State Observer Design

Size: px
Start display at page:

Download "State Observer Design"

Transcription

1 Sa Obsrvr Dsgn A. Khak Sdgh Conrol Sysms Group Faculy of Elcrcal and Compur Engnrng K. N. Toos Unvrsy of Tchnology Fbruary Problm Formulaon A ky assumpon n gnvalu assgnmn and sablzng sysms usng Sa Fdback: Accss o all sa varabls for sa fdback. A Powr Plan Supr Har has up o 80 sa varabls! In many cass, assumpons mad abou accss o all sa varabls ar mpraccal du o h: Non physcal sa varabls Th numbr of sa varabls Cos of snsors Srvc and mannanc ssus Envronmnal nos Rlably Snsor placmn 2 1

2 A Ky Obsrvaon: Sa varabls Esmaon or Obsrvaon s crucal for sa fdback conrol. Obsrvr: A dynamcal sysm ha s oupu s sa varabls smas. Hsorcal rvw of Obsrvr Dsgn problm Wnr (1942) Kalman (1960) Kalman and Bucy (1961): Kalman Flr Lunbrgr (1963): Obsrvrs 3 Th frs vw: Ral Sas ha can no b masurd Inpu Ral Sysm Sysm Oupu Modl Modl Oupu Modl Sas Imporan Qusons? 4 2

3 Improvmns mad n h srucur o rsolv som of h rasd ssus: Ral Sas ha can no b masurd Inpu Ral Sysm Sysm Oupu Modl Modl Oupu Modl Sas Esmaon Error 5 Applcaons of Obsrvr and Opmal Obsrvrs or Kalman Flrs Esmaon of sa varabls for conrol Faul Dagnoss Bhavour Prdcon of Dynamcal Procsss Mssl Gudanc Fnancal Engnrng: Sock Bhavour, Inflaon ra, Powr Engnrng: Load prdcon, Gnral Sysm Engnrng Bo Mdcal Engnrng

4 Obsrvr Srucur and s Proprs ( ) x () = Ax + Bu() u ( ) = Kx( ) y() = Cx() Sysm wh un-masurabl sa varabls Sa Fdback Conrol Masurabl Oupus Obsrvr Dynamcal Sysm: u () y() Obsrvr Dynamcal Sysm () x Dynamcal Equaons? 7 Obsrvr Dynamcal Equaons: ( ) x () = Ax + Bu() + Ly() Slc hs marcs such ha h Obsrvaon Errors ar Asympocally drvd o zro () = x () x () ( ) ( ) () = x() x() = Ax + Bu() [ Ax + Bu() + Ly()] 8 4

5 () = Ax + Bu() [ Ax + Bu() + Ly()] ( ) ( ) () = Ax + Bu () Ax [ ()] Bu () LCx () ( ) ( ) () = A () + ( A LC A) x() + ( B B ) u() Slc hs marcs n ordr o zro h obsrvaon rror asympocally and ndpndn from npu and sa vcors: A = A LC B = B Obsrvr Dsgn Marx 9 ( ) x() = Ax + Bu() + Ly() ( ) x() = ( A LC) x + Bu() + Ly() u () x() = ( A LC) x ( ) + [ B L] y () or ( ) x () = Ax + Bu() + L[ y() Cx()] Obsrvr Dynamcal Equaons 10 5

6 Block Dagram of Obsrvr Dynamcal Sysm 11 () = A () Dynamcal Sably of Obsrvaon Error Sysm. Obsrvr Pols Trad-off n choosng h Pol loc Kalman Flr Sngl oupu and Mul-oupu sysm. Ncssary and Suffcn condons for h Obsrvr Dsgn? 12 6

7 Ncssary and Suffcn condons for Obsrvr Dsgn T T T d[ λi ( A LC)] = d[ λi ( A C L )] Dualy Prncpl: Th ncssary and Suffcn Condon for Obsrvr Dsgn s Sysm Obsrvably. 13 Sa Fdback Conrol Sysms Dsgn wh Obsrvr Sa fdback conrol sysm srucur wh a full ordr obsrvr: ( ) ( x( ) = Ax + Bu ) y( ) = Cx( ) u ( ) = Kx( ) ( ) x () = Ax + Bu() + L[ y() Cx()] u ( ) = K x ( ) ( ) x () = [ A BK LC] x + Ly() 14 7

8 Closd Loop Block Dagram: 15 Closd Loop Sysm Analyss: x () A BK x() = LC A LC BK x () x() () = x () x () () = ( A LC )() x () = ( A BK ) x () + BK() lm () = 0 x () = [ A BK] x () Marx Slcon: Obsrvr Dsgn + Conrol 16 8

9 Sparaon Prncpl: I 0 x () A BK x() T = = I I LC A LC BK x () x() 1 I 0 T = I I x () A BK BK x() = 0 A LC ( ) () si A + BK BK d 0 si A LC = + d( si A + BK)d( si A + LC) Sparaon Prncpl 17 Dscr-Tm Obsrvrs Consdr h sa fdback dscr m conrol sysm: x( k + 1) = Gx( k) + Hu( k) y( k) = Cx( k) u( k) = Kx ~ ( k) Th closd loop dynamcal quaon: Prdcon Obsrvr x~ ( k + 1) = Gx ~ ( k) + Hu( k) + K ( k + 1) = (G K C) ( k) { y( k) y~ ( k) } x~ ( k + 1) = (G K C)x ~ ( k) + Hu( k) + K y( k) Obsrvr Fdback Gan Marx 18 9

10 Conrollr Puls Transfr Funcon Marx: U( z) = KX ~ ( z) z-ransform of prdcon obsrvr and subsuon of abov fdback quaon ylds: U(z) = 1 [ K( zi G + K C + HK) K ] Y( z) Conrollr Characrsc quaon: zi G + K C + HK = 0 Ackrman Formula for h Obsrvr Gan Marx: Error Dynamcs Characrsc Equaon 1 C 0 CG 0 K (G) n n 1 = φ, φ(g) = G + α1g + + αn-1g + αn-1i n 1 CG 1 19 Transfr Funcon Approach o Pol placmn-obsrvr Dsgn y( ) = cx( ) ( ) ( x( ) = Ax + bu ) 1 bs ( ) g( s) = c( si A) b = as ( ) ( ) x () = A x + bu() + Ly() c u () = Kx () + r () sx ( s) = A X s + bu ( s) + LY ( s) U( s) = kx ( s) + R( s) ( ) c 20 10

11 U s R s k si A LY s bu s 1 ( ) ( ) = ( c ) [ ( ) + ( )] kadj( si Ac) L kadj( si Ac) b = Y ( s ) U( s) si A si A qs ( ) s ( ) = Y ( s) U ( s) d( s) d( s) c c as ( ) qs ( ) s ( ) as ( ) Y ( s) R( s) = Y ( s) Y ( s) bs ( ) ds ( ) ds ( ) bs ( ) Y ( s) b( s) d( s) b( s) d( s) = = Rs ( ) as ( )[ ds ( ) + ( s)] + q( s) b( s) a( s) p( s) + q( s) bs ( ) 21 Dophann Equaon: as ( ) ps ( ) + qs) ( bs ( ) = cs ( ) Ncssary and Suffcn condon for solvng h Dophann quaons. Closd Loop Sysm ordr and Full ordr obsrvr. Pol Placmn dsgn wh Pol-Zro Cancllaon approach

12 Dophann Equaon: as ( ) = s + a s + a s + + as+ a n n 1 n 2 n 1 n bs ( ) = b s + b s + + bs+ b n 1 cs ( ) = s + c s n 1 n 2 n n 2n 1 2n c s + c p( s) = s + p s + p s + + p s + p n n 1 n 2 n 1 n qs ( ) = q s + q s + + q s + q And, n 1 n 2 n 1 n 2 1 as ( ) ps ( ) + qs) ( bs ( ) = cs ( )

Consider a system of 2 simultaneous first order linear equations

Consider a system of 2 simultaneous first order linear equations Soluon of sysms of frs ordr lnar quaons onsdr a sysm of smulanous frs ordr lnar quaons a b c d I has h alrna mar-vcor rprsnaon a b c d Or, n shorhand A, f A s alrady known from con W know ha h abov sysm

More information

t=0 t>0: + vr - i dvc Continuation

t=0 t>0: + vr - i dvc Continuation hapr Ga Dlay and rcus onnuaon s rcu Equaon >: S S Ths dffrnal quaon, oghr wh h nal condon, fully spcfs bhaor of crcu afr swch closs Our n challng: larn how o sol such quaons TUE/EE 57 nwrk analys 4/5 NdM

More information

Summary: Solving a Homogeneous System of Two Linear First Order Equations in Two Unknowns

Summary: Solving a Homogeneous System of Two Linear First Order Equations in Two Unknowns Summary: Solvng a Homognous Sysm of Two Lnar Frs Ordr Equaons n Two Unknowns Gvn: A Frs fnd h wo gnvalus, r, and hr rspcv corrspondng gnvcors, k, of h coffcn mar A Dpndng on h gnvalus and gnvcors, h gnral

More information

CSE 245: Computer Aided Circuit Simulation and Verification

CSE 245: Computer Aided Circuit Simulation and Verification CSE 45: Compur Aidd Circui Simulaion and Vrificaion Fall 4, Sp 8 Lcur : Dynamic Linar Sysm Oulin Tim Domain Analysis Sa Equaions RLC Nwork Analysis by Taylor Expansion Impuls Rspons in im domain Frquncy

More information

9. Simple Rules for Monetary Policy

9. Simple Rules for Monetary Policy 9. Smpl Ruls for Monar Polc John B. Talor, Ma 0, 03 Woodford, AR 00 ovrvw papr Purpos s o consdr o wha xn hs prscrpon rsmbls h sor of polc ha conomc hor would rcommnd Bu frs, l s rvw how hs sor of polc

More information

Chapter 13 Laplace Transform Analysis

Chapter 13 Laplace Transform Analysis Chapr aplac Tranorm naly Chapr : Ouln aplac ranorm aplac Tranorm -doman phaor analy: x X σ m co ω φ x X X m φ x aplac ranorm: [ o ] d o d < aplac Tranorm Thr condon Unlaral on-dd aplac ranorm: aplac ranorm

More information

Chap 2: Reliability and Availability Models

Chap 2: Reliability and Availability Models Chap : lably ad valably Modls lably = prob{s s fully fucog [,]} Suppos from [,] m prod, w masur ou of N compos, of whch N : # of compos oprag corrcly a m N f : # of compos whch hav fald a m rlably of h

More information

The Variance-Covariance Matrix

The Variance-Covariance Matrix Th Varanc-Covaranc Marx Our bggs a so-ar has bn ng a lnar uncon o a s o daa by mnmzng h las squars drncs rom h o h daa wh mnsarch. Whn analyzng non-lnar daa you hav o us a program l Malab as many yps o

More information

innovations shocks white noise

innovations shocks white noise Innovaons Tm-srs modls ar consrucd as lnar funcons of fundamnal forcasng rrors, also calld nnovaons or shocks Ths basc buldng blocks sasf var σ Srall uncorrlad Ths rrors ar calld wh nos In gnral, f ou

More information

where: u: input y: output x: state vector A, B, C, D are const matrices

where: u: input y: output x: state vector A, B, C, D are const matrices Sa pac modl: linar: y or in om : Sa q : f, u Oupu q : y h, u u Du F Gu y H Ju whr: u: inpu y: oupu : a vcor,,, D ar con maric Eampl " $ & ' " $ & 'u y " & * * * * [ ],, D H D I " $ " & $ ' " & $ ' " &

More information

Chapter 9 Transient Response

Chapter 9 Transient Response har 9 Transn sons har 9: Ouln N F n F Frs-Ordr Transns Frs-Ordr rcus Frs ordr crcus: rcus conan onl on nducor or on caacor gornd b frs-ordr dffrnal quaons. Zro-nu rsons: h crcu has no ald sourc afr a cran

More information

TRACKING AND DISTURBANCE REJECTION

TRACKING AND DISTURBANCE REJECTION TRACKING AND DISTURBANCE REJECTION Sadegh Bolouki Lecture slides for ECE 515 University of Illinois, Urbana-Champaign Fall 2016 S. Bolouki (UIUC) 1 / 13 General objective: The output to track a reference

More information

Guaranteed Cost Control for a Class of Uncertain Delay Systems with Actuator Failures Based on Switching Method

Guaranteed Cost Control for a Class of Uncertain Delay Systems with Actuator Failures Based on Switching Method 49 Inrnaonal Journal of Conrol, Ru Wang Auomaon, and Jun and Zhao Sysms, vol. 5, no. 5, pp. 49-5, Ocobr 7 Guarand Cos Conrol for a Class of Uncran Dlay Sysms wh Acuaor Falurs Basd on Swchng Mhod Ru Wang

More information

Mitigation of Inrush Current in Load Transformer for Series Voltage Sag Compensator

Mitigation of Inrush Current in Load Transformer for Series Voltage Sag Compensator Inrnaonal Journal of Engnrng Rsarch & Tchnology (IJERT) ISSN: 2278-181 Vol. 3 Issu 5, May - 214 Mgaon of Inrush Currn n Load Transformr for Srs Volag Sag Compnsaor Asf Hamd Wan M.Tch Scholar, Dp. of Elcrcal

More information

EE"232"Lightwave"Devices Lecture"16:"p7i7n"Photodiodes"and" Photoconductors"

EE232LightwaveDevices Lecture16:p7i7nPhotodiodesand Photoconductors EE"232"Lgwav"Dvcs Lcur"16:"p77n"Pooos"an" Pooconucors" Rang:"Cuang,"Cap."15"(2 n E) Insrucor:"Mng"C."Wu Unvrsy"of"Calforna,"Brkly Elcrcal"Engnrng"an"Compur"Scncs"Dp. EE232$Lcur$16-1 Rvrs"bas%p""n%juncon

More information

FTCS Solution to the Heat Equation

FTCS Solution to the Heat Equation FTCS Soluon o he Hea Equaon ME 448/548 Noes Gerald Reckenwald Porland Sae Unversy Deparmen of Mechancal Engneerng gerry@pdxedu ME 448/548: FTCS Soluon o he Hea Equaon Overvew Use he forward fne d erence

More information

Robust decentralized control with scalar output of multivariable structurally uncertain plants with state delay 1

Robust decentralized control with scalar output of multivariable structurally uncertain plants with state delay 1 rprns of h 8h IFAC World Congrss lano Ial Augus 8 - Spmbr obus dcnralzd conrol wh scalar oupu of mulvarabl srucurall uncran plans wh sa dla Elzava arshva Absrac h problm of a robus conrol ssm dsgn for

More information

Advanced Queueing Theory. M/G/1 Queueing Systems

Advanced Queueing Theory. M/G/1 Queueing Systems Advand Quung Thory Ths slds ar rad by Dr. Yh Huang of Gorg Mason Unvrsy. Sudns rgsrd n Dr. Huang's ourss a GMU an ma a sngl mahn-radabl opy and prn a sngl opy of ah sld for hr own rfrn, so long as ah sld

More information

Joint State and Parameter Estimation by Extended Kalman Filter (EKF) technique

Joint State and Parameter Estimation by Extended Kalman Filter (EKF) technique Inrnaonal Journal o Engnrng Rsarch an Dvlopmn -ISSN: 78-67 p-issn: 78-8 www.jr.com Volum Issu 8 (Augus 5.4-5 Jon Sa an aramr Esmaon by En alman Flr (EF chnu S. Damoharao.S.L.V. Ayyarao G Sun Dp. o EEE

More information

Boosting and Ensemble Methods

Boosting and Ensemble Methods Boosng and Ensmbl Mhods PAC Larnng modl Som dsrbuon D ovr doman X Eampls: c* s h arg funcon Goal: Wh hgh probably -d fnd h n H such ha rrorh,c* < d and ar arbrarly small. Inro o ML 2 Wak Larnng

More information

Supplementary Figure 1. Experiment and simulation with finite qudit. anharmonicity. (a), Experimental data taken after a 60 ns three-tone pulse.

Supplementary Figure 1. Experiment and simulation with finite qudit. anharmonicity. (a), Experimental data taken after a 60 ns three-tone pulse. Supplmnar Fgur. Eprmn and smulaon wh fn qud anharmonc. a, Eprmnal daa akn afr a 6 ns hr-on puls. b, Smulaon usng h amlonan. Supplmnar Fgur. Phagoran dnamcs n h m doman. a, Eprmnal daa. Th hr-on puls s

More information

Dynamic Team Decision Theory. EECS 558 Project Shrutivandana Sharma and David Shuman December 10, 2005

Dynamic Team Decision Theory. EECS 558 Project Shrutivandana Sharma and David Shuman December 10, 2005 Dynamc Team Decson Theory EECS 558 Proec Shruvandana Sharma and Davd Shuman December 0, 005 Oulne Inroducon o Team Decson Theory Decomposon of he Dynamc Team Decson Problem Equvalence of Sac and Dynamc

More information

Safety and Reliability of Embedded Systems. (Sicherheit und Zuverlässigkeit eingebetteter Systeme) Stochastic Reliability Analysis

Safety and Reliability of Embedded Systems. (Sicherheit und Zuverlässigkeit eingebetteter Systeme) Stochastic Reliability Analysis Safy and Rlably of Embddd Sysms (Schrh und Zuvrlässgk ngbr Sysm) Sochasc Rlably Analyss Safy and Rlably of Embddd Sysms Conn Dfnon of Rlably Hardwar- vs. Sofwar Rlably Tool Asssd Rlably Modlng Dscrpons

More information

Safety and Reliability of Embedded Systems. (Sicherheit und Zuverlässigkeit eingebetteter Systeme) Stochastic Reliability Analysis

Safety and Reliability of Embedded Systems. (Sicherheit und Zuverlässigkeit eingebetteter Systeme) Stochastic Reliability Analysis (Schrh und Zuvrlässgk ngbr Sysm) Sochasc Rlably Analyss Conn Dfnon of Rlably Hardwar- vs. Sofwar Rlably Tool Asssd Rlably Modlng Dscrpons of Falurs ovr Tm Rlably Modlng Exampls of Dsrbuon Funcons Th xponnal

More information

Dynamic modeling, simulation and control of a hybrid driven press mechanism

Dynamic modeling, simulation and control of a hybrid driven press mechanism INTERNTIONL JOURNL OF MECHNICS Volum 1 16 Dynamc modlng smulaon and conrol of a hybrd drvn prss mchansm Mhm Erkan Küük Lal Canan Dülgr bsrac Hybrd drvn mchansm combns h moon of a larg consan vlocy moor

More information

8. Queueing systems. Contents. Simple teletraffic model. Pure queueing system

8. Queueing systems. Contents. Simple teletraffic model. Pure queueing system 8. Quug sysms Cos 8. Quug sysms Rfrshr: Sml lraffc modl Quug dscl M/M/ srvr wag lacs Alcao o ack lvl modllg of daa raffc M/M/ srvrs wag lacs lc8. S-38.45 Iroduco o Tlraffc Thory Srg 5 8. Quug sysms 8.

More information

Linear Systems Analysis in the Time Domain

Linear Systems Analysis in the Time Domain Liar Sysms Aalysis i h Tim Domai Firs Ordr Sysms di vl = L, vr = Ri, d di L + Ri = () d R x= i, x& = x+ ( ) L L X() s I() s = = = U() s E() s Ls+ R R L s + R u () = () =, i() = L i () = R R Firs Ordr Sysms

More information

FAULT TOLERANT SYSTEMS

FAULT TOLERANT SYSTEMS FAULT TOLERANT SYSTEMS hp://www.cs.umass.du/c/orn/faultolransysms ar 4 Analyss Mhods Chapr HW Faul Tolranc ar.4.1 Duplx Sysms Boh procssors xcu h sam as If oupus ar n agrmn - rsul s assumd o b corrc If

More information

Lecture 4 : Backpropagation Algorithm. Prof. Seul Jung ( Intelligent Systems and Emotional Engineering Laboratory) Chungnam National University

Lecture 4 : Backpropagation Algorithm. Prof. Seul Jung ( Intelligent Systems and Emotional Engineering Laboratory) Chungnam National University Lcur 4 : Bacpropagaon Algorhm Pro. Sul Jung Inllgn Sm and moonal ngnrng Laboraor Chungnam Naonal Unvr Inroducon o Bacpropagaon algorhm 969 Mn and Papr aac. 980 Parr and Wrbo dcovrd bac propagaon algorhm.

More information

POLE PLACEMENT. Sadegh Bolouki. Lecture slides for ECE 515. University of Illinois, Urbana-Champaign. Fall S. Bolouki (UIUC) 1 / 19

POLE PLACEMENT. Sadegh Bolouki. Lecture slides for ECE 515. University of Illinois, Urbana-Champaign. Fall S. Bolouki (UIUC) 1 / 19 POLE PLACEMENT Sadegh Bolouki Lecture slides for ECE 515 University of Illinois, Urbana-Champaign Fall 2016 S. Bolouki (UIUC) 1 / 19 Outline 1 State Feedback 2 Observer 3 Observer Feedback 4 Reduced Order

More information

Reliability analysis of time - dependent stress - strength system when the number of cycles follows binomial distribution

Reliability analysis of time - dependent stress - strength system when the number of cycles follows binomial distribution raoal Joural of Sascs ad Ssms SSN 97-675 Volum, Numbr 7,. 575-58 sarch da Publcaos h://www.rublcao.com labl aalss of m - dd srss - srgh ssm wh h umbr of ccls follows bomal dsrbuo T.Sumah Umamahswar, N.Swah,

More information

Comb Filters. Comb Filters

Comb Filters. Comb Filters The smple flers dscussed so far are characered eher by a sngle passband and/or a sngle sopband There are applcaons where flers wh mulple passbands and sopbands are requred Thecomb fler s an example of

More information

CS434a/541a: Pattern Recognition Prof. Olga Veksler. Lecture 4

CS434a/541a: Pattern Recognition Prof. Olga Veksler. Lecture 4 CS434a/54a: Paern Recognon Prof. Olga Veksler Lecure 4 Oulne Normal Random Varable Properes Dscrmnan funcons Why Normal Random Varables? Analycally racable Works well when observaon comes form a corruped

More information

Control Systems. Mathematical Modeling of Control Systems.

Control Systems. Mathematical Modeling of Control Systems. Conrol Syem Mahemacal Modelng of Conrol Syem chbum@eoulech.ac.kr Oulne Mahemacal model and model ype. Tranfer funcon model Syem pole and zero Chbum Lee -Seoulech Conrol Syem Mahemacal Model Model are key

More information

Dynamic Controllability with Overlapping Targets: Or Why Target Independence May Not be Good for You

Dynamic Controllability with Overlapping Targets: Or Why Target Independence May Not be Good for You Dynamc Conrollably wh Ovrlappng Targs: Or Why Targ Indpndnc May No b Good for You Ncola Acoclla Unvrsy of Rom La Sapnza Govann D Barolomo Unvrsy of Rom La Sapnza and Unvrsy of Tramo Andrw Hughs Hall Vandrbl

More information

Chapter 7 Stead St y- ate Errors

Chapter 7 Stead St y- ate Errors Char 7 Say-Sa rror Inroucon Conrol ym analy an gn cfcaon a. rann ron b. Sably c. Say-a rror fnon of ay-a rror : u c a whr u : nu, c: ouu Val only for abl ym chck ym ably fr! nu for ay-a a nu analy U o

More information

6 OUTPUT FEEDBACK DESIGN

6 OUTPUT FEEDBACK DESIGN 6 OUTPUT FEEDBACK DESIGN When the whole sate vector is not available for feedback, i.e, we can measure only y = Cx. 6.1 Review of observer design Recall from the first class in linear systems that a simple

More information

Department of Economics University of Toronto

Department of Economics University of Toronto Deparmen of Economcs Unversy of Torono ECO408F M.A. Economercs Lecure Noes on Heeroskedascy Heeroskedascy o Ths lecure nvolves lookng a modfcaons we need o make o deal wh he regresson model when some of

More information

SIMEON BALL AND AART BLOKHUIS

SIMEON BALL AND AART BLOKHUIS A BOUND FOR THE MAXIMUM WEIGHT OF A LINEAR CODE SIMEON BALL AND AART BLOKHUIS Absrac. I s shown ha h paramrs of a lnar cod ovr F q of lngh n, dmnson k, mnmum wgh d and maxmum wgh m sasfy a cran congrunc

More information

Control System Engineering (EE301T) Assignment: 2

Control System Engineering (EE301T) Assignment: 2 Conrol Sysm Enginring (EE0T) Assignmn: PART-A (Tim Domain Analysis: Transin Rspons Analysis). Oain h rspons of a uniy fdack sysm whos opn-loop ransfr funcion is (s) s ( s 4) for a uni sp inpu and also

More information

Control Systems (Lecture note #6)

Control Systems (Lecture note #6) 6.5 Corol Sysms (Lcur o #6 Las Tm: Lar algbra rw Lar algbrac quaos soluos Paramrzao of all soluos Smlary rasformao: compao form Egalus ad gcors dagoal form bg pcur: o brach of h cours Vcor spacs marcs

More information

Dynamic Power Allocation in MIMO Fading Systems Without Channel Distribution Information

Dynamic Power Allocation in MIMO Fading Systems Without Channel Distribution Information PROC. IEEE INFOCOM 06 Dynamc Powr Allocaon n MIMO Fadng Sysms Whou Channl Dsrbuon Informaon Hao Yu and Mchal J. Nly Unvrsy of Souhrn Calforna Absrac Ths papr consdrs dynamc powr allocaon n MIMO fadng sysms

More information

Least squares and motion. Nuno Vasconcelos ECE Department, UCSD

Least squares and motion. Nuno Vasconcelos ECE Department, UCSD Las squars ad moo uo Vascoclos ECE Dparm UCSD Pla for oda oda w wll dscuss moo smao hs s rsg wo was moo s vr usful as a cu for rcogo sgmao comprsso c. s a gra ampl of las squars problm w wll also wrap

More information

An introduction to Support Vector Machine

An introduction to Support Vector Machine An nroducon o Suppor Vecor Machne 報告者 : 黃立德 References: Smon Haykn, "Neural Neworks: a comprehensve foundaon, second edon, 999, Chaper 2,6 Nello Chrsann, John Shawe-Tayer, An Inroducon o Suppor Vecor Machnes,

More information

Applying Software Reliability Techniques to Low Retail Demand Estimation

Applying Software Reliability Techniques to Low Retail Demand Estimation Applyng Sofwar Rlably Tchnqus o Low Ral Dmand Esmaon Ma Lndsy Unvrsy of Norh Txas ITDS Dp P.O. Box 30549 Dnon, TX 7603-549 940 565 3174 lndsym@un.du Robr Pavur Unvrsy of Norh Txas ITDS Dp P.O. Box 30549

More information

External Equivalent. EE 521 Analysis of Power Systems. Chen-Ching Liu, Boeing Distinguished Professor Washington State University

External Equivalent. EE 521 Analysis of Power Systems. Chen-Ching Liu, Boeing Distinguished Professor Washington State University xtrnal quvalnt 5 Analyss of Powr Systms Chn-Chng Lu, ong Dstngushd Profssor Washngton Stat Unvrsty XTRNAL UALNT ach powr systm (ara) s part of an ntrconnctd systm. Montorng dvcs ar nstalld and data ar

More information

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science : MULTIVARIABLE CONTROL SYSTEMS by A.

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science : MULTIVARIABLE CONTROL SYSTEMS by A. Massachusetts Institute of Technology Department of Electrical Engineering and Computer Science 6.245: MULTIVARIABLE CONTROL SYSTEMS by A. Megretski Q-Parameterization 1 This lecture introduces the so-called

More information

The Hyperelastic material is examined in this section.

The Hyperelastic material is examined in this section. 4. Hyprlastcty h Hyprlastc matral s xad n ths scton. 4..1 Consttutv Equatons h rat of chang of ntrnal nrgy W pr unt rfrnc volum s gvn by th strss powr, whch can b xprssd n a numbr of dffrnt ways (s 3.7.6):

More information

Online Supplement for Dynamic Multi-Technology. Production-Inventory Problem with Emissions Trading

Online Supplement for Dynamic Multi-Technology. Production-Inventory Problem with Emissions Trading Onlne Supplemen for Dynamc Mul-Technology Producon-Invenory Problem wh Emssons Tradng by We Zhang Zhongsheng Hua Yu Xa and Baofeng Huo Proof of Lemma For any ( qr ) Θ s easy o verfy ha he lnear programmng

More information

Transient Analysis of Two-dimensional State M/G/1 Queueing Model with Multiple Vacations and Bernoulli Schedule

Transient Analysis of Two-dimensional State M/G/1 Queueing Model with Multiple Vacations and Bernoulli Schedule Inrnaonal Journal of Compur Applcaons (975 8887) Volum 4 No.3, Fbruary 22 Transn Analyss of Two-dmnsonal Sa M/G/ Quung Modl wh Mulpl Vacaons and Brnoull Schdul Indra Assoca rofssor Dparmn of Sascs and

More information

Problem 1: Consider the following stationary data generation process for a random variable y t. e t ~ N(0,1) i.i.d.

Problem 1: Consider the following stationary data generation process for a random variable y t. e t ~ N(0,1) i.i.d. A/CN C m Sr Anal Profor Òcar Jordà Wnr conomc.c. Dav POBLM S SOLIONS Par I Analcal Quon Problm : Condr h followng aonar daa gnraon proc for a random varabl - N..d. wh < and N -. a Oban h populaon man varanc

More information

2.1. Differential Equations and Solutions #3, 4, 17, 20, 24, 35

2.1. Differential Equations and Solutions #3, 4, 17, 20, 24, 35 MATH 5 PS # Summr 00.. Diffrnial Equaions and Soluions PS.# Show ha ()C #, 4, 7, 0, 4, 5 ( / ) is a gnral soluion of h diffrnial quaion. Us a compur or calculaor o skch h soluions for h givn valus of h

More information

horizontal force output data block Hankel matrix transfer function complex frequency response function impedance matrix

horizontal force output data block Hankel matrix transfer function complex frequency response function impedance matrix AMBENT VBRATON Nomnclatur 1 NOMENCLATURE a a acclraton, coffcnt dmnsonlss corrcton factor A, B, C, D dscrt-tm stat sac modl b coffcnt c damng, stffnss C constant valu, damng matrx d damtr d dyn d stat

More information

Lecture 1: Contents of the course. Advanced Digital Control. IT tools CCSDEMO

Lecture 1: Contents of the course. Advanced Digital Control. IT tools CCSDEMO Goals of he course Lecure : Advanced Digial Conrol To beer undersand discree-ime sysems To beer undersand compuer-conrolled sysems u k u( ) u( ) Hold u k D-A Process Compuer y( ) A-D y ( ) Sampler y k

More information

Frequency Response. Response of an LTI System to Eigenfunction

Frequency Response. Response of an LTI System to Eigenfunction Frquncy Rsons Las m w Rvsd formal dfnons of lnary and m-nvaranc Found an gnfuncon for lnar m-nvaran sysms Found h frquncy rsons of a lnar sysm o gnfuncon nu Found h frquncy rsons for cascad, fdbac, dffrnc

More information

MEM 355 Performance Enhancement of Dynamical Systems A First Control Problem - Cruise Control

MEM 355 Performance Enhancement of Dynamical Systems A First Control Problem - Cruise Control MEM 355 Prformanc Enhancmn of Dynamical Sysms A Firs Conrol Problm - Cruis Conrol Harry G. Kwany Darmn of Mchanical Enginring & Mchanics Drxl Univrsiy Cruis Conrol ( ) mv = F mg sinθ cv v +.2v= u 9.8θ

More information

Analysis of decentralized potential field based multi-agent navigation via primal-dual Lyapunov theory

Analysis of decentralized potential field based multi-agent navigation via primal-dual Lyapunov theory Analyss of dcnralzd ponal fld basd mul-agn navgaon va prmal-dual Lyapunov hory Th MIT Faculy has mad hs arcl opnly avalabl. Plas shar how hs accss bnfs you. Your sory mars. Caon As Publshd Publshr Dmarogonas,

More information

Numbering Systems Basic Building Blocks Scaling and Round-off Noise. Number Representation. Floating vs. Fixed point. DSP Design.

Numbering Systems Basic Building Blocks Scaling and Round-off Noise. Number Representation. Floating vs. Fixed point. DSP Design. Numbring Systms Basic Building Blocks Scaling and Round-off Nois Numbr Rprsntation Viktor Öwall viktor.owall@it.lth.s Floating vs. Fixd point In floating point a valu is rprsntd by mantissa dtrmining th

More information

Full Order Observer Controller Design for Two Interacting Tank System Based on State Space Approach

Full Order Observer Controller Design for Two Interacting Tank System Based on State Space Approach Intrnational Journal of Application or Innovation in Enginring & Managmnt (IJAIEM) Wb Sit: www.ijaim.org Email: ditor@ijaim.org Volum 6, Issu 7, July 07 ISSN 39-4847 Full Ordr Obsrvr Controllr Dsign for

More information

Implementation of the Extended Conjugate Gradient Method for the Two- Dimensional Energized Wave Equation

Implementation of the Extended Conjugate Gradient Method for the Two- Dimensional Energized Wave Equation Lonardo Elcronc Jornal of raccs and Tchnolos ISSN 58-078 Iss 9 Jl-Dcmbr 006 p. -4 Implmnaon of h Endd Cona Gradn Mhod for h Two- Dmnsonal Enrd Wav Eqaon Vcor Onoma WAZIRI * Snda Ass REJU Mahmacs/Compr

More information

Lecture 6: Learning for Control (Generalised Linear Regression)

Lecture 6: Learning for Control (Generalised Linear Regression) Lecure 6: Learnng for Conrol (Generalsed Lnear Regresson) Conens: Lnear Mehods for Regresson Leas Squares, Gauss Markov heorem Recursve Leas Squares Lecure 6: RLSC - Prof. Sehu Vjayakumar Lnear Regresson

More information

Economics 600: August, 2007 Dynamic Part: Problem Set 5. Problems on Differential Equations and Continuous Time Optimization

Economics 600: August, 2007 Dynamic Part: Problem Set 5. Problems on Differential Equations and Continuous Time Optimization THE UNIVERSITY OF MARYLAND COLLEGE PARK, MARYLAND Economcs 600: August, 007 Dynamc Part: Problm St 5 Problms on Dffrntal Equatons and Contnuous Tm Optmzaton Quston Solv th followng two dffrntal quatons.

More information

Theoretical Seismology

Theoretical Seismology Thorcal Ssmology Lcur 9 Sgnal Procssng Fourr analyss Fourr sudd a h Écol Normal n Pars, augh by Lagrang, who Fourr dscrbd as h frs among Europan mn of scnc, Laplac, who Fourr rad lss hghly, and by Mong.

More information

The Finite Element Method for the Analysis of Non-Linear and Dynamic Systems

The Finite Element Method for the Analysis of Non-Linear and Dynamic Systems Swss Federal Insue of Page 1 The Fne Elemen Mehod for he Analyss of Non-Lnear and Dynamc Sysems Prof. Dr. Mchael Havbro Faber Dr. Nebojsa Mojslovc Swss Federal Insue of ETH Zurch, Swzerland Mehod of Fne

More information

COMPLEX NUMBER PAIRWISE COMPARISON AND COMPLEX NUMBER AHP

COMPLEX NUMBER PAIRWISE COMPARISON AND COMPLEX NUMBER AHP ISAHP 00, Bal, Indonsa, August -9, 00 COMPLEX NUMBER PAIRWISE COMPARISON AND COMPLEX NUMBER AHP Chkako MIYAKE, Kkch OHSAWA, Masahro KITO, and Masaak SHINOHARA Dpartmnt of Mathmatcal Informaton Engnrng

More information

A universal saturation controller design for mobile robots

A universal saturation controller design for mobile robots A unvrsal sauraon conrollr sgn for mobl robos K.D. Do,,Z.P.Jang an J. Pan Dparmn of Elcrcal an Compur Engnrng, Polychnc Unvrsy, NY, USA. Emal: uc@mch.uwa.u.au, zjang@conrol.poly.u Dparmn of Mchancal Engnrng,

More information

John Geweke a and Gianni Amisano b a Departments of Economics and Statistics, University of Iowa, USA b European Central Bank, Frankfurt, Germany

John Geweke a and Gianni Amisano b a Departments of Economics and Statistics, University of Iowa, USA b European Central Bank, Frankfurt, Germany Herarchcal Markov Normal Mxure models wh Applcaons o Fnancal Asse Reurns Appendx: Proofs of Theorems and Condonal Poseror Dsrbuons John Geweke a and Gann Amsano b a Deparmens of Economcs and Sascs, Unversy

More information

Performance Analysis for a Network having Standby Redundant Unit with Waiting in Repair

Performance Analysis for a Network having Standby Redundant Unit with Waiting in Repair TECHNI Inernaonal Journal of Compung Scence Communcaon Technologes VOL.5 NO. July 22 (ISSN 974-3375 erformance nalyss for a Nework havng Sby edundan Un wh ang n epar Jendra Sngh 2 abns orwal 2 Deparmen

More information

XV Exponential and Logarithmic Functions

XV Exponential and Logarithmic Functions MATHEMATICS 0-0-RE Dirnial Calculus Marin Huard Winr 08 XV Eponnial and Logarihmic Funcions. Skch h graph o h givn uncions and sa h domain and rang. d) ) ) log. Whn Sarah was born, hr parns placd $000

More information

Approximate Analytic Solution of (2+1) - Dimensional Zakharov-Kuznetsov(Zk) Equations Using Homotopy

Approximate Analytic Solution of (2+1) - Dimensional Zakharov-Kuznetsov(Zk) Equations Using Homotopy Arcle Inernaonal Journal of Modern Mahemacal Scences, 4, (): - Inernaonal Journal of Modern Mahemacal Scences Journal homepage: www.modernscenfcpress.com/journals/jmms.aspx ISSN: 66-86X Florda, USA Approxmae

More information

Gaussian Random Process and Its Application for Detecting the Ionospheric Disturbances Using GPS

Gaussian Random Process and Its Application for Detecting the Ionospheric Disturbances Using GPS Journal of Global Posonng Sysms (005) Vol. 4, No. 1-: 76-81 Gaussan Random Procss and Is Applcaon for Dcng h Ionosphrc Dsurbancs Usng GPS H.. Zhang 1,, J. Wang 3, W. Y. Zhu 1, C. Huang 1 (1) Shangha Asronomcal

More information

A Note on Estimability in Linear Models

A Note on Estimability in Linear Models Intrnatonal Journal of Statstcs and Applcatons 2014, 4(4): 212-216 DOI: 10.5923/j.statstcs.20140404.06 A Not on Estmablty n Lnar Modls S. O. Adymo 1,*, F. N. Nwob 2 1 Dpartmnt of Mathmatcs and Statstcs,

More information

4/12/2018. PHY 712 Electrodynamics 9-9:50 AM MWF Olin 105. Plan for Lecture 34: Review radiating systems

4/12/2018. PHY 712 Electrodynamics 9-9:50 AM MWF Olin 105. Plan for Lecture 34: Review radiating systems PHY 7 Eodynams 9-9:5 M MWF On 5 Pan o u : Rvw adang sysms Souon o Maxw s quaons wh sous Tm pod sous Examps //8 PHY 7 Spng 8 -- u //8 PHY 7 Spng 8 -- u Gna vw -- SI uns mosop o vauum om ( P M ): Couombs

More information

UNIVERSITAT AUTÒNOMA DE BARCELONA MARCH 2017 EXAMINATION

UNIVERSITAT AUTÒNOMA DE BARCELONA MARCH 2017 EXAMINATION INTERNATIONAL TRADE T. J. KEHOE UNIVERSITAT AUTÒNOMA DE BARCELONA MARCH 27 EXAMINATION Please answer wo of he hree quesons. You can consul class noes, workng papers, and arcles whle you are workng on he

More information

Kybernetika. Vladimír Kučera Linear quadratic control. State space vs. polynomial equations

Kybernetika. Vladimír Kučera Linear quadratic control. State space vs. polynomial equations Kybernetika Vladimír Kučera Linear quadratic control. State space vs. polynomial equations Kybernetika, Vol. 19 (1983), No. 3, 185--195 Persistent URL: http://dml.cz/dmlcz/124912 Terms of use: Institute

More information

On One Analytic Method of. Constructing Program Controls

On One Analytic Method of. Constructing Program Controls Appled Mahemacal Scences, Vol. 9, 05, no. 8, 409-407 HIKARI Ld, www.m-hkar.com hp://dx.do.org/0.988/ams.05.54349 On One Analyc Mehod of Consrucng Program Conrols A. N. Kvko, S. V. Chsyakov and Yu. E. Balyna

More information

Dual Approximate Dynamic Programming for Large Scale Hydro Valleys

Dual Approximate Dynamic Programming for Large Scale Hydro Valleys Dual Approxmae Dynamc Programmng for Large Scale Hydro Valleys Perre Carpener and Jean-Phlppe Chanceler 1 ENSTA ParsTech and ENPC ParsTech CMM Workshop, January 2016 1 Jon work wh J.-C. Alas, suppored

More information

Thermodynamic Properties of the Harmonic Oscillator and a Four Level System

Thermodynamic Properties of the Harmonic Oscillator and a Four Level System www.ccsn.org/apr Appld Physcs Rsarch Vol. 3, No. ; May Thrmodynamc Proprs of h Harmonc Oscllaor and a Four Lvl Sysm Oladunjoy A. Awoga Thorcal Physcs Group, Dparmn of Physcs, Unvrsy of Uyo, Uyo, Ngra E-mal:

More information

A THREE COMPARTMENT MATHEMATICAL MODEL OF LIVER

A THREE COMPARTMENT MATHEMATICAL MODEL OF LIVER A THREE COPARTENT ATHEATICAL ODEL OF LIVER V. An N. Ch. Paabhi Ramacharyulu Faculy of ahmaics, R D collgs, Hanamonda, Warangal, India Dparmn of ahmaics, Naional Insiu of Tchnology, Warangal, India E-ail:

More information

CALIFORNIA INSTITUTE OF TECHNOLOGY Control and Dynamical Systems. CDS 110b

CALIFORNIA INSTITUTE OF TECHNOLOGY Control and Dynamical Systems. CDS 110b CALIFORNIA INSTITUTE OF TECHNOLOGY Control and Dynamical Systems CDS 110b R. M. Murray Kalman Filters 14 January 2007 Reading: This set of lectures provides a brief introduction to Kalman filtering, following

More information

HEAT CONDUCTION PROBLEM IN A TWO-LAYERED HOLLOW CYLINDER BY USING THE GREEN S FUNCTION METHOD

HEAT CONDUCTION PROBLEM IN A TWO-LAYERED HOLLOW CYLINDER BY USING THE GREEN S FUNCTION METHOD Journal of Appled Mahemacs and Compuaonal Mechancs 3, (), 45-5 HEAT CONDUCTION PROBLEM IN A TWO-LAYERED HOLLOW CYLINDER BY USING THE GREEN S FUNCTION METHOD Sansław Kukla, Urszula Sedlecka Insue of Mahemacs,

More information

WiH Wei He

WiH Wei He Sysem Idenfcaon of onlnear Sae-Space Space Baery odels WH We He wehe@calce.umd.edu Advsor: Dr. Chaochao Chen Deparmen of echancal Engneerng Unversy of aryland, College Par 1 Unversy of aryland Bacground

More information

An Indian Journal FULL PAPER. Trade Science Inc. A stage-structured model of a single-species with density-dependent and birth pulses ABSTRACT

An Indian Journal FULL PAPER. Trade Science Inc. A stage-structured model of a single-species with density-dependent and birth pulses ABSTRACT [Typ x] [Typ x] [Typ x] ISSN : 974-7435 Volum 1 Issu 24 BioTchnology 214 An Indian Journal FULL PAPE BTAIJ, 1(24), 214 [15197-1521] A sag-srucurd modl of a singl-spcis wih dnsiy-dpndn and birh pulss LI

More information

Unbalanced Panel Data Models

Unbalanced Panel Data Models Ubalacd Pal Data odls Chaptr 9 from Baltag: Ecoomtrc Aalyss of Pal Data 5 by Adrás alascs 4448 troducto balacd or complt pals: a pal data st whr data/obsrvatos ar avalabl for all crosssctoal uts th tr

More information

CONTROL DESIGN FOR SET POINT TRACKING

CONTROL DESIGN FOR SET POINT TRACKING Chapter 5 CONTROL DESIGN FOR SET POINT TRACKING In this chapter, we extend the pole placement, observer-based output feedback design to solve tracking problems. By tracking we mean that the output is commanded

More information

Wave Superposition Principle

Wave Superposition Principle Physcs 36: Was Lcur 5 /7/8 Wa Suroson Prncl I s qu a common suaon for wo or mor was o arr a h sam on n sac or o xs oghr along h sam drcon. W wll consdr oday sral moran cass of h combnd ffcs of wo or mor

More information

Chapter 6: AC Circuits

Chapter 6: AC Circuits Chaper 6: AC Crcus Chaper 6: Oulne Phasors and he AC Seady Sae AC Crcus A sable, lnear crcu operang n he seady sae wh snusodal excaon (.e., snusodal seady sae. Complee response forced response naural response.

More information

Response of LTI Systems to Complex Exponentials

Response of LTI Systems to Complex Exponentials 3 Fourir sris coiuous-im Rspos of LI Sysms o Complx Expoials Ouli Cosidr a LI sysm wih h ui impuls rspos Suppos h ipu sigal is a complx xpoial s x s is a complx umbr, xz zis a complx umbr h or h h w will

More information

EE243 Advanced Electromagnetic Theory Lec # 10: Poynting s Theorem, Time- Harmonic EM Fields

EE243 Advanced Electromagnetic Theory Lec # 10: Poynting s Theorem, Time- Harmonic EM Fields Appl M Fall 6 Nuruhr Lcur # r 9/6/6 4 Avanc lcromagnc Thory Lc # : Poynng s Thorm Tm- armonc M Fls Poynng s Thorm Consrvaon o nrgy an momnum Poynng s Thorm or Lnar sprsv Ma Poynng s Thorm or Tm-armonc

More information

Relative controllability of nonlinear systems with delays in control

Relative controllability of nonlinear systems with delays in control Relave conrollably o nonlnear sysems wh delays n conrol Jerzy Klamka Insue o Conrol Engneerng, Slesan Techncal Unversy, 44- Glwce, Poland. phone/ax : 48 32 37227, {jklamka}@a.polsl.glwce.pl Keywor: Conrollably.

More information

Finite element discretization of Laplace and Poisson equations

Finite element discretization of Laplace and Poisson equations Finit lmnt discrtization of Laplac and Poisson quations Yashwanth Tummala Tutor: Prof S.Mittal 1 Outlin Finit Elmnt Mthod for 1D Introduction to Poisson s and Laplac s Equations Finit Elmnt Mthod for 2D-Discrtization

More information

Density Matrix Description of NMR BCMB/CHEM 8190

Density Matrix Description of NMR BCMB/CHEM 8190 Densy Marx Descrpon of NMR BCMBCHEM 89 Operaors n Marx Noaon If we say wh one bass se, properes vary only because of changes n he coeffcens weghng each bass se funcon x = h< Ix > - hs s how we calculae

More information

AR(1) Process. The first-order autoregressive process, AR(1) is. where e t is WN(0, σ 2 )

AR(1) Process. The first-order autoregressive process, AR(1) is. where e t is WN(0, σ 2 ) AR() Procss Th firs-ordr auorgrssiv procss, AR() is whr is WN(0, σ ) Condiional Man and Varianc of AR() Condiional man: Condiional varianc: ) ( ) ( Ω Ω E E ) var( ) ) ( var( ) var( σ Ω Ω Ω Ω E Auocovarianc

More information

2. Transfer function. Kanazawa University Microelectronics Research Lab. Akio Kitagawa

2. Transfer function. Kanazawa University Microelectronics Research Lab. Akio Kitagawa . ransfr funion Kanazawa Univrsiy Mirolronis Rsarh Lab. Akio Kiagawa . Wavforms in mix-signal iruis Configuraion of mix-signal sysm x Digial o Analog Analog o Digial Anialiasing Digial moohing Filr Prossor

More information

Dealing with quantitative data and problem solving life is a story problem! Attacking Quantitative Problems

Dealing with quantitative data and problem solving life is a story problem! Attacking Quantitative Problems Daling with quantitati data and problm soling lif is a story problm! A larg portion of scinc inols quantitati data that has both alu and units. Units can sa your butt! Nd handl on mtric prfixs Dimnsional

More information

DYNAMICS and CONTROL

DYNAMICS and CONTROL DYNAMICS an CONTROL Mol IV(I) IV(II) Conrol Sysms Dsign Conrol sysm aramrs Prsn by Pro Albros Profssor of Sysms Enginring an Conrol - UPV Mols: Examls of sysms an signals Mols of sysms an signals Conroll

More information

EEE582 Homework Problems

EEE582 Homework Problems EEE582 Homework Problems HW. Write a state-space realization of the linearized model for the cruise control system around speeds v = 4 (Section.3, http://tsakalis.faculty.asu.edu/notes/models.pdf). Use

More information

Searching for pairing interactions with coherent charge fluctuations spectroscopy

Searching for pairing interactions with coherent charge fluctuations spectroscopy Sarchng for parng nracons wh cohrn charg flucuaons spcroscopy J. Lornzana ISC-CNR, Sapnza, Unvrsy of Rom B. Mansar, A. Mann, A. Odh, M. Scaronglla, M. Chrgu, F. Carbon EPFL, Lausann Ouln Raman scarng Cohrn

More information

Physics 160 Lecture 3. R. Johnson April 6, 2015

Physics 160 Lecture 3. R. Johnson April 6, 2015 Physics 6 Lcur 3 R. Johnson April 6, 5 RC Circui (Low-Pass Filr This is h sam RC circui w lookd a arlir h im doma, bu hr w ar rsd h frquncy rspons. So w pu a s wav sad of a sp funcion. whr R C RC Complx

More information

Ordinary Differential Equations in Neuroscience with Matlab examples. Aim 1- Gain understanding of how to set up and solve ODE s

Ordinary Differential Equations in Neuroscience with Matlab examples. Aim 1- Gain understanding of how to set up and solve ODE s Ordnary Dfferenal Equaons n Neuroscence wh Malab eamples. Am - Gan undersandng of how o se up and solve ODE s Am Undersand how o se up an solve a smple eample of he Hebb rule n D Our goal a end of class

More information