0 () t and an equation

Size: px
Start display at page:

Download "0 () t and an equation"

Transcription

1 DECETRALIZED ODEL REFERECE ADAPTIVE PRECIZE COTROL OF COPLEX OBJECTS S.D. Zemyakov, V.Yu. Rutkovsky, V.. Gumov ad V.. Sukhaov Isttute of Cotro Scece by V.A. Trapezkov Russa Academy of Scece 65 Profsoyuzaya, 7997, oscow, Russa Te: 007 (950) ; e-ma: Abstract: Ths paper deas wth the cotro probem of compex obects o the base of the mode referece adaptve prcpe. For decomposto ad precse cotro ew agorthms are dscovered. A space robotc modue s cosdered as the exampe of a compex obect. Computer smuato demostrates good resuts. Key words: Compex Obect, Decomposto, ode Referece Adaptve Cotro, Lyapuov Fucto, Smuato. ITRODUCTIO As a compex we cosder a obect wth some tercoected subsystems (Voroov, 985; Šak, 99). A mathematca mode () of such a obect s usuay oear ad ostatoary oe. Sythess of cotro agorthms for such a obect s ot a smpe probem. Usua f ot a sge method for ths goa s decomposto ad aggregato (Voroov, 985; Šak, 99). The decomposto coud be reazed o physca or mathematca prcpes (Šak, 99). For every subsystem a oca good cotro ( ay sese) s dscovered provded that tercoectos are mssg. The t s ecessary to prove that the good behavor of the system o the whoe takes pace (Voroov, 965; Šak, 99). I ths paper we use other a approach: for every subsystem a compoet of tercoectos s seected ad compesated o the base of adaptve cotro. ore correcty the approach cudes the use of computer aded cotro (Zemyakov ad Rutkovsky, 004); of a programmed adaptve cotro; of a mode referece adaptve cotro (Zemyakov ad Rutkovsky, 966; Pertrov, et a., 980). A oca adaptve cotro system for a obect subsystem coud be sytheszed o the base of tradtoa agorthms for a mode referece adaptve approach. I resut the adaptve system w be oear ad ostatoary. It s kow that for such a system s dffcut to suppy good dyamcs ot speakg about precse cotro (Zemyakov ad Rutkovsky, 966; Pertrov, et a., 980). So ths paper we derve ew otradtoa agorthms for a system wth referece mode that are capabe to guaratee the desered precse cotro.. PROBLE STATEET Cosder a compex obect (Zemyakov ad Rutkovsky, 004) descrbed by the dffereta equato T A( qq ) + qd s( qq ) es Sq ( ) () s q R, s D ( q) ( d ( q)) s d, t R, Aq ( ) ( a ( q)) 0 a ( q) a ( q) a ( q) + qt q qs (, ts,, ). s s st t t >, Durg the obect s operatg matrces A(q), D s(q), S(q) (s, ) are kow (Zemyakov ad Rutkovsky, 004); vectors q q(), t q q () t are measurabe. For every q (, ) there exsts a fucto q 0 () t ad a equato

2 + kq kq 0 () t, () the fucto q 0 () t ad the umbers k > 0, d > 0 are prescrbed advace. The probem: It s ecessary to dscover cotro agorthms tqq (,, ) that guaratees the moto (). 3. DECOPOSITIO OF A OBJECT ATHEATICAL ODEL Let a obect wth () s decomposed to subsystems accordg to the physca prcpe (Šak, 99) A ( qq ) + ( qq, ) S ( q ) (, ), A ( q ) > 0, q, vectors q, T q ( q, q,, q ), T (,,, ). are compoets of Dmesos of the vectors q, (, ) are equa ad. The for ay subsystem coud be wrtte the A ( qq ) S ( q ) + F( tqqq,,, ) (, ), A ( q) R, A ( q ) > 0, F () S () q A ( q) ( q, q ) R, (3). 4. ATHEATICAL ODEL OF A SUBSYSTE WITH ACTUATORS Dfferet subsystems coud have actuators of dfferet ature. I ths paper we cosder oy dc motors as actuators. The of dc motor s we kow (Krutko, 99) J, d r ( r) k k k τ + d d m m ω u rq R R (, ;, ), (4) s the umber of subsystems wth dc motor actuators. d I (4) s a movg momet of a motor; s a momet for a oad rotato; r s a reducto coeffcet; J, R, τ, km, k ω are motor costructve parameters; u are cotro agorthms to be dscovered. Let the of a obect subsystem wth dc motor actuators has the (3). To the equato (3) t s ecessary to add the equatos for actuators (4) preseted matrx d A R( ), d d τ + ρu β q (5) (, ), A dag ( J ( r ), J ( r ),..., J ( r ) ), R dag r r r (,,..., ), d T d d d ( ) (,,..., ), T ( ) (,,..., ), dag τ ( τ, τ,..., τ ), k m k k m m ρ dag(,,..., ), R R R k mk k k ω kmkω m ω dag(,,..., ) R R R β. From (3) ad (5) the of a subsystem movemet together wth actuators accepts the

3 [ A ( q) + S ( q) A ] d S ( qr ) + F( tqqq,,, ), d d τ + ρu β q, (, ). (6) For cotro agorthms sythess t s possbe (Krutko, 99) to take the codto From the equato (0) we see that f t s vad the equaty [ f ( t, q,, q) + S ] 0 () the the movemet of a subsystem wth umber s decomposed o separate subsubsystems wth τ ad to smpfy the system (6) to the [ A ( q) + S ( q) A ] S ( q) R ρ U S ( qr ) β q + F( tqqq,,, ). 0 (7) + k q k q 0 () t ( ),. () From () ad () we see that the cotro agorthm (9) gves the probem souto f the equaty () takes pace. 5. ADAPTIVE PROGRAED COTROL OF A OBJECT SUBSYSTE The of a obect subsystem wth dc motor actuators (7) coud be preseted the [ A ( q) + S ( q) A ] S ( q) R ρ U + + f ( tqqq,,, ), f tqqq A q S qa [ F( tqqq,,, ) S ( qr ) β q ]. (,,, ) [ ( ) + ( ) ] The codto det( A ( q) + S ( q) A ) 0 (8) 6. ODEL REFERECE ADAPTIVE COTROL Beow we w try to hod up the equaty () o the base of mode referece adaptve cotro (Pertrov, et a., 980). Adaptve agorthms w ot be tradtoa as (Zemyakov ad Rutkovsky, 966; Pertrov, et a., 980) but more costructve for precse cotro. If the equaty () does ot take pace tha the equato () takes the + k q k q 0 () t + f () t + S ( ),, f ( t) f ( tqqq,,, ). Let us choose a referece mode the (3) s usuay rght. Let us take a cotro agorthm the U ( S ( q) R ρ ) [ A ( q) + S ( q) A ] + 0 [ K ( q q ) D q S ], ( q ) ( q ( t), q ( t),..., q ( t)), 0 T (,,..., ), (,,..., ), T. K dag k k k D dag d d d ( S ) ( S, S,..., S ) Equates (8) ad (9) together gve a equato + D q + K q K q 0 () t + + [ f ( tqqq,,, ) + S]. (9) (0) + k q k q 0 () t m m m (, ) ad wth the equaty ε q q m (4) we receve from (3) ad (4) the equato wth respect to the error ε the ε + d ε + k ε [ f ( t) + S ] Wth otato ( ),. (5) 3

4 ε x, ε x f () t + S y, f () t µ (), t ψ,, S equato (5) coud be wrtte the matrx x Ax + β, y ψ + µ (), t ( ) T (, T x x x), ( β ) (0 y ), 0 A. k d (6) We w try to choose a agorthm for S purposefu varato from the codto of the asymptotca covergece of the system (6) movemet wth respect to the movemet x 0, y 0. For ths goa we take a Lyapuov fucto the V ( x, y ) κ ( x ) P ( x ) ( y ) T + (7) P s a postve defte matrx, κ cost > 0. The dervatve of V (, x y ) wth respect to tme aog a souto of the system (6) s the equaty T V ( x, y ) κ ( x ) Q ( x ) + + y [ σ + µ ( t) + ψ ], (8) Q s the prescrbed egatve defte matrx, σ ( ), p x + p x prk are eemets of the matrx P ( p ) ( r, k,). rk For a aaytca resut we suppose that the sg of the coordate y s kow. Reay t s possbe oy to approach to ths assumpto by dfferet way. I the paper we do ot cocetrate o ths questo. Oe smpe possbe way w be accepted for smuato. The we choose the desred agorthm the ψ σ ksgy ( ), (9) k > 0. We suppose that the equaty k > µ () t takes pace. The we have the resut V ( x, y ) > 0, V ( x, y ) < 0 whch esure the souto of the probem. 7. SIULATIO RESULTS For smuato we cosder a space robotc modue (SR) (Zemyakov ad Rutkovsky, 004) as a compex obect wth (). As a mechaca obect SR has a bg umber of degrees of freedom. of a SR s oear mutcoected ostatoary oe. Automatc cotro by such a obect s ot a smpe probem. At the same tme SR operatg demads precse dyamc accuracy to provde for ts safety, for the safety of obects wth whch t teracts. O the base of physca prcpe (Šak, 99) the of SR coud be dvded o two tercoected subsystems: subsystem for the carryg body ad subsystem for mapuators (Zemyakov ad Rutkovsky, 004). Cotro agorthms for the carryg body subsystem coud be sytheszed o the base of rey cotro, Potryag maxmum prcpe ad the drect Lyapuov method (Zemyakov ad Rutkovsky, 004). Here we w try to sythesze cotro agorthms for the mapuators subsystem o the base of adaptve cotro (Zemyakov ad Rutkovsky, 966; Pertrov, et a., 980). We assume that the decomposto probem s soved ad ow t s ecessary to show that agorthms (9) reay provde precse cotro of q () t wth respect to q 0 () t (). Let k 0.5 ad d 0.7 (3), Q (8). The P (7) ad hece σ (0.x x ) (8). 4

5 Let q 0 ( t) s(0.5 t). I fg..a uder the umber we see kq t ad uder the umber 0 () 0 kq () t + f () t. The term f () t dsturbs 0 () kq t sgfcaty. I fg..b uder umbers ad respectvey we see q () t ad qm () t uder codto S () t 0. Of course wthout adaptato fg..b the dfferece ε () () t q t qm() t s sgfcat. I fg..c S () t 0. We see that q () t practcay cocdes wth q () t. Fg. shows the same resut for aother fucto q 0 () t. a m Compex Cotro Systems. auka, oscow, (Russa). Šak D., (99). Decetrazed Cotro of Compex Systems. Academc Press, Ic., ew York,. Zemyakov S.D. ad Rutkovsky V.Yu. (004). Computer Aded odeg ad Aaytca Sythess of Cotro Agorthms for a Spacecraft wth Dscretey chagg Structure. I: Proc. of the 6-th IFAC Symposum o Aut. Cotro Space. State Uversty of Aerospace Istrumetato. Sat-Petersburg. Russa. Zemyakov S.D. ad Rutkovsky V.Yu. (966). Sythess of ode Referece Adaptve Cotro System. I: Automatka ad Teemechaka. 3. P auka, oscow (Russa). Petrov B.., Rutkovsky V.Yu. ad Zemyakov S.D. (980). Adaptve Coordate-Parametrc Cotro of ostatoary Obects. auka, oscow (Russa). Krutko P.D. (99). Cotro of Robot Actuators. auka, oscow (Russa). b c Fg.. a b c Fg.. ACKOWLEDGEET The work reported ths paper s a cotrbuto to proect fuded by Russa Foudato for Basc research for whch authors are gratefu. REFERECES Voroov A.A., (985).Itroducto to Dyamcs of 5

A stopping criterion for Richardson s extrapolation scheme. under finite digit arithmetic.

A stopping criterion for Richardson s extrapolation scheme. under finite digit arithmetic. A stoppg crtero for cardso s extrapoato sceme uder fte dgt artmetc MAKOO MUOFUSHI ad HIEKO NAGASAKA epartmet of Lbera Arts ad Sceces Poytecc Uversty 4-1-1 Hasmotoda,Sagamara,Kaagawa 229-1196 JAPAN Abstract:

More information

Functions of Random Variables

Functions of Random Variables Fuctos of Radom Varables Chapter Fve Fuctos of Radom Varables 5. Itroducto A geeral egeerg aalyss model s show Fg. 5.. The model output (respose) cotas the performaces of a system or product, such as weght,

More information

ANALYSIS ON THE NATURE OF THE BASIC EQUATIONS IN SYNERGETIC INTER-REPRESENTATION NETWORK

ANALYSIS ON THE NATURE OF THE BASIC EQUATIONS IN SYNERGETIC INTER-REPRESENTATION NETWORK Far East Joural of Appled Mathematcs Volume, Number, 2008, Pages Ths paper s avalable ole at http://www.pphm.com 2008 Pushpa Publshg House ANALYSIS ON THE NATURE OF THE ASI EQUATIONS IN SYNERGETI INTER-REPRESENTATION

More information

ON THE MOTION OF PLANAR BARS SYSTEMS WITH CLEARANCES IN JOINTS

ON THE MOTION OF PLANAR BARS SYSTEMS WITH CLEARANCES IN JOINTS ON THE MOTION OF PLANAR BARS SYSTEMS WITH CLEARANCES IN JOINTS Şl uv dr g Ja-Crsta GRIGORE, Uverstatea d Pteşt, strtîrgu dvale Nr Prof uv dr g Ncolae PANDREA, Uverstatea d Pteşt, strtîrgu dvale Nr Cof

More information

Application of Generating Functions to the Theory of Success Runs

Application of Generating Functions to the Theory of Success Runs Aled Mathematcal Sceces, Vol. 10, 2016, o. 50, 2491-2495 HIKARI Ltd, www.m-hkar.com htt://dx.do.org/10.12988/ams.2016.66197 Alcato of Geeratg Fuctos to the Theory of Success Rus B.M. Bekker, O.A. Ivaov

More information

A Remark on the Uniform Convergence of Some Sequences of Functions

A Remark on the Uniform Convergence of Some Sequences of Functions Advaces Pure Mathematcs 05 5 57-533 Publshed Ole July 05 ScRes. http://www.scrp.org/joural/apm http://dx.do.org/0.436/apm.05.59048 A Remark o the Uform Covergece of Some Sequeces of Fuctos Guy Degla Isttut

More information

Newton s Power Flow algorithm

Newton s Power Flow algorithm Power Egeerg - Egll Beedt Hresso ewto s Power Flow algorthm Power Egeerg - Egll Beedt Hresso The ewto s Method of Power Flow 2 Calculatos. For the referece bus #, we set : V = p.u. ad δ = 0 For all other

More information

Multiple Choice Test. Chapter Adequacy of Models for Regression

Multiple Choice Test. Chapter Adequacy of Models for Regression Multple Choce Test Chapter 06.0 Adequac of Models for Regresso. For a lear regresso model to be cosdered adequate, the percetage of scaled resduals that eed to be the rage [-,] s greater tha or equal to

More information

Q-analogue of a Linear Transformation Preserving Log-concavity

Q-analogue of a Linear Transformation Preserving Log-concavity Iteratoal Joural of Algebra, Vol. 1, 2007, o. 2, 87-94 Q-aalogue of a Lear Trasformato Preservg Log-cocavty Daozhog Luo Departmet of Mathematcs, Huaqao Uversty Quazhou, Fua 362021, P. R. Cha ldzblue@163.com

More information

RECURSIVE FORMULATION FOR MULTIBODY DYNAMICS

RECURSIVE FORMULATION FOR MULTIBODY DYNAMICS ultbody Dyamcs Lecture Uversty of okyo, Japa Dec. 8, 25 RECURSIVE FORULAION FOR ULIODY DYNAICS Lecturer: Sug-Soo m Professor, Dept. of echatrocs Egeerg, orea Vstg Professor, Ceter for Collaboratve Research

More information

ε. Therefore, the estimate

ε. Therefore, the estimate Suggested Aswers, Problem Set 3 ECON 333 Da Hugerma. Ths s ot a very good dea. We kow from the secod FOC problem b) that ( ) SSE / = y x x = ( ) Whch ca be reduced to read y x x = ε x = ( ) The OLS model

More information

Fourth Order Four-Stage Diagonally Implicit Runge-Kutta Method for Linear Ordinary Differential Equations ABSTRACT INTRODUCTION

Fourth Order Four-Stage Diagonally Implicit Runge-Kutta Method for Linear Ordinary Differential Equations ABSTRACT INTRODUCTION Malasa Joural of Mathematcal Sceces (): 95-05 (00) Fourth Order Four-Stage Dagoall Implct Ruge-Kutta Method for Lear Ordar Dfferetal Equatos Nur Izzat Che Jawas, Fudzah Ismal, Mohamed Sulema, 3 Azm Jaafar

More information

Can we take the Mysticism Out of the Pearson Coefficient of Linear Correlation?

Can we take the Mysticism Out of the Pearson Coefficient of Linear Correlation? Ca we tae the Mstcsm Out of the Pearso Coeffcet of Lear Correlato? Itroducto As the ttle of ths tutoral dcates, our purpose s to egeder a clear uderstadg of the Pearso coeffcet of lear correlato studets

More information

New Bounds using the Solution of the Discrete Lyapunov Matrix Equation

New Bounds using the Solution of the Discrete Lyapunov Matrix Equation Iteratoa Joura of Cotro, Automato, ad Systems Vo., No. 4, December 2003 459 New Bouds usg the Souto of the Dscrete Lyapuov Matrx Euato Dog-G Lee, Gwag-Hee Heo, ad Jog-Myug Woo Abstract: I ths paper, ew

More information

1 Lyapunov Stability Theory

1 Lyapunov Stability Theory Lyapuov Stablty heory I ths secto we cosder proofs of stablty of equlbra of autoomous systems. hs s stadard theory for olear systems, ad oe of the most mportat tools the aalyss of olear systems. It may

More information

TESTS BASED ON MAXIMUM LIKELIHOOD

TESTS BASED ON MAXIMUM LIKELIHOOD ESE 5 Toy E. Smth. The Basc Example. TESTS BASED ON MAXIMUM LIKELIHOOD To llustrate the propertes of maxmum lkelhood estmates ad tests, we cosder the smplest possble case of estmatg the mea of the ormal

More information

Decomposition of Hadamard Matrices

Decomposition of Hadamard Matrices Chapter 7 Decomposto of Hadamard Matrces We hae see Chapter that Hadamard s orgal costructo of Hadamard matrces states that the Kroecer product of Hadamard matrces of orders m ad s a Hadamard matrx of

More information

Coding Theorems on New Fuzzy Information Theory of Order α and Type β

Coding Theorems on New Fuzzy Information Theory of Order α and Type β Progress Noear yamcs ad Chaos Vo 6, No, 28, -9 ISSN: 232 9238 oe Pubshed o 8 February 28 wwwresearchmathscorg OI: http://ddoorg/22457/pdacv6a Progress Codg Theorems o New Fuzzy Iormato Theory o Order ad

More information

Lecture 07: Poles and Zeros

Lecture 07: Poles and Zeros Lecture 07: Poles ad Zeros Defto of poles ad zeros The trasfer fucto provdes a bass for determg mportat system respose characterstcs wthout solvg the complete dfferetal equato. As defed, the trasfer fucto

More information

ENGI 4421 Propagation of Error Page 8-01

ENGI 4421 Propagation of Error Page 8-01 ENGI 441 Propagato of Error Page 8-01 Propagato of Error [Navd Chapter 3; ot Devore] Ay realstc measuremet procedure cotas error. Ay calculatos based o that measuremet wll therefore also cota a error.

More information

Extreme Value Theory: An Introduction

Extreme Value Theory: An Introduction (correcto d Extreme Value Theory: A Itroducto by Laures de Haa ad Aa Ferrera Wth ths webpage the authors ted to form the readers of errors or mstakes foud the book after publcato. We also gve extesos for

More information

Finsler Geometry & Cosmological constants

Finsler Geometry & Cosmological constants Avaabe oe at www.peaaresearchbrary.com Peaa esearch Lbrary Advaces Apped Scece esearch, 0, (6):44-48 Fser Geometry & Cosmooca costats. K. Mshra ad Aruesh Padey ISSN: 0976-860 CODEN (USA): AASFC Departmet

More information

STK4011 and STK9011 Autumn 2016

STK4011 and STK9011 Autumn 2016 STK4 ad STK9 Autum 6 Pot estmato Covers (most of the followg materal from chapter 7: Secto 7.: pages 3-3 Secto 7..: pages 3-33 Secto 7..: pages 35-3 Secto 7..3: pages 34-35 Secto 7.3.: pages 33-33 Secto

More information

On the Modeling and Simulation of Collision and Collision-Free Motion for Planar Robotic Arm Galia V. Tzvetkova

On the Modeling and Simulation of Collision and Collision-Free Motion for Planar Robotic Arm Galia V. Tzvetkova Iteratoal Joural of Egeerg Research & Scece (IJOER [Vol-, Issue-9, December- 25] O the Modelg ad Smulato of Collso ad Collso-Free Moto for Plaar Robotc Arm Gala V. Tzvetova Isttute of mechacs, Bulgara

More information

Consensus Control for a Class of High Order System via Sliding Mode Control

Consensus Control for a Class of High Order System via Sliding Mode Control Cosesus Cotrol for a Class of Hgh Order System va Sldg Mode Cotrol Chagb L, Y He, ad Aguo Wu School of Electrcal ad Automato Egeerg, Taj Uversty, Taj, Cha, 300072 Abstract. I ths paper, cosesus problem

More information

Solving Constrained Flow-Shop Scheduling. Problems with Three Machines

Solving Constrained Flow-Shop Scheduling. Problems with Three Machines It J Cotemp Math Sceces, Vol 5, 2010, o 19, 921-929 Solvg Costraed Flow-Shop Schedulg Problems wth Three Maches P Pada ad P Rajedra Departmet of Mathematcs, School of Advaced Sceces, VIT Uversty, Vellore-632

More information

Dr. Shalabh Department of Mathematics and Statistics Indian Institute of Technology Kanpur

Dr. Shalabh Department of Mathematics and Statistics Indian Institute of Technology Kanpur Aalyss of Varace ad Desg of Exermets-I MODULE II LECTURE - GENERAL LINEAR HYPOTHESIS AND ANALYSIS OF VARIANCE Dr Shalabh Deartmet of Mathematcs ad Statstcs Ida Isttute of Techology Kaur Tukey s rocedure

More information

A tighter lower bound on the circuit size of the hardest Boolean functions

A tighter lower bound on the circuit size of the hardest Boolean functions Electroc Colloquum o Computatoal Complexty, Report No. 86 2011) A tghter lower boud o the crcut sze of the hardest Boolea fuctos Masak Yamamoto Abstract I [IPL2005], Fradse ad Mlterse mproved bouds o the

More information

Introduction to local (nonparametric) density estimation. methods

Introduction to local (nonparametric) density estimation. methods Itroducto to local (oparametrc) desty estmato methods A slecture by Yu Lu for ECE 66 Sprg 014 1. Itroducto Ths slecture troduces two local desty estmato methods whch are Parze desty estmato ad k-earest

More information

Point Estimation: definition of estimators

Point Estimation: definition of estimators Pot Estmato: defto of estmators Pot estmator: ay fucto W (X,..., X ) of a data sample. The exercse of pot estmato s to use partcular fuctos of the data order to estmate certa ukow populato parameters.

More information

A New Method for Solving Fuzzy Linear. Programming by Solving Linear Programming

A New Method for Solving Fuzzy Linear. Programming by Solving Linear Programming ppled Matheatcal Sceces Vol 008 o 50 7-80 New Method for Solvg Fuzzy Lear Prograg by Solvg Lear Prograg S H Nasser a Departet of Matheatcs Faculty of Basc Sceces Mazadara Uversty Babolsar Ira b The Research

More information

Application of Legendre Bernstein basis transformations to degree elevation and degree reduction

Application of Legendre Bernstein basis transformations to degree elevation and degree reduction Computer Aded Geometrc Desg 9 79 78 www.elsever.com/locate/cagd Applcato of Legedre Berste bass trasformatos to degree elevato ad degree reducto Byug-Gook Lee a Yubeom Park b Jaechl Yoo c a Dvso of Iteret

More information

{ }{ ( )} (, ) = ( ) ( ) ( ) Chapter 14 Exercises in Sampling Theory. Exercise 1 (Simple random sampling): Solution:

{ }{ ( )} (, ) = ( ) ( ) ( ) Chapter 14 Exercises in Sampling Theory. Exercise 1 (Simple random sampling): Solution: Chapter 4 Exercses Samplg Theory Exercse (Smple radom samplg: Let there be two correlated radom varables X ad A sample of sze s draw from a populato by smple radom samplg wthout replacemet The observed

More information

Rademacher Complexity. Examples

Rademacher Complexity. Examples Algorthmc Foudatos of Learg Lecture 3 Rademacher Complexty. Examples Lecturer: Patrck Rebesch Verso: October 16th 018 3.1 Itroducto I the last lecture we troduced the oto of Rademacher complexty ad showed

More information

CS5620 Intro to Computer Graphics

CS5620 Intro to Computer Graphics CS56 Itro to Computer Graphcs Geometrc Modelg art II Geometrc Modelg II hyscal Sples Curve desg pre-computers Cubc Sples Stadard sple put set of pots { } =, No dervatves specfed as put Iterpolate by cubc

More information

CSE 5526: Introduction to Neural Networks Linear Regression

CSE 5526: Introduction to Neural Networks Linear Regression CSE 556: Itroducto to Neural Netorks Lear Regresso Part II 1 Problem statemet Part II Problem statemet Part II 3 Lear regresso th oe varable Gve a set of N pars of data , appromate d by a lear fucto

More information

2/20/2013. Topics. Power Flow Part 1 Text: Power Transmission. Power Transmission. Power Transmission. Power Transmission

2/20/2013. Topics. Power Flow Part 1 Text: Power Transmission. Power Transmission. Power Transmission. Power Transmission /0/0 Topcs Power Flow Part Text: 0-0. Power Trassso Revsted Power Flow Equatos Power Flow Proble Stateet ECEGR 45 Power Systes Power Trassso Power Trassso Recall that for a short trassso le, the power

More information

Part 4b Asymptotic Results for MRR2 using PRESS. Recall that the PRESS statistic is a special type of cross validation procedure (see Allen (1971))

Part 4b Asymptotic Results for MRR2 using PRESS. Recall that the PRESS statistic is a special type of cross validation procedure (see Allen (1971)) art 4b Asymptotc Results for MRR usg RESS Recall that the RESS statstc s a specal type of cross valdato procedure (see Alle (97)) partcular to the regresso problem ad volves fdg Y $,, the estmate at the

More information

ENGI 3423 Simple Linear Regression Page 12-01

ENGI 3423 Simple Linear Regression Page 12-01 ENGI 343 mple Lear Regresso Page - mple Lear Regresso ometmes a expermet s set up where the expermeter has cotrol over the values of oe or more varables X ad measures the resultg values of aother varable

More information

SUBCLASS OF HARMONIC UNIVALENT FUNCTIONS ASSOCIATED WITH SALAGEAN DERIVATIVE. Sayali S. Joshi

SUBCLASS OF HARMONIC UNIVALENT FUNCTIONS ASSOCIATED WITH SALAGEAN DERIVATIVE. Sayali S. Joshi Faculty of Sceces ad Matheatcs, Uversty of Nš, Serba Avalable at: http://wwwpfacyu/float Float 3:3 (009), 303 309 DOI:098/FIL0903303J SUBCLASS OF ARMONIC UNIVALENT FUNCTIONS ASSOCIATED WIT SALAGEAN DERIVATIVE

More information

Research on Complex Networks Control Based on Fuzzy Integral Sliding Theory

Research on Complex Networks Control Based on Fuzzy Integral Sliding Theory Advanced Scence and Technoogy Letters Vo.83 (ISA 205), pp.60-65 http://dx.do.org/0.4257/ast.205.83.2 Research on Compex etworks Contro Based on Fuzzy Integra Sdng Theory Dongsheng Yang, Bngqng L, 2, He

More information

EVALUATION OF FUNCTIONAL INTEGRALS BY MEANS OF A SERIES AND THE METHOD OF BOREL TRANSFORM

EVALUATION OF FUNCTIONAL INTEGRALS BY MEANS OF A SERIES AND THE METHOD OF BOREL TRANSFORM EVALUATION OF FUNCTIONAL INTEGRALS BY MEANS OF A SERIES AND THE METHOD OF BOREL TRANSFORM Jose Javer Garca Moreta Ph. D research studet at the UPV/EHU (Uversty of Basque coutry) Departmet of Theoretcal

More information

On Submanifolds of an Almost r-paracontact Riemannian Manifold Endowed with a Quarter Symmetric Metric Connection

On Submanifolds of an Almost r-paracontact Riemannian Manifold Endowed with a Quarter Symmetric Metric Connection Theoretcal Mathematcs & Applcatos vol. 4 o. 4 04-7 ISS: 79-9687 prt 79-9709 ole Scepress Ltd 04 O Submafolds of a Almost r-paracotact emaa Mafold Edowed wth a Quarter Symmetrc Metrc Coecto Mob Ahmad Abdullah.

More information

The number of observed cases The number of parameters. ith case of the dichotomous dependent variable. the ith case of the jth parameter

The number of observed cases The number of parameters. ith case of the dichotomous dependent variable. the ith case of the jth parameter LOGISTIC REGRESSION Notato Model Logstc regresso regresses a dchotomous depedet varable o a set of depedet varables. Several methods are mplemeted for selectg the depedet varables. The followg otato s

More information

d dt d d dt dt Also recall that by Taylor series, / 2 (enables use of sin instead of cos-see p.27 of A&F) dsin

d dt d d dt dt Also recall that by Taylor series, / 2 (enables use of sin instead of cos-see p.27 of A&F) dsin Learzato of the Swg Equato We wll cover sectos.5.-.6 ad begg of Secto 3.3 these otes. 1. Sgle mache-fte bus case Cosder a sgle mache coected to a fte bus, as show Fg. 1 below. E y1 V=1./_ Fg. 1 The admttace

More information

13. Parametric and Non-Parametric Uncertainties, Radial Basis Functions and Neural Network Approximations

13. Parametric and Non-Parametric Uncertainties, Radial Basis Functions and Neural Network Approximations Lecture 7 3. Parametrc ad No-Parametrc Ucertates, Radal Bass Fuctos ad Neural Network Approxmatos he parameter estmato algorthms descrbed prevous sectos were based o the assumpto that the system ucertates

More information

Arithmetic Mean and Geometric Mean

Arithmetic Mean and Geometric Mean Acta Mathematca Ntresa Vol, No, p 43 48 ISSN 453-6083 Arthmetc Mea ad Geometrc Mea Mare Varga a * Peter Mchalča b a Departmet of Mathematcs, Faculty of Natural Sceces, Costate the Phlosopher Uversty Ntra,

More information

Ahmed Elgamal. MDOF Systems & Modal Analysis

Ahmed Elgamal. MDOF Systems & Modal Analysis DOF Systems & odal Aalyss odal Aalyss (hese otes cover sectos from Ch. 0, Dyamcs of Structures, Al Chopra, Pretce Hall, 995). Refereces Dyamcs of Structures, Al K. Chopra, Pretce Hall, New Jersey, ISBN

More information

Lecture 12 APPROXIMATION OF FIRST ORDER DERIVATIVES

Lecture 12 APPROXIMATION OF FIRST ORDER DERIVATIVES FDM: Appromato of Frst Order Dervatves Lecture APPROXIMATION OF FIRST ORDER DERIVATIVES. INTRODUCTION Covectve term coservato equatos volve frst order dervatves. The smplest possble approach for dscretzato

More information

Supervised Learning! B." Neural Network Learning! Typical Artificial Neuron! Feedforward Network! Typical Artificial Neuron! Equations!

Supervised Learning! B. Neural Network Learning! Typical Artificial Neuron! Feedforward Network! Typical Artificial Neuron! Equations! Part 4B: Neura Networ earg 10/22/08 Superved earg B. Neura Networ earg Produce dered output for trag put Geeraze reaoaby appropratey to other put Good exampe: patter recogto Feedforward mutayer etwor 10/22/08

More information

Dimensionality Reduction and Learning

Dimensionality Reduction and Learning CMSC 35900 (Sprg 009) Large Scale Learg Lecture: 3 Dmesoalty Reducto ad Learg Istructors: Sham Kakade ad Greg Shakharovch L Supervsed Methods ad Dmesoalty Reducto The theme of these two lectures s that

More information

On the convergence of derivatives of Bernstein approximation

On the convergence of derivatives of Bernstein approximation O the covergece of dervatves of Berste approxmato Mchael S. Floater Abstract: By dfferetatg a remader formula of Stacu, we derve both a error boud ad a asymptotc formula for the dervatves of Berste approxmato.

More information

Analysis of Lagrange Interpolation Formula

Analysis of Lagrange Interpolation Formula P IJISET - Iteratoal Joural of Iovatve Scece, Egeerg & Techology, Vol. Issue, December 4. www.jset.com ISS 348 7968 Aalyss of Lagrage Iterpolato Formula Vjay Dahya PDepartmet of MathematcsMaharaja Surajmal

More information

Regression and the LMS Algorithm

Regression and the LMS Algorithm CSE 556: Itroducto to Neural Netorks Regresso ad the LMS Algorthm CSE 556: Regresso 1 Problem statemet CSE 556: Regresso Lear regresso th oe varable Gve a set of N pars of data {, d }, appromate d b a

More information

CHAPTER 4 RADICAL EXPRESSIONS

CHAPTER 4 RADICAL EXPRESSIONS 6 CHAPTER RADICAL EXPRESSIONS. The th Root of a Real Number A real umber a s called the th root of a real umber b f Thus, for example: s a square root of sce. s also a square root of sce ( ). s a cube

More information

Assignment 5/MATH 247/Winter Due: Friday, February 19 in class (!) (answers will be posted right after class)

Assignment 5/MATH 247/Winter Due: Friday, February 19 in class (!) (answers will be posted right after class) Assgmet 5/MATH 7/Wter 00 Due: Frday, February 9 class (!) (aswers wll be posted rght after class) As usual, there are peces of text, before the questos [], [], themselves. Recall: For the quadratc form

More information

Optimal Constants in the Rosenthal Inequality for Random Variables with Zero Odd Moments.

Optimal Constants in the Rosenthal Inequality for Random Variables with Zero Odd Moments. Optma Costats the Rosetha Iequaty for Radom Varabes wth Zero Odd Momets. The Harvard commuty has made ths artce opey avaabe. Pease share how ths access beefts you. Your story matters Ctato Ibragmov, Rustam

More information

Lecture 9: Tolerant Testing

Lecture 9: Tolerant Testing Lecture 9: Tolerat Testg Dael Kae Scrbe: Sakeerth Rao Aprl 4, 07 Abstract I ths lecture we prove a quas lear lower boud o the umber of samples eeded to do tolerat testg for L dstace. Tolerat Testg We have

More information

ROBUST OVERLAPPING DECENTRALIZED CONTROL FOR MULTI-AREA LONGITUDINAL POWER SYSTEMS

ROBUST OVERLAPPING DECENTRALIZED CONTROL FOR MULTI-AREA LONGITUDINAL POWER SYSTEMS ROBUS OVERLPPG DECERLZED COROL FOR MUL-RE LOGUDL POWER SYSEMS Xaohua L Xue-Bo Che Yuawe Jg We Wag 3.Faculty of formato Scece ad Egeerg ortheaster Uversty Sheyag 0004 Cha.School of Electroc ad formato Egeerg

More information

2.160 System Identification, Estimation, and Learning Lecture Notes No. 17 April 24, 2006

2.160 System Identification, Estimation, and Learning Lecture Notes No. 17 April 24, 2006 .6 System Idetfcato, Estmato, ad Learg Lectre Notes No. 7 Aprl 4, 6. Iformatve Expermets. Persstece of Exctato Iformatve data sets are closely related to Persstece of Exctato, a mportat cocept sed adaptve

More information

UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS

UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS Exam: ECON430 Statstcs Date of exam: Frday, December 8, 07 Grades are gve: Jauary 4, 08 Tme for exam: 0900 am 00 oo The problem set covers 5 pages Resources allowed:

More information

DEMONSTRATION AND METHOD FOR CALCULATING THE EFFICIENCY OF DIFFERENTIAL MECHANISM

DEMONSTRATION AND METHOD FOR CALCULATING THE EFFICIENCY OF DIFFERENTIAL MECHANISM DEMONSTRATION AND METHOD FOR CALCULATING THE EFFICIENCY OF DIFFERENTIAL MECHANISM Assoc. Prof. Barbu PLOSCEANU, PhD, POLITEHNICA Uversty of Bucharest, Assst. Prof. Ovdu VASILE, PhD, POLITEHNICA Uversty

More information

KURODA S METHOD FOR CONSTRUCTING CONSISTENT INPUT-OUTPUT DATA SETS. Peter J. Wilcoxen. Impact Research Centre, University of Melbourne.

KURODA S METHOD FOR CONSTRUCTING CONSISTENT INPUT-OUTPUT DATA SETS. Peter J. Wilcoxen. Impact Research Centre, University of Melbourne. KURODA S METHOD FOR CONSTRUCTING CONSISTENT INPUT-OUTPUT DATA SETS by Peter J. Wlcoxe Ipact Research Cetre, Uversty of Melboure Aprl 1989 Ths paper descrbes a ethod that ca be used to resolve cossteces

More information

A Primer on Summation Notation George H Olson, Ph. D. Doctoral Program in Educational Leadership Appalachian State University Spring 2010

A Primer on Summation Notation George H Olson, Ph. D. Doctoral Program in Educational Leadership Appalachian State University Spring 2010 Summato Operator A Prmer o Summato otato George H Olso Ph D Doctoral Program Educatoal Leadershp Appalacha State Uversty Sprg 00 The summato operator ( ) {Greek letter captal sgma} s a structo to sum over

More information

Manipulator Dynamics. Amirkabir University of Technology Computer Engineering & Information Technology Department

Manipulator Dynamics. Amirkabir University of Technology Computer Engineering & Information Technology Department Mapulator Dyamcs mrkabr Uversty of echology omputer Egeerg formato echology Departmet troducto obot arm dyamcs deals wth the mathematcal formulatos of the equatos of robot arm moto. hey are useful as:

More information

Strong Convergence of Weighted Averaged Approximants of Asymptotically Nonexpansive Mappings in Banach Spaces without Uniform Convexity

Strong Convergence of Weighted Averaged Approximants of Asymptotically Nonexpansive Mappings in Banach Spaces without Uniform Convexity BULLETIN of the MALAYSIAN MATHEMATICAL SCIENCES SOCIETY Bull. Malays. Math. Sc. Soc. () 7 (004), 5 35 Strog Covergece of Weghted Averaged Appromats of Asymptotcally Noepasve Mappgs Baach Spaces wthout

More information

GENERALIZATIONS OF CEVA S THEOREM AND APPLICATIONS

GENERALIZATIONS OF CEVA S THEOREM AND APPLICATIONS GENERLIZTIONS OF CEV S THEOREM ND PPLICTIONS Floret Smaradache Uversty of New Mexco 200 College Road Gallup, NM 87301, US E-mal: smarad@um.edu I these paragraphs oe presets three geeralzatos of the famous

More information

1 Onto functions and bijections Applications to Counting

1 Onto functions and bijections Applications to Counting 1 Oto fuctos ad bectos Applcatos to Coutg Now we move o to a ew topc. Defto 1.1 (Surecto. A fucto f : A B s sad to be surectve or oto f for each b B there s some a A so that f(a B. What are examples of

More information

On the construction of symmetric nonnegative matrix with prescribed Ritz values

On the construction of symmetric nonnegative matrix with prescribed Ritz values Joural of Lear ad Topologcal Algebra Vol. 3, No., 14, 61-66 O the costructo of symmetrc oegatve matrx wth prescrbed Rtz values A. M. Nazar a, E. Afshar b a Departmet of Mathematcs, Arak Uversty, P.O. Box

More information

III-16 G. Brief Review of Grand Orthogonality Theorem and impact on Representations (Γ i ) l i = h n = number of irreducible representations.

III-16 G. Brief Review of Grand Orthogonality Theorem and impact on Representations (Γ i ) l i = h n = number of irreducible representations. III- G. Bref evew of Grad Orthogoalty Theorem ad mpact o epresetatos ( ) GOT: h [ () m ] [ () m ] δδ δmm ll GOT puts great restrcto o form of rreducble represetato also o umber: l h umber of rreducble

More information

MMJ 1113 FINITE ELEMENT METHOD Introduction to PART I

MMJ 1113 FINITE ELEMENT METHOD Introduction to PART I MMJ FINITE EEMENT METHOD Cotut requremets Assume that the fuctos appearg uder the tegral the elemet equatos cota up to (r) th order To esure covergece N must satsf Compatblt requremet the fuctos must have

More information

Chapter 2 - Free Vibration of Multi-Degree-of-Freedom Systems - II

Chapter 2 - Free Vibration of Multi-Degree-of-Freedom Systems - II CEE49b Chapter - Free Vbrato of Mult-Degree-of-Freedom Systems - II We ca obta a approxmate soluto to the fudametal atural frequecy through a approxmate formula developed usg eergy prcples by Lord Raylegh

More information

CS475 Parallel Programming

CS475 Parallel Programming CS475 Parallel Programmg Deretato ad Itegrato Wm Bohm Colorado State Uversty Ecept as otherwse oted, the cotet o ths presetato s lcesed uder the Creatve Commos Attrbuto.5 lcese. Pheomea Physcs: heat, low,

More information

MOLECULAR VIBRATIONS

MOLECULAR VIBRATIONS MOLECULAR VIBRATIONS Here we wsh to vestgate molecular vbratos ad draw a smlarty betwee the theory of molecular vbratos ad Hückel theory. 1. Smple Harmoc Oscllator Recall that the eergy of a oe-dmesoal

More information

Journal of Mathematical Analysis and Applications

Journal of Mathematical Analysis and Applications J. Math. Aal. Appl. 365 200) 358 362 Cotets lsts avalable at SceceDrect Joural of Mathematcal Aalyss ad Applcatos www.elsever.com/locate/maa Asymptotc behavor of termedate pots the dfferetal mea value

More information

Entropy ISSN by MDPI

Entropy ISSN by MDPI Etropy 2003, 5, 233-238 Etropy ISSN 1099-4300 2003 by MDPI www.mdp.org/etropy O the Measure Etropy of Addtve Cellular Automata Hasa Aı Arts ad Sceces Faculty, Departmet of Mathematcs, Harra Uversty; 63100,

More information

THE COMPLETE ENUMERATION OF FINITE GROUPS OF THE FORM R 2 i ={R i R j ) k -i=i

THE COMPLETE ENUMERATION OF FINITE GROUPS OF THE FORM R 2 i ={R i R j ) k -i=i ENUMERATON OF FNTE GROUPS OF THE FORM R ( 2 = (RfR^'u =1. 21 THE COMPLETE ENUMERATON OF FNTE GROUPS OF THE FORM R 2 ={R R j ) k -= H. S. M. COXETER*. ths paper, we vestgate the abstract group defed by

More information

Lecture 2 - What are component and system reliability and how it can be improved?

Lecture 2 - What are component and system reliability and how it can be improved? Lecture 2 - What are compoet ad system relablty ad how t ca be mproved? Relablty s a measure of the qualty of the product over the log ru. The cocept of relablty s a exteded tme perod over whch the expected

More information

[ L] υ = (3) [ L] n. Q: What are the units of K in Eq. (3)? (Why is units placed in quotations.) What is the relationship to K in Eq. (1)?

[ L] υ = (3) [ L] n. Q: What are the units of K in Eq. (3)? (Why is units placed in quotations.) What is the relationship to K in Eq. (1)? Chem 78 Spr. M. Wes Bdg Polyomals Bdg Polyomals We ve looked at three cases of lgad bdg so far: The sgle set of depedet stes (ss[]s [ ] [ ] Multple sets of depedet stes (ms[]s, or m[]ss All or oe, or two-state

More information

Load balancing by MPLS in differentiated services networks

Load balancing by MPLS in differentiated services networks Load baacg by MPLS dfferetated servces etworks Rkka Sustava Supervsor: Professor Jora Vrtao Istructors: Ph.D. Prkko Kuusea Ph.D. Sau Aato Networkg Laboratory 6.8.2002 Thess Sear o Networkg Techoogy 1 Cotets

More information

Module 1 : The equation of continuity. Lecture 5: Conservation of Mass for each species. & Fick s Law

Module 1 : The equation of continuity. Lecture 5: Conservation of Mass for each species. & Fick s Law Module : The equato of cotuty Lecture 5: Coservato of Mass for each speces & Fck s Law NPTEL, IIT Kharagpur, Prof. Sakat Chakraborty, Departmet of Chemcal Egeerg 2 Basc Deftos I Mass Trasfer, we usually

More information

Matricial Potentiation

Matricial Potentiation Matrcal Potetato By Ezo March* ad Mart Mates** Abstract I ths short ote we troduce the potetato of matrces of the same sze. We study some smple propertes ad some example. * Emertus Professor UNSL, Sa Lus

More information

BAL-001-AB-0a Real Power Balancing Control Performance

BAL-001-AB-0a Real Power Balancing Control Performance Alberta Relablty Stadards Resource ad Demad Balacg BAL-00-AB-0a. Purpose BAL-00-AB-0a Real Power Balacg Cotrol Performace The purpose of ths relablty stadard s to mata WECC steady-state frequecy wth defed

More information

L5 Polynomial / Spline Curves

L5 Polynomial / Spline Curves L5 Polyomal / Sple Curves Cotets Coc sectos Polyomal Curves Hermte Curves Bezer Curves B-Sples No-Uform Ratoal B-Sples (NURBS) Mapulato ad Represetato of Curves Types of Curve Equatos Implct: Descrbe a

More information

Energy method in solving the problems of stability for a viscoelastic polymer rods

Energy method in solving the problems of stability for a viscoelastic polymer rods MATEC Web of Cofereces 19 51 (17) ICMTMTE 17 DOI: 1151/mateccof/171951 Eergy method sovg the probems of stabty for a vscoeastc poymer rods Serdar Yazyev 1 Mara Kozeskaya 1 Grgory Strekov 1 ad Stepa Ltvov

More information

Non-uniform Turán-type problems

Non-uniform Turán-type problems Joural of Combatoral Theory, Seres A 111 2005 106 110 wwwelsevercomlocatecta No-uform Turá-type problems DhruvMubay 1, Y Zhao 2 Departmet of Mathematcs, Statstcs, ad Computer Scece, Uversty of Illos at

More information

Almost Sure Convergence of Pair-wise NQD Random Sequence

Almost Sure Convergence of Pair-wise NQD Random Sequence www.ccseet.org/mas Moder Appled Scece Vol. 4 o. ; December 00 Almost Sure Covergece of Par-wse QD Radom Sequece Yachu Wu College of Scece Gul Uversty of Techology Gul 54004 Cha Tel: 86-37-377-6466 E-mal:

More information

MATH 371 Homework assignment 1 August 29, 2013

MATH 371 Homework assignment 1 August 29, 2013 MATH 371 Homework assgmet 1 August 29, 2013 1. Prove that f a subset S Z has a smallest elemet the t s uque ( other words, f x s a smallest elemet of S ad y s also a smallest elemet of S the x y). We kow

More information

F. Inequalities. HKAL Pure Mathematics. 進佳數學團隊 Dr. Herbert Lam 林康榮博士. [Solution] Example Basic properties

F. Inequalities. HKAL Pure Mathematics. 進佳數學團隊 Dr. Herbert Lam 林康榮博士. [Solution] Example Basic properties 進佳數學團隊 Dr. Herbert Lam 林康榮博士 HKAL Pure Mathematcs F. Ieualtes. Basc propertes Theorem Let a, b, c be real umbers. () If a b ad b c, the a c. () If a b ad c 0, the ac bc, but f a b ad c 0, the ac bc. Theorem

More information

Evaluating Polynomials

Evaluating Polynomials Uverst of Nebraska - Lcol DgtalCommos@Uverst of Nebraska - Lcol MAT Exam Expostor Papers Math the Mddle Isttute Partershp 7-7 Evaluatg Polomals Thomas J. Harrgto Uverst of Nebraska-Lcol Follow ths ad addtoal

More information

UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS

UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS Postpoed exam: ECON430 Statstcs Date of exam: Jauary 0, 0 Tme for exam: 09:00 a.m. :00 oo The problem set covers 5 pages Resources allowed: All wrtte ad prted

More information

STRONG CONSISTENCY FOR SIMPLE LINEAR EV MODEL WITH v/ -MIXING

STRONG CONSISTENCY FOR SIMPLE LINEAR EV MODEL WITH v/ -MIXING Joural of tatstcs: Advaces Theory ad Alcatos Volume 5, Number, 6, Pages 3- Avalable at htt://scetfcadvaces.co. DOI: htt://d.do.org/.864/jsata_7678 TRONG CONITENCY FOR IMPLE LINEAR EV MODEL WITH v/ -MIXING

More information

CIS 800/002 The Algorithmic Foundations of Data Privacy October 13, Lecture 9. Database Update Algorithms: Multiplicative Weights

CIS 800/002 The Algorithmic Foundations of Data Privacy October 13, Lecture 9. Database Update Algorithms: Multiplicative Weights CIS 800/002 The Algorthmc Foudatos of Data Prvacy October 13, 2011 Lecturer: Aaro Roth Lecture 9 Scrbe: Aaro Roth Database Update Algorthms: Multplcatve Weghts We ll recall aga) some deftos from last tme:

More information

Channel Models with Memory. Channel Models with Memory. Channel Models with Memory. Channel Models with Memory

Channel Models with Memory. Channel Models with Memory. Channel Models with Memory. Channel Models with Memory Chael Models wth Memory Chael Models wth Memory Hayder radha Electrcal ad Comuter Egeerg Mchga State Uversty I may ractcal etworkg scearos (cludg the Iteret ad wreless etworks), the uderlyg chaels are

More information

Block-Based Compact Thermal Modeling of Semiconductor Integrated Circuits

Block-Based Compact Thermal Modeling of Semiconductor Integrated Circuits Block-Based Compact hermal Modelg of Semcoductor Itegrated Crcuts Master s hess Defese Caddate: Jg Ba Commttee Members: Dr. Mg-Cheg Cheg Dr. Daqg Hou Dr. Robert Schllg July 27, 2009 Outle Itroducto Backgroud

More information

Beam Warming Second-Order Upwind Method

Beam Warming Second-Order Upwind Method Beam Warmg Secod-Order Upwd Method Petr Valeta Jauary 6, 015 Ths documet s a part of the assessmet work for the subject 1DRP Dfferetal Equatos o Computer lectured o FNSPE CTU Prague. Abstract Ths documet

More information

Lecture 7. Confidence Intervals and Hypothesis Tests in the Simple CLR Model

Lecture 7. Confidence Intervals and Hypothesis Tests in the Simple CLR Model Lecture 7. Cofdece Itervals ad Hypothess Tests the Smple CLR Model I lecture 6 we troduced the Classcal Lear Regresso (CLR) model that s the radom expermet of whch the data Y,,, K, are the outcomes. The

More information

Fractional Order Finite Difference Scheme For Soil Moisture Diffusion Equation And Its Applications

Fractional Order Finite Difference Scheme For Soil Moisture Diffusion Equation And Its Applications IOS Joural of Mathematcs (IOS-JM e-iss: 78-578. Volume 5, Issue 4 (Ja. - Feb. 3, PP -8 www.osrourals.org Fractoal Order Fte Dfferece Scheme For Sol Mosture Dffuso quato Ad Its Applcatos S.M.Jogdad, K.C.Takale,

More information

A Model Reduction Technique for linear Model Predictive Control for Non-linear Large Scale Distributed Systems

A Model Reduction Technique for linear Model Predictive Control for Non-linear Large Scale Distributed Systems A Model Reducto Techque for lear Model Predctve Cotrol for No-lear Large Scale Dstrbuted Systes Weguo Xe ad Costatos Theodoropoulos School of Checal Egeerg ad Aalytcal Scece Uversty of Machester, Machester

More information

Bounds for block sparse tensors

Bounds for block sparse tensors A Bouds for bock sparse tesors Oe of the ma bouds to cotro s the spectra orm of the sparse perturbato tesor S The success of the power teratos ad the mprovemet accuracy of recovery over teratve steps of

More information