value ad the badwidth iformatio fid the value for u d (ii)for thi value ad with, fid the umerical value for c (iii) [See (e)] (i) H( ) d 5 (ii) 5c c 95 (iii) c( f ) cf 95(383) 66 u amp u

4 (f)(5pt) (i)ue the Matlab commad lim to imulate the tem repoe to a white oie iput with the variace ou foud i (e) Your imulatio hould tart with 5 +5 poit Dicard the firt 5 poit, ad plot the remaiig data (ii)commet o whether the rage of value eem to be correct baed o the rage 4 [See (f)] (i) See plot at right Sice, Thi matche the rage how Partial Realizatio of w (t) Figure (f) Partial realizatio x 4 4 (g)(5pt)(i)ue the Matlab commad xcorr to obtai a ubiaed etimate of { R ( ) ; 7ec} ad overla it o Figure (a) (ii) Commet [See (g)(i)it i the plot i black (ii)the correlatio fuctio i ver imilar to the impule repoe

5 5 PROBLEM 3(pt) Coider a trafer fuctio (a)(5pt) For a uit tep impule, f ( t) ( t) t H ( ) From etr 4 i the poted table: h( t) e, recall that from the covolutio itegral, h ( t) h( ) ( t ) d For a itegratio time tep, approximate thi itegral uig our expreio for h(t) i (a) Approximate the uit impule b ( k) / for k Show that the expreio for the ummatio lead to h( ) ( e ) C ( k) ( ) h( ) e [( k) / ] e ( e ) k (b)(5pt) Deote h( k) h The the z-traform of the equece k { h k } k x I cla, the followig power erie relatio wa proved: x x relatio to how that the z-traform of k k k k k C H( z) hk z C z C ( z ) z (c)(5pt) Recall that the fuctio k k k z z ) e i defied a Thu, for k H ( z) h k z k x, k C h( ) C i H ( z) for z-value that atif z z e, ad o for i, we have i ( i periodic, with frequec / x k Ue thi lat x z z e i It hould be clear that, a a fuctio of frequec, rad/ec It follow that we eed ol coider frequecie i the iterval [ /, / ] The frequec / rad/ec i called the quit frequec ow, for a ufficietl low frequec,, we have H( i ), which i the tatic gai of H () For the ame we have i i C z ( ) e Hece, for thi, we have H( e i ), ad o the tatic gai of the ampled tem H (z) i ot (or ) Ad o, coider the caled dicrete tem FRF H( z) H( z) For ec write our ow C Matlab code (do ot ue a of Matlab code) to compute ad plot the magitude (i ) ad the phae (i degree) of i the dicrete Frequec Repoe Fuctio (FRF) H ( e ) for radial frequecie i the iterval [ /, / ] Alo, ue the emilogx commad o that our frequec axi i paced logarithmicall, ad ue a frequec tep ize of [Place our code i a Appedix at the ed of thi homework] [See 3(e)] Scaled H(z) & H()FRF Magitude Scaled H(z)& H()FRF Phae Degree -5-3 i Figure 3(e) FRF for H ( e ) (blue) ad for H ( i) (red)

6 (d)(5pt) I would hope that it bother ou that we impl applied a fudge factor to make thig work out i (c) It hould! Rather tha jut applig a fudge factor, i thi part ou will tr to dicover that omethig wa miig i (d) To thi ed, begi b recallig the defiitio: t H( ) h( ) e d Approximate thi itegral a a Riema um, call it The expre it i term of the variable H ( z) k h k z k You hould dicover that compare it to the value of the fudge factor H( ) z e Call thi H (z) H (z) differ from ( ) / C H () Fiall, compare thi to the defiitio of the z-traform H (z) b a cotat Compute the value of thi cotat ad t ( k) k h( ) e d h( k) e h z H ( z) Hece, H (z) i (c) i miig 34 [It wa k k k caceled out b the approximate delta fuctio] The fudge factor we ued i (e) wa 97 3 Thee C are almot idetical 6

7 7 Appedix %PROGRAM AME: hw3m 9/3/8 %PROBLEM : %(a): =[ ]; d=[ 5]; H=tf(,d); [h,t]=impule(h); figure() impule(h) T=t()-t() %(b): r=root(d); wd=ab(imag(r())); tau=-real(r()); hh=exp(-t)*(*co(wd*t)-(/wd)*i(wd*t)); hold o plot(t,hh,'r','liewidth',) title('impule Reoe from (a) (blue) & from (b) (red') %(c): figure() bode(h); wmax=;%thi wa obtaied from the Bode plot famp=wmax/pi; T=/famp %(e): Fid total area uder Hw) ^: H =@(w) (ab(((()*w*i+())/(d(3)*(i*w)^+d()*i*w+d()))))^; RH=*itegral(H,,wmax)/(*pi) vary=; c=vary/rh %white oie pd value varu=c*famp % PART (f): = ^5; u=ormrd(,qrt(varu),+5,); t=:t:(+5-)*t; t=t'; =lim(u,h,t); t=t(:); =(5:+5); figure() plot(t,) title('partial Realizatio of w (t)') %(g) maxlag = fix(7/t); rhat=xcorr(,maxlag,'ubiaed'); rhat=rhat(maxlag+:*maxlag+); tau=t*(:maxlag); tau=tau'; figure() plot(tau,rhat,'k--','liewidth',) title('impule Reoe from (a) (blue),from (b) (red) & Correlatio Fc (black)') %========================================================== % PROBLEM 3: %(e) C = ; alpha = 97; del = *pi; dw = ; w = *pi/del:dw:pi/del; z = exp(w*del*i); H = C*(-alpha*z^-)^-; HH = ((-alpha)/c)*h; M = *log(ab(hh)); Ph = (8/pi)*agle(HH); figure(3) ubplot(,,), emilogx(w,m)

8 label('') title('scaled H(z)FRF Magitude') ubplot(,,), emilogx(w,ph) label('degree') title('scaled H(z)FRF Phae') %(g) % The cotiuou-time tem FRF =tf(,[ ]); [Mag,Phae]=bode(,w); lw = legth(w); Mag = rehape(mag,,lw); Phae = rehape(phae,,lw); Mag = *log(mag); figure(3) ubplot(,,), emilogx(w,m) hold o ubplot(,,), emilogx(w,mag,'r') label('') title('scaled H(z) & H()FRF Magitude') ubplot(,,), emilogx(w,ph) hold o ubplot(,,), emilogx(w,phae,'r') label('degree') title('scaled H(z)& H()FRF Phae') 8

1the 1it is said to be overdamped. When 1, the roots of

1the 1it is said to be overdamped. When 1, the roots of Homework 3 AERE573 Fall 08 Due 0/8(M) ame PROBLEM (40pts) Cosider a D order uderdamped system trasfer fuctio H( s) s ratio 0 The deomiator is the system characteristic polyomial P( s) s s (a)(5pts) Use

More information

State space systems analysis

State space systems analysis State pace ytem aalyi Repreetatio of a ytem i tate-pace (tate-pace model of a ytem To itroduce the tate pace formalim let u tart with a eample i which the ytem i dicuio i a imple electrical circuit with

More information

CONTROL SYSTEMS. Chapter 7 : Bode Plot. 40dB/dec 1.0. db/dec so resultant slope will be 20 db/dec and this is due to the factor s

CONTROL SYSTEMS. Chapter 7 : Bode Plot. 40dB/dec 1.0. db/dec so resultant slope will be 20 db/dec and this is due to the factor s CONTROL SYSTEMS Chapter 7 : Bode Plot GATE Objective & Numerical Type Solutio Quetio 6 [Practice Book] [GATE EE 999 IIT-Bombay : 5 Mark] The aymptotic Bode plot of the miimum phae ope-loop trafer fuctio

More information

Brief Review of Linear System Theory

Brief Review of Linear System Theory Brief Review of Liear Sytem heory he followig iformatio i typically covered i a coure o liear ytem theory. At ISU, EE 577 i oe uch coure ad i highly recommeded for power ytem egieerig tudet. We have developed

More information

Lecture 30: Frequency Response of Second-Order Systems

Lecture 30: Frequency Response of Second-Order Systems Lecture 3: Frequecy Repoe of Secod-Order Sytem UHTXHQF\ 5HVSRQVH RI 6HFRQGUGHU 6\VWHPV A geeral ecod-order ytem ha a trafer fuctio of the form b + b + b H (. (9.4 a + a + a It ca be table, utable, caual

More information

ELEC 372 LECTURE NOTES, WEEK 4 Dr. Amir G. Aghdam Concordia University

ELEC 372 LECTURE NOTES, WEEK 4 Dr. Amir G. Aghdam Concordia University ELEC 37 LECTURE NOTES, WEE 4 Dr Amir G Aghdam Cocordia Uiverity Part of thee ote are adapted from the material i the followig referece: Moder Cotrol Sytem by Richard C Dorf ad Robert H Bihop, Pretice Hall

More information

EECE 301 Signals & Systems Prof. Mark Fowler

EECE 301 Signals & Systems Prof. Mark Fowler EECE 30 Sigal & Sytem Prof. Mark Fowler Note Set #8 C-T Sytem: Laplace Traform Solvig Differetial Equatio Readig Aigmet: Sectio 6.4 of Kame ad Heck / Coure Flow Diagram The arrow here how coceptual flow

More information

a 1 = 1 a a a a n n s f() s = Σ log a 1 + a a n log n sup log a n+1 + a n+2 + a n+3 log n sup () s = an /n s s = + t i

a 1 = 1 a a a a n n s f() s = Σ log a 1 + a a n log n sup log a n+1 + a n+2 + a n+3 log n sup () s = an /n s s = + t i 0 Dirichlet Serie & Logarithmic Power Serie. Defiitio & Theorem Defiitio.. (Ordiary Dirichlet Serie) Whe,a,,3, are complex umber, we call the followig Ordiary Dirichlet Serie. f() a a a a 3 3 a 4 4 Note

More information

u t u 0 ( 7) Intuitively, the maximum principles can be explained by the following observation. Recall

u t u 0 ( 7) Intuitively, the maximum principles can be explained by the following observation. Recall Oct. Heat Equatio M aximum priciple I thi lecture we will dicu the maximum priciple ad uiquee of olutio for the heat equatio.. Maximum priciple. The heat equatio alo ejoy maximum priciple a the Laplace

More information

Homework 5 STAT 305B Fall 2018 Due 11/9(R) Name

Homework 5 STAT 305B Fall 2018 Due 11/9(R) Name 1 Homewor 5 STAT 305B Fall 018 Due 11/9(R) Name PROBLEM 1(5pts) Throughout the remaider of the course we will regularly ecouter the terms 1/ ad 1/ ( 1). I this problem we will edeavor to illustrate the

More information

Statistical Inference Procedures

Statistical Inference Procedures Statitical Iferece Procedure Cofidece Iterval Hypothei Tet Statitical iferece produce awer to pecific quetio about the populatio of iteret baed o the iformatio i a ample. Iferece procedure mut iclude a

More information

Fig. 1: Streamline coordinates

Fig. 1: Streamline coordinates 1 Equatio of Motio i Streamlie Coordiate Ai A. Soi, MIT 2.25 Advaced Fluid Mechaic Euler equatio expree the relatiohip betwee the velocity ad the preure field i ivicid flow. Writte i term of treamlie coordiate,

More information

Comments on Discussion Sheet 18 and Worksheet 18 ( ) An Introduction to Hypothesis Testing

Comments on Discussion Sheet 18 and Worksheet 18 ( ) An Introduction to Hypothesis Testing Commet o Dicuio Sheet 18 ad Workheet 18 ( 9.5-9.7) A Itroductio to Hypothei Tetig Dicuio Sheet 18 A Itroductio to Hypothei Tetig We have tudied cofidece iterval for a while ow. Thee are method that allow

More information

STUDENT S t-distribution AND CONFIDENCE INTERVALS OF THE MEAN ( )

STUDENT S t-distribution AND CONFIDENCE INTERVALS OF THE MEAN ( ) STUDENT S t-distribution AND CONFIDENCE INTERVALS OF THE MEAN Suppoe that we have a ample of meaured value x1, x, x3,, x of a igle uow quatity. Aumig that the meauremet are draw from a ormal ditributio

More information

System Control. Lesson #19a. BME 333 Biomedical Signals and Systems - J.Schesser

System Control. Lesson #19a. BME 333 Biomedical Signals and Systems - J.Schesser Sytem Cotrol Leo #9a 76 Sytem Cotrol Baic roblem Say you have a ytem which you ca ot alter but it repoe i ot optimal Example Motor cotrol for exokeleto Robotic cotrol roblem that ca occur Utable Traiet

More information

Introduction to Control Systems

Introduction to Control Systems Itroductio to Cotrol Sytem CLASSIFICATION OF MATHEMATICAL MODELS Icreaig Eae of Aalyi Static Icreaig Realim Dyamic Determiitic Stochatic Lumped Parameter Ditributed Parameter Liear Noliear Cotat Coefficiet

More information

We will look for series solutions to (1) around (at most) regular singular points, which without

We will look for series solutions to (1) around (at most) regular singular points, which without ENM 511 J. L. Baai April, 1 Frobeiu Solutio to a d order ODE ear a regular igular poit Coider the ODE y 16 + P16 y 16 + Q1616 y (1) We will look for erie olutio to (1) aroud (at mot) regular igular poit,

More information

Erick L. Oberstar Fall 2001 Project: Sidelobe Canceller & GSC 1. Advanced Digital Signal Processing Sidelobe Canceller (Beam Former)

Erick L. Oberstar Fall 2001 Project: Sidelobe Canceller & GSC 1. Advanced Digital Signal Processing Sidelobe Canceller (Beam Former) Erick L. Obertar Fall 001 Project: Sidelobe Caceller & GSC 1 Advaced Digital Sigal Proceig Sidelobe Caceller (Beam Former) Erick L. Obertar 001 Erick L. Obertar Fall 001 Project: Sidelobe Caceller & GSC

More information

ME 410 MECHANICAL ENGINEERING SYSTEMS LABORATORY REGRESSION ANALYSIS

ME 410 MECHANICAL ENGINEERING SYSTEMS LABORATORY REGRESSION ANALYSIS ME 40 MECHANICAL ENGINEERING REGRESSION ANALYSIS Regreio problem deal with the relatiohip betwee the frequec ditributio of oe (depedet) variable ad aother (idepedet) variable() which i (are) held fied

More information

Last time: Completed solution to the optimum linear filter in real-time operation

Last time: Completed solution to the optimum linear filter in real-time operation 6.3 tochatic Etimatio ad Cotrol, Fall 4 ecture at time: Completed olutio to the oimum liear filter i real-time operatio emi-free cofiguratio: t D( p) F( p) i( p) dte dp e π F( ) F( ) ( ) F( p) ( p) 4444443

More information

STRONG DEVIATION THEOREMS FOR THE SEQUENCE OF CONTINUOUS RANDOM VARIABLES AND THE APPROACH OF LAPLACE TRANSFORM

STRONG DEVIATION THEOREMS FOR THE SEQUENCE OF CONTINUOUS RANDOM VARIABLES AND THE APPROACH OF LAPLACE TRANSFORM Joural of Statitic: Advace i Theory ad Applicatio Volume, Number, 9, Page 35-47 STRONG DEVIATION THEORES FOR THE SEQUENCE OF CONTINUOUS RANDO VARIABLES AND THE APPROACH OF LAPLACE TRANSFOR School of athematic

More information

Professor: Mihnea UDREA DIGITAL SIGNAL PROCESSING. Grading: Web: MOODLE. 1. Introduction. General information

Professor: Mihnea UDREA DIGITAL SIGNAL PROCESSING. Grading: Web:   MOODLE. 1. Introduction. General information Geeral iformatio DIGITL SIGL PROCESSIG Profeor: ihea UDRE B29 mihea@comm.pub.ro Gradig: Laboratory: 5% Proect: 5% Tet: 2% ial exam : 5% Coure quiz: ±% Web: www.electroica.pub.ro OODLE 2 alog igal proceig

More information

MASSACHUSETTS INSTITUTE OF TECHNOLOGY 6.265/15.070J Fall 2013 Lecture 16 11/04/2013. Ito integral. Properties

MASSACHUSETTS INSTITUTE OF TECHNOLOGY 6.265/15.070J Fall 2013 Lecture 16 11/04/2013. Ito integral. Properties MASSACHUSES INSIUE OF ECHNOLOGY 6.65/15.7J Fall 13 Lecture 16 11/4/13 Ito itegral. Propertie Cotet. 1. Defiitio of Ito itegral. Propertie of Ito itegral 1 Ito itegral. Exitece We cotiue with the cotructio

More information

The Performance of Feedback Control Systems

The Performance of Feedback Control Systems The Performace of Feedbac Cotrol Sytem Objective:. Secify the meaure of erformace time-domai the firt te i the deig roce Percet overhoot / Settlig time T / Time to rie / Steady-tate error e. ut igal uch

More information

SOLUTION: The 95% confidence interval for the population mean µ is x ± t 0.025; 49

SOLUTION: The 95% confidence interval for the population mean µ is x ± t 0.025; 49 C22.0103 Sprig 2011 Homework 7 olutio 1. Baed o a ample of 50 x-value havig mea 35.36 ad tadard deviatio 4.26, fid a 95% cofidece iterval for the populatio mea. SOLUTION: The 95% cofidece iterval for the

More information

Time Response. First Order Systems. Time Constant, T c We call 1/a the time constant of the response. Chapter 4 Time Response

Time Response. First Order Systems. Time Constant, T c We call 1/a the time constant of the response. Chapter 4 Time Response Time Repoe Chapter 4 Time Repoe Itroductio The output repoe of a ytem i the um of two repoe: the forced repoe ad the atural repoe. Although may techique, uch a olvig a differetial equatio or takig the

More information

ON THE SCALE PARAMETER OF EXPONENTIAL DISTRIBUTION

ON THE SCALE PARAMETER OF EXPONENTIAL DISTRIBUTION Review of the Air Force Academy No. (34)/7 ON THE SCALE PARAMETER OF EXPONENTIAL DISTRIBUTION Aca Ileaa LUPAŞ Military Techical Academy, Bucharet, Romaia (lua_a@yahoo.com) DOI:.96/84-938.7.5..6 Abtract:

More information

REVIEW OF SIMPLE LINEAR REGRESSION SIMPLE LINEAR REGRESSION

REVIEW OF SIMPLE LINEAR REGRESSION SIMPLE LINEAR REGRESSION REVIEW OF SIMPLE LINEAR REGRESSION SIMPLE LINEAR REGRESSION I liear regreio, we coider the frequecy ditributio of oe variable (Y) at each of everal level of a ecod variable (X). Y i kow a the depedet variable.

More information

STAT Homework 1 - Solutions

STAT Homework 1 - Solutions STAT-36700 Homework 1 - Solutios Fall 018 September 11, 018 This cotais solutios for Homework 1. Please ote that we have icluded several additioal commets ad approaches to the problems to give you better

More information

DISCRETE MELLIN CONVOLUTION AND ITS EXTENSIONS, PERRON FORMULA AND EXPLICIT FORMULAE

DISCRETE MELLIN CONVOLUTION AND ITS EXTENSIONS, PERRON FORMULA AND EXPLICIT FORMULAE DISCRETE MELLIN CONVOLUTION AND ITS EXTENSIONS, PERRON FORMULA AND EXPLICIT FORMULAE Joe Javier Garcia Moreta Graduate tudet of Phyic at the UPV/EHU (Uiverity of Baque coutry) I Solid State Phyic Addre:

More information

Société de Calcul Mathématique, S. A. Algorithmes et Optimisation

Société de Calcul Mathématique, S. A. Algorithmes et Optimisation Société de Calcul Mathématique S A Algorithme et Optimiatio Radom amplig of proportio Berard Beauzamy Jue 2008 From time to time we fid a problem i which we do ot deal with value but with proportio For

More information

18.05 Problem Set 9, Spring 2014 Solutions

18.05 Problem Set 9, Spring 2014 Solutions 18.05 Problem Set 9, Sprig 2014 Solutio Problem 1. (10 pt.) (a) We have x biomial(, θ), o E(X) =θ ad Var(X) = θ(1 θ). The rule-of-thumb variace i jut 4. So the ditributio beig plotted are biomial(250,

More information

Isolated Word Recogniser

Isolated Word Recogniser Lecture 5 Iolated Word Recogitio Hidde Markov Model of peech State traitio ad aligmet probabilitie Searchig all poible aligmet Dyamic Programmig Viterbi Aligmet Iolated Word Recogitio 8. Iolated Word Recogier

More information

M227 Chapter 9 Section 1 Testing Two Parameters: Means, Variances, Proportions

M227 Chapter 9 Section 1 Testing Two Parameters: Means, Variances, Proportions M7 Chapter 9 Sectio 1 OBJECTIVES Tet two mea with idepedet ample whe populatio variace are kow. Tet two variace with idepedet ample. Tet two mea with idepedet ample whe populatio variace are equal Tet

More information

Math 113, Calculus II Winter 2007 Final Exam Solutions

Math 113, Calculus II Winter 2007 Final Exam Solutions Math, Calculus II Witer 7 Fial Exam Solutios (5 poits) Use the limit defiitio of the defiite itegral ad the sum formulas to compute x x + dx The check your aswer usig the Evaluatio Theorem Solutio: I this

More information

Tables and Formulas for Sullivan, Fundamentals of Statistics, 2e Pearson Education, Inc.

Tables and Formulas for Sullivan, Fundamentals of Statistics, 2e Pearson Education, Inc. Table ad Formula for Sulliva, Fudametal of Statitic, e. 008 Pearo Educatio, Ic. CHAPTER Orgaizig ad Summarizig Data Relative frequecy frequecy um of all frequecie Cla midpoit: The um of coecutive lower

More information

Heat Equation: Maximum Principles

Heat Equation: Maximum Principles Heat Equatio: Maximum Priciple Nov. 9, 0 I thi lecture we will dicu the maximum priciple ad uiquee of olutio for the heat equatio.. Maximum priciple. The heat equatio alo ejoy maximum priciple a the Laplace

More information

Zeta-reciprocal Extended reciprocal zeta function and an alternate formulation of the Riemann hypothesis By M. Aslam Chaudhry

Zeta-reciprocal Extended reciprocal zeta function and an alternate formulation of the Riemann hypothesis By M. Aslam Chaudhry Zeta-reciprocal Eteded reciprocal zeta fuctio ad a alterate formulatio of the Riema hypothei By. Alam Chaudhry Departmet of athematical Sciece, Kig Fahd Uiverity of Petroleum ad ieral Dhahra 36, Saudi

More information

x z Increasing the size of the sample increases the power (reduces the probability of a Type II error) when the significance level remains fixed.

x z Increasing the size of the sample increases the power (reduces the probability of a Type II error) when the significance level remains fixed. ] z-tet for the mea, μ If the P-value i a mall or maller tha a pecified value, the data are tatitically igificat at igificace level. Sigificace tet for the hypothei H 0: = 0 cocerig the ukow mea of a populatio

More information

Automatic Control Systems

Automatic Control Systems Automatic Cotrol Sytem Lecture-5 Time Domai Aalyi of Orer Sytem Emam Fathy Departmet of Electrical a Cotrol Egieerig email: emfmz@yahoo.com Itrouctio Compare to the implicity of a firt-orer ytem, a eco-orer

More information

into a discrete time function. Recall that the table of Laplace/z-transforms is constructed by (i) selecting to get

into a discrete time function. Recall that the table of Laplace/z-transforms is constructed by (i) selecting to get Lecture 25 Introduction to Some Matlab c2d Code in Relation to Sampled Sytem here are many way to convert a continuou time function, { h( t) ; t [0, )} into a dicrete time function { h ( k) ; k {0,,, }}

More information

Mechatronics. Time Response & Frequency Response 2 nd -Order Dynamic System 2-Pole, Low-Pass, Active Filter

Mechatronics. Time Response & Frequency Response 2 nd -Order Dynamic System 2-Pole, Low-Pass, Active Filter Time Respose & Frequecy Respose d -Order Dyamic System -Pole, Low-Pass, Active Filter R 4 R 7 C 5 e i R 1 C R 3 - + R 6 - + e out Assigmet: Perform a Complete Dyamic System Ivestigatio of the Two-Pole,

More information

Assignment 1 - Solutions. ECSE 420 Parallel Computing Fall November 2, 2014

Assignment 1 - Solutions. ECSE 420 Parallel Computing Fall November 2, 2014 Aigmet - Solutio ECSE 420 Parallel Computig Fall 204 ovember 2, 204. (%) Decribe briefly the followig term, expoe their caue, ad work-aroud the idutry ha udertake to overcome their coequece: (i) Memory

More information

STA 4032 Final Exam Formula Sheet

STA 4032 Final Exam Formula Sheet Chapter 2. Probability STA 4032 Fial Eam Formula Sheet Some Baic Probability Formula: (1) P (A B) = P (A) + P (B) P (A B). (2) P (A ) = 1 P (A) ( A i the complemet of A). (3) If S i a fiite ample pace

More information

EE Control Systems

EE Control Systems Copyright FL Lewis 7 All rights reserved Updated: Moday, November 1, 7 EE 4314 - Cotrol Systems Bode Plot Performace Specificatios The Bode Plot was developed by Hedrik Wade Bode i 1938 while he worked

More information

Confidence Intervals. Confidence Intervals

Confidence Intervals. Confidence Intervals A overview Mot probability ditributio are idexed by oe me parameter. F example, N(µ,σ 2 ) B(, p). I igificace tet, we have ued poit etimat f parameter. F example, f iid Y 1,Y 2,...,Y N(µ,σ 2 ), Ȳ i a poit

More information

HOMEWORK #10 SOLUTIONS

HOMEWORK #10 SOLUTIONS Math 33 - Aalysis I Sprig 29 HOMEWORK # SOLUTIONS () Prove that the fuctio f(x) = x 3 is (Riema) itegrable o [, ] ad show that x 3 dx = 4. (Without usig formulae for itegratio that you leart i previous

More information

Chapter 7: The z-transform. Chih-Wei Liu

Chapter 7: The z-transform. Chih-Wei Liu Chapter 7: The -Trasform Chih-Wei Liu Outlie Itroductio The -Trasform Properties of the Regio of Covergece Properties of the -Trasform Iversio of the -Trasform The Trasfer Fuctio Causality ad Stability

More information

IntroEcono. Discrete RV. Continuous RV s

IntroEcono. Discrete RV. Continuous RV s ItroEcoo Aoc. Prof. Poga Porchaiwiekul, Ph.D... ก ก e-mail: Poga.P@chula.ac.th Homepage: http://pioeer.chula.ac.th/~ppoga (c) Poga Porchaiwiekul, Chulalogkor Uiverity Quatitative, e.g., icome, raifall

More information

Chapter 9. Key Ideas Hypothesis Test (Two Populations)

Chapter 9. Key Ideas Hypothesis Test (Two Populations) Chapter 9 Key Idea Hypothei Tet (Two Populatio) Sectio 9-: Overview I Chapter 8, dicuio cetered aroud hypothei tet for the proportio, mea, ad tadard deviatio/variace of a igle populatio. However, ofte

More information

The z-transform. 7.1 Introduction. 7.2 The z-transform Derivation of the z-transform: x[n] = z n LTI system, h[n] z = re j

The z-transform. 7.1 Introduction. 7.2 The z-transform Derivation of the z-transform: x[n] = z n LTI system, h[n] z = re j The -Trasform 7. Itroductio Geeralie the complex siusoidal represetatio offered by DTFT to a represetatio of complex expoetial sigals. Obtai more geeral characteristics for discrete-time LTI systems. 7.

More information

COMPARISONS INVOLVING TWO SAMPLE MEANS. Two-tail tests have these types of hypotheses: H A : 1 2

COMPARISONS INVOLVING TWO SAMPLE MEANS. Two-tail tests have these types of hypotheses: H A : 1 2 Tetig Hypothee COMPARISONS INVOLVING TWO SAMPLE MEANS Two type of hypothee:. H o : Null Hypothei - hypothei of o differece. or 0. H A : Alterate Hypothei hypothei of differece. or 0 Two-tail v. Oe-tail

More information

ECEN620: Network Theory Broadband Circuit Design Fall 2014

ECEN620: Network Theory Broadband Circuit Design Fall 2014 ECE60: etwork Theory Broadbad Circuit Deig Fall 04 Lecture 3: PLL Aalyi Sam Palermo Aalog & Mixed-Sigal Ceter Texa A&M Uiverity Ageda & Readig PLL Overview & Applicatio PLL Liear Model Phae & Frequecy

More information

20. CONFIDENCE INTERVALS FOR THE MEAN, UNKNOWN VARIANCE

20. CONFIDENCE INTERVALS FOR THE MEAN, UNKNOWN VARIANCE 20. CONFIDENCE INTERVALS FOR THE MEAN, UNKNOWN VARIANCE If the populatio tadard deviatio σ i ukow, a it uually will be i practice, we will have to etimate it by the ample tadard deviatio. Sice σ i ukow,

More information

TUTORIAL 6. Review of Electrostatic

TUTORIAL 6. Review of Electrostatic TUTOIAL 6 eview of Electrotatic Outlie Some mathematic Coulomb Law Gau Law Potulatio for electrotatic Electric potetial Poio equatio Boudar coditio Capacitace Some mathematic Del operator A operator work

More information

ECE 422 Power System Operations & Planning 6 Small Signal Stability. Spring 2015 Instructor: Kai Sun

ECE 422 Power System Operations & Planning 6 Small Signal Stability. Spring 2015 Instructor: Kai Sun ECE 4 Power Sytem Operatio & Plaig 6 Small Sigal Stability Sprig 15 Itructor: Kai Su 1 Referece Saadat Chapter 11.4 EPRI Tutorial Chapter 8 Power Ocillatio Kudur Chapter 1 Power Ocillatio The power ytem

More information

100(1 α)% confidence interval: ( x z ( sample size needed to construct a 100(1 α)% confidence interval with a margin of error of w:

100(1 α)% confidence interval: ( x z ( sample size needed to construct a 100(1 α)% confidence interval with a margin of error of w: Stat 400, ectio 7. Large Sample Cofidece Iterval ote by Tim Pilachowki a Large-Sample Two-ided Cofidece Iterval for a Populatio Mea ectio 7.1 redux The poit etimate for a populatio mea µ will be a ample

More information

Math 10A final exam, December 16, 2016

Math 10A final exam, December 16, 2016 Please put away all books, calculators, cell phoes ad other devices. You may cosult a sigle two-sided sheet of otes. Please write carefully ad clearly, USING WORDS (ot just symbols). Remember that the

More information

13.4 Scalar Kalman Filter

13.4 Scalar Kalman Filter 13.4 Scalar Kalma Filter Data Model o derive the Kalma filter we eed the data model: a 1 + u < State quatio > + w < Obervatio quatio > Aumptio 1. u i zero mea Gauia, White, u } σ. w i zero mea Gauia, White,

More information

ECM Control Engineering Dr Mustafa M Aziz (2013) SYSTEM RESPONSE

ECM Control Engineering Dr Mustafa M Aziz (2013) SYSTEM RESPONSE ECM5 - Cotrol Egieerig Dr Mutafa M Aziz (3) SYSTEM RESPONSE. Itroductio. Repoe Aalyi of Firt-Order Sytem 3. Secod-Order Sytem 4. Siuoidal Repoe of the Sytem 5. Bode Diagram 6. Baic Fact About Egieerig

More information

Last time: Ground rules for filtering and control system design

Last time: Ground rules for filtering and control system design 6.3 Stochatic Etimatio ad Cotrol, Fall 004 Lecture 7 Lat time: Groud rule for filterig ad cotrol ytem deig Gral ytem Sytem parameter are cotaied i w( t ad w ( t. Deired output i grated by takig the igal

More information

1. (25 points) Use the limit definition of the definite integral and the sum formulas 1 to compute

1. (25 points) Use the limit definition of the definite integral and the sum formulas 1 to compute Math, Calculus II Fial Eam Solutios. 5 poits) Use the limit defiitio of the defiite itegral ad the sum formulas to compute 4 d. The check your aswer usig the Evaluatio Theorem. ) ) Solutio: I this itegral,

More information

8.6 Order-Recursive LS s[n]

8.6 Order-Recursive LS s[n] 8.6 Order-Recurive LS [] Motivate ti idea wit Curve Fittig Give data: 0,,,..., - [0], [],..., [-] Wat to fit a polyomial to data.., but wic oe i te rigt model?! Cotat! Quadratic! Liear! Cubic, Etc. ry

More information

Generalized Likelihood Functions and Random Measures

Generalized Likelihood Functions and Random Measures Pure Mathematical Sciece, Vol. 3, 2014, o. 2, 87-95 HIKARI Ltd, www.m-hikari.com http://dx.doi.org/10.12988/pm.2014.437 Geeralized Likelihood Fuctio ad Radom Meaure Chrito E. Koutzaki Departmet of Mathematic

More information

Chapter (a) ζ. ω. 5 2 (a) Type 0 (b) Type 0 (c) Type 1 (d) Type 2 (e) Type 3 (f) Type 3. (g) type 2 (h) type (a) K G s.

Chapter (a) ζ. ω. 5 2 (a) Type 0 (b) Type 0 (c) Type 1 (d) Type 2 (e) Type 3 (f) Type 3. (g) type 2 (h) type (a) K G s. Chapter 5 5 1 (a) ζ. ω 0 707 rad / ec (b) 0 ζ 0. 707 ω rad / ec (c) ζ 0. 5 1 ω 5 rad / ec (d) 0. 5 ζ 0. 707 ω 0. 5 rad / ec 5 (a) Type 0 (b) Type 0 (c) Type 1 (d) Type (e) Type 3 (f) Type 3 (g) type (h)

More information

MATH Exam 1 Solutions February 24, 2016

MATH Exam 1 Solutions February 24, 2016 MATH 7.57 Exam Solutios February, 6. Evaluate (A) l(6) (B) l(7) (C) l(8) (D) l(9) (E) l() 6x x 3 + dx. Solutio: D We perform a substitutio. Let u = x 3 +, so du = 3x dx. Therefore, 6x u() x 3 + dx = [

More information

Continuous Functions

Continuous Functions Cotiuous Fuctios Q What does it mea for a fuctio to be cotiuous at a poit? Aswer- I mathematics, we have a defiitio that cosists of three cocepts that are liked i a special way Cosider the followig defiitio

More information

FREE VIBRATION RESPONSE OF A SYSTEM WITH COULOMB DAMPING

FREE VIBRATION RESPONSE OF A SYSTEM WITH COULOMB DAMPING Mechaical Vibratios FREE VIBRATION RESPONSE OF A SYSTEM WITH COULOMB DAMPING A commo dampig mechaism occurrig i machies is caused by slidig frictio or dry frictio ad is called Coulomb dampig. Coulomb dampig

More information

Appendix: The Laplace Transform

Appendix: The Laplace Transform Appedix: The Laplace Trasform The Laplace trasform is a powerful method that ca be used to solve differetial equatio, ad other mathematical problems. Its stregth lies i the fact that it allows the trasformatio

More information

3. Z Transform. Recall that the Fourier transform (FT) of a DT signal xn [ ] is ( ) [ ] = In order for the FT to exist in the finite magnitude sense,

3. Z Transform. Recall that the Fourier transform (FT) of a DT signal xn [ ] is ( ) [ ] = In order for the FT to exist in the finite magnitude sense, 3. Z Trasform Referece: Etire Chapter 3 of text. Recall that the Fourier trasform (FT) of a DT sigal x [ ] is ω ( ) [ ] X e = j jω k = xe I order for the FT to exist i the fiite magitude sese, S = x [

More information

Reasons for Sampling. Forest Sampling. Scales of Measurement. Scales of Measurement. Sampling Error. Sampling - General Approach

Reasons for Sampling. Forest Sampling. Scales of Measurement. Scales of Measurement. Sampling Error. Sampling - General Approach Foret amplig Aver & Burkhart, Chpt. & Reao for amplig Do NOT have the time or moe to do a complete eumeratio Remember that the etimate of the populatio parameter baed o a ample are ot accurate, therefore

More information

MATH CALCULUS II Objectives and Notes for Test 4

MATH CALCULUS II Objectives and Notes for Test 4 MATH 44 - CALCULUS II Objectives ad Notes for Test 4 To do well o this test, ou should be able to work the followig tpes of problems. Fid a power series represetatio for a fuctio ad determie the radius

More information

Chapter 2 The Monte Carlo Method

Chapter 2 The Monte Carlo Method Chapter 2 The Mote Carlo Method The Mote Carlo Method stads for a broad class of computatioal algorithms that rely o radom sampligs. It is ofte used i physical ad mathematical problems ad is most useful

More information

2 nd class Advance Mathematics and numerical analysis الریاضیات المتقدمة والتحلیل العددي الماده:م.د.عبدالمحسن جابرعبدالحسین

2 nd class Advance Mathematics and numerical analysis الریاضیات المتقدمة والتحلیل العددي الماده:م.د.عبدالمحسن جابرعبدالحسین Save from: www.uotecholog.edu.iq d cla Advace Mathematic ad umerical aali استاذ الریاضیات المتقدمة والتحلیل العددي الماده:م.د.عبدالمحسن جابرعبدالحسین CHAPTER ONE Defiitio -Partial Derivative If f i a fuctio

More information

An application of a subset S of C onto another S' defines a function [f(z)] of the complex variable z.

An application of a subset S of C onto another S' defines a function [f(z)] of the complex variable z. Diola Bagaoko (1 ELEMENTARY FNCTIONS OFA COMPLEX VARIABLES I Basic Defiitio of a Fuctio of a Comple Variable A applicatio of a subset S of C oto aother S' defies a fuctio [f(] of the comple variable z

More information

Lecture 11. Course Review. (The Big Picture) G. Hovland Input-Output Limitations (Skogestad Ch. 3) Discrete. Time Domain

Lecture 11. Course Review. (The Big Picture) G. Hovland Input-Output Limitations (Skogestad Ch. 3) Discrete. Time Domain MER4 Advaced Cotrol Lecture Coure Review (he ig Picture MER4 ADVANCED CONROL EMEER, 4 G. Hovlad 4 Mai heme of MER4 Frequecy Domai Aalyi (Nie Chapter Phae ad Gai Margi Iput-Output Limitatio (kogetad Ch.

More information

EE Midterm Test 1 - Solutions

EE Midterm Test 1 - Solutions EE35 - Midterm Test - Solutios Total Poits: 5+ 6 Bous Poits Time: hour. ( poits) Cosider the parallel itercoectio of the two causal systems, System ad System 2, show below. System x[] + y[] System 2 The

More information

Performance-Based Plastic Design (PBPD) Procedure

Performance-Based Plastic Design (PBPD) Procedure Performace-Baed Platic Deig (PBPD) Procedure 3. Geeral A outlie of the tep-by-tep, Performace-Baed Platic Deig (PBPD) procedure follow, with detail to be dicued i ubequet ectio i thi chapter ad theoretical

More information

UNIVERSITY OF CALICUT

UNIVERSITY OF CALICUT Samplig Ditributio 1 UNIVERSITY OF CALICUT SCHOOL OF DISTANCE EDUCATION BSc. MATHEMATICS COMPLEMENTARY COURSE CUCBCSS 2014 Admiio oward III Semeter STATISTICAL INFERENCE Quetio Bak 1. The umber of poible

More information

MASSACHUSETTS INSTITUTE OF TECHNOLOGY Department of Electrical Engineering and Computer Science. BACKGROUND EXAM September 30, 2004.

MASSACHUSETTS INSTITUTE OF TECHNOLOGY Department of Electrical Engineering and Computer Science. BACKGROUND EXAM September 30, 2004. MASSACHUSETTS INSTITUTE OF TECHNOLOGY Departmet of Electrical Egieerig ad Computer Sciece 6.34 Discrete Time Sigal Processig Fall 24 BACKGROUND EXAM September 3, 24. Full Name: Note: This exam is closed

More information

CONTROL ENGINEERING LABORATORY

CONTROL ENGINEERING LABORATORY Uiverity of Techology Departmet of Electrical Egieerig Cotrol Egieerig Lab. CONTROL ENGINEERING LABORATORY By Dr. Abdul. Rh. Humed M.Sc. Quay Salim Tawfeeq M.Sc. Nihad Mohammed Amee M.Sc. Waleed H. Habeeb

More information

Chapter 2 Feedback Control Theory Continued

Chapter 2 Feedback Control Theory Continued Chapter Feedback Cotrol Theor Cotiued. Itroductio I the previous chapter, the respose characteristic of simple first ad secod order trasfer fuctios were studied. It was show that first order trasfer fuctio,

More information

Implicit function theorem

Implicit function theorem Jovo Jaric Implicit fuctio theorem The reader kows that the equatio of a curve i the x - plae ca be expressed F x, =., this does ot ecessaril represet a fuctio. Take, for example F x, = 2x x =. (1 either

More information

VIII. Interval Estimation A. A Few Important Definitions (Including Some Reminders)

VIII. Interval Estimation A. A Few Important Definitions (Including Some Reminders) VIII. Iterval Etimatio A. A Few Importat Defiitio (Icludig Some Remider) 1. Poit Etimate - a igle umerical value ued a a etimate of a parameter.. Poit Etimator - the ample tatitic that provide the poit

More information

THE CONCEPT OF THE ROOT LOCUS. H(s) THE CONCEPT OF THE ROOT LOCUS

THE CONCEPT OF THE ROOT LOCUS. H(s) THE CONCEPT OF THE ROOT LOCUS So far i the tudie of cotrol yte the role of the characteritic equatio polyoial i deteriig the behavior of the yte ha bee highlighted. The root of that polyoial are the pole of the cotrol yte, ad their

More information

MATH 10550, EXAM 3 SOLUTIONS

MATH 10550, EXAM 3 SOLUTIONS MATH 155, EXAM 3 SOLUTIONS 1. I fidig a approximate solutio to the equatio x 3 +x 4 = usig Newto s method with iitial approximatio x 1 = 1, what is x? Solutio. Recall that x +1 = x f(x ) f (x ). Hece,

More information

Confidence Intervals: Three Views Class 23, Jeremy Orloff and Jonathan Bloom

Confidence Intervals: Three Views Class 23, Jeremy Orloff and Jonathan Bloom Cofidece Iterval: Three View Cla 23, 18.05 Jeremy Orloff ad Joatha Bloom 1 Learig Goal 1. Be able to produce z, t ad χ 2 cofidece iterval baed o the correpodig tadardized tatitic. 2. Be able to ue a hypothei

More information

MATH301 Real Analysis (2008 Fall) Tutorial Note #7. k=1 f k (x) converges pointwise to S(x) on E if and

MATH301 Real Analysis (2008 Fall) Tutorial Note #7. k=1 f k (x) converges pointwise to S(x) on E if and MATH01 Real Aalysis (2008 Fall) Tutorial Note #7 Sequece ad Series of fuctio 1: Poitwise Covergece ad Uiform Covergece Part I: Poitwise Covergece Defiitio of poitwise covergece: A sequece of fuctios f

More information

Another Look at Estimation for MA(1) Processes With a Unit Root

Another Look at Estimation for MA(1) Processes With a Unit Root Aother Look at Etimatio for MA Procee With a Uit Root F. Jay Breidt Richard A. Davi Na-Jug Hu Murray Roeblatt Colorado State Uiverity Natioal Tig-Hua Uiverity U. of Califoria, Sa Diego http://www.tat.colotate.edu/~rdavi/lecture

More information

Capacitors and PN Junctions. Lecture 8: Prof. Niknejad. Department of EECS University of California, Berkeley. EECS 105 Fall 2003, Lecture 8

Capacitors and PN Junctions. Lecture 8: Prof. Niknejad. Department of EECS University of California, Berkeley. EECS 105 Fall 2003, Lecture 8 CS 15 Fall 23, Lecture 8 Lecture 8: Capacitor ad PN Juctio Prof. Nikejad Lecture Outlie Review of lectrotatic IC MIM Capacitor No-Liear Capacitor PN Juctio Thermal quilibrium lectrotatic Review 1 lectric

More information

Name: Math 10550, Final Exam: December 15, 2007

Name: Math 10550, Final Exam: December 15, 2007 Math 55, Fial Exam: December 5, 7 Name: Be sure that you have all pages of the test. No calculators are to be used. The exam lasts for two hours. Whe told to begi, remove this aswer sheet ad keep it uder

More information

6.3 Testing Series With Positive Terms

6.3 Testing Series With Positive Terms 6.3. TESTING SERIES WITH POSITIVE TERMS 307 6.3 Testig Series With Positive Terms 6.3. Review of what is kow up to ow I theory, testig a series a i for covergece amouts to fidig the i= sequece of partial

More information

f t dt. Write the third-degree Taylor polynomial for G

f t dt. Write the third-degree Taylor polynomial for G AP Calculus BC Homework - Chapter 8B Taylor, Maclauri, ad Power Series # Taylor & Maclauri Polyomials Critical Thikig Joural: (CTJ: 5 pts.) Discuss the followig questios i a paragraph: What does it mea

More information

Math 213b (Spring 2005) Yum-Tong Siu 1. Explicit Formula for Logarithmic Derivative of Riemann Zeta Function

Math 213b (Spring 2005) Yum-Tong Siu 1. Explicit Formula for Logarithmic Derivative of Riemann Zeta Function Math 3b Sprig 005 Yum-og Siu Expliit Formula for Logarithmi Derivative of Riema Zeta Futio he expliit formula for the logarithmi derivative of the Riema zeta futio i the appliatio to it of the Perro formula

More information

Weak formulation and Lagrange equations of motion

Weak formulation and Lagrange equations of motion Chapter 4 Weak formulatio ad Lagrage equatio of motio A mot commo approach to tudy tructural dyamic i the ue of the Lagrage equatio of motio. Thee are obtaied i thi chapter tartig from the Cauchy equatio

More information

Hidden Markov Model Parameters

Hidden Markov Model Parameters .PPT 5/04/00 Lecture 6 HMM Traiig Traiig Hidde Markov Model Iitial model etimate Viterbi traiig Baum-Welch traiig 8.7.PPT 5/04/00 8.8 Hidde Markov Model Parameter c c c 3 a a a 3 t t t 3 c a t A Hidde

More information

Infinite Sequences and Series

Infinite Sequences and Series Chapter 6 Ifiite Sequeces ad Series 6.1 Ifiite Sequeces 6.1.1 Elemetary Cocepts Simply speakig, a sequece is a ordered list of umbers writte: {a 1, a 2, a 3,...a, a +1,...} where the elemets a i represet

More information

Physics 116A Solutions to Homework Set #9 Winter 2012

Physics 116A Solutions to Homework Set #9 Winter 2012 Physics 116A Solutios to Homework Set #9 Witer 1 1. Boas, problem 11.3 5. Simplify Γ( 1 )Γ(4)/Γ( 9 ). Usig xγ(x) Γ(x + 1) repeatedly, oe obtais Γ( 9) 7 Γ( 7) 7 5 Γ( 5 ), etc. util fially obtaiig Γ( 9)

More information

Most text will write ordinary derivatives using either Leibniz notation 2 3. y + 5y= e and y y. xx tt t

Most text will write ordinary derivatives using either Leibniz notation 2 3. y + 5y= e and y y. xx tt t Itroductio to Differetial Equatios Defiitios ad Termiolog Differetial Equatio: A equatio cotaiig the derivatives of oe or more depedet variables, with respect to oe or more idepedet variables, is said

More information

Math 312 Lecture Notes One Dimensional Maps

Math 312 Lecture Notes One Dimensional Maps Math 312 Lecture Notes Oe Dimesioal Maps Warre Weckesser Departmet of Mathematics Colgate Uiversity 21-23 February 25 A Example We begi with the simplest model of populatio growth. Suppose, for example,

More information