Chapter3 Pattern Association & Associative Memory

Size: px
Start display at page:

Download "Chapter3 Pattern Association & Associative Memory"

Transcription

1 Cher3 Per Aoco & Aocve Memor Aocg er hch re mlr, corr, cloe roxm l, cloe ucceo emorl Aocve recll evoe oced er recll er b r of evoe/recll h comlee/ o er To e of oco. For o er d heero-oco! : relg o dffere er uo-oco : relg r of er h oher r

2 Archecure of NN ocve memor gle ler h/ou u ler o ler for bdrecol oc. Lerg lgorhm for AM Hebb lerg rule d vro grde dece Al orge cc ho m er c be remembered correcl memor covergece AM model for hum memor

3 Trg Algorhm for Smle AM Neor rucure: gle ler oe ouu ler of o-ler u d oe u ler mlr o he mle eor for clfco Ch. _ x _m _ x _m _m _m Gol of lerg: o ob e of egh _ from e of rg er r {:} uch h he led o he u ler, comued he ouu ler for ll rg r : : f T for ll

4 Smlr o hebb lerg for clfco Ch. Algorhm: bolr or br er If For ech rg mle :: cree f boh d re ON br or hve he me g bolr ll. P W { P Hebb rule The, Ied of obg W b erve ude, c be comued from he rg e b clculg he ouer roduc of d. fer ude } for ll P rg er

5 Ouer roduc. Le d be ro vecor. The for rculr rg r : Ad I volve 3 eed loo,, order of rrelev o P /* for ever rg r */ o /* for ever ro W */ o m /* for ever eleme ro */ [ ] m m m m m m T W ,... P T P W : +

6 Doe h mehod rovde good oco? Recll h rg mle fer he egh re lered or comued Al o oe ler, hoe er o he oher, e.g. M o l ucceed ech egh co ome formo from ll mle W f + + T T T P T P T W cro-l erm rcl erm

7 Prcl erm gve he oco beee d. Cro-l reree correlo beee : d oher rg r. Whe cro-l lrge, ll recll omehg oher h. T If ll re orhogol o ech oher, he, o mle oher h : corbue o he reul. There re mo orhogol vecor -dmeol ce. Cro-l cree he P cree. Ho m rbrr rg r c be ored AM? C be more h llog ome o-orhogol er hle eeg cro-l erm mll? Sorge cc more ler

8 Del Rule Smlr o h ued Adle The orgl del rule for egh ude: Exeded del rule For ouu u h dffereble cvo fuco Derved follog grde dece roch. x α _ ' f x α I J J J J IJ J J J IJ J J J IJ J J J J J IJ J J IJ x f E x f x f f E ' ' ' m E

9 me he ude rule for ouu ode BP lerg. Wor ell f S re lerl deede eve f o orhogol.

10 Exmle of heero-ocve memor Br er r : h 4 d. Tol eghed u o ouu u: _ x Acvo fuco: hrehold f _ > f _ Wegh re comued b Hebb rule um of ouer roduc of ll rg r W Trg mle: P T,, 3, 4,

11 T 3 3 T T 4 4 T W Comug he egh

12 recll: x x mlr o S d S x,,,, cl,, cl, o uffcel mlr o cl del-rule ould gve me or mlr reul.

13 Exmle of uo-ocve memor Sme heero-ocve e, exce. Ued o recll er b o or comlee vero. er comleo/er recover A gle er,,, - ored egh comued b Hebb rule ouer roduc W rg. o mg fo more o W W W W o recogzed

14 Dgol eleme ll dome he comuo he mulle er re ored P. Whe P lrge, W cloe o de mrx. Th cue ouu u, hch m o be oed er. The er correco oer lo. Relce dgol eleme b zero. W W ' W ' 3 W ' W ' rog

15 Sorge Cc # of er h c be correcl ored & reclled b eor. More er c be ored f he re o mlr o ech oher e.g., orhogol o-orhogol orhogol W W o ored correcl I W correcl reclled hree er c be All

16 Addg oe more orhogol er he egh mrx become: W The memor comleel deroed! Theorem: b eor ble o ore u o - muull orhogol M.O. bolr vecor of - dmeo, bu o uch vecor. Iforml rgume: Suoe m orhogol vecor re ored h he follog egh mrx: f zero dgol m ohere Hebb rule... m

17 m m W : comoe h he,...,,...,,..., Le r o recll oe of hem,... ce re M.O. d ce T

18 [ ] m m m Therefore, m W Whe m <, c correcl recll elf he m, ouu vecor, recll fl I ler lgebrc erm, egevecor of W, hoe correodg egevlue -m. he m, W h egevlue zero, he ol egevecor, hch rvl egevecor.

19 Ho m muull orhogol bolr vecor h gve dmeo? c be re m, here m odd eger. The mxmll: M.O. vecor Follo u queo: Wh ould be he cc of AM f ored er re o muull orhogol rdom Abl of er recover d comleo. Ho fr off er c be from ored er h ll ble o recll correc/ored er Suoe x ored er, x cloe o x, d x fxw eve cloer o x h x. Wh hould e do? Feed bc x, d hoe ero of feedbc ll led o x

20 Exmle: Ierve Auoocve Neor x,,, W I geerl: ug curre ouu u of he ex ero x l recll u xi fxi-w, I,, ul xn xk here K < N A comlee recll u : x',,, x' W,,, x" x" W 3,,, 3,,, x Ouu u re hrehold u

21 Dmc Sem: e vecor xi If N-, xn ble e fxed o fxnw fxn-w xn If xk oe of he ored er, he xk clled geue memor Ohere, xk urou memor cued b crol/erferece beee geue memore Ech fxed o geue or urou memor rcor h dffere rco b If! N-, lm-crcle, The eor ll ree xk, xk+,..xnxk he ero coue. Iero ll eveull o becue he ol umber of dc e fe 3^ f hrehold u re ued. If gmod u re ued, he em m coue evolve forever cho.

22 Dcree Hofeld Model A gle ler eor ech ode boh u d ouu u More h AM Oher lco e.g., comborl omzo Dffere form: dcree & couou Mor corbuo of Joh Hofeld o NN Treg eor dmc em Iroduce he oo of eerg fuco & rcor o NN reerch

23 Dcree Hofeld Model DHM AM Archecure: gle ler u erve boh u d ouu ode re hrehold u br or bolr egh: full coeced, mmerc, d zero dgol re exerl x u, hch m be re or erme

24 Wegh: To ore er,,, P bolr: me Hebb rule h zero dgol br: coverg o bolr he corucg W.

25 Recll Ue u vecor o recll ored vecor boo cll he lco of DHM Ech me, rdoml elec u for ude Recll Procedure.Al recll u vecor x o he eor: : x,,....whle covergece fl do..rdoml elec u.. Comue _ x +.3. Deerme cvo of Y f _ > θ f _ θ f _ < θ.4. Perodcll e for covergece.

26 Noe:. Ech u hould hve equl robbl o be eleced e.. Theorecll, o guree covergece of he recll roce, ol oe u lloed o ude cvo me durg he comuo. Hoever, he em m coverge fer f ll u re lloed o ude her cvo he me me. 3. Covergece e: curre ex 4. uull e o zero. θ x 5. e. _ x + ool.

27 Exmle: Sore oe er: br er,,, bolr couerr - gve he mew W Recll u x,,,, fr o b re rog Y eleced Y 4 eleced _ x + + _ 4 x Y,,, Y,,, + Y3 eleced _ 3 x3 + 3 Y,,, 3 3 Y eleced + _ x + Y,,, + The ored er correcl reclled

28 Covergece Al of DHM To queo:.wll Hofeld AM coverge o h gve recll u?.wll Hofeld AM coverge o he ored er h cloe o he recll u? Hofeld rovde er o he fr queo B roducg eerg fuco o h model, No fcor er o he ecod queo o fr. Eerg fuco: Noo hermo-dmc hcl em. The em h edec o move ord loer eerg e. Alo o Luov fuco. Afer Luov heorem for he bl of em of dfferel equo.

29 I geerl, he eerg fuco E, here he e of he em e me, mu f o codo. E bouded from belo E The eerg fuco defed for DHM c. E mooocll ocreg. E + E + E couou vero : E& E.5 x + θ Sho E + A +, Y eleced for ude + + Noe : + ol oe u c ude me E + E x + + θ.5 x + θ +

30 erm hch re dffere he o r re hoe volvg E, + [, + x x, θ θ ] + ce : f & + + _ < θ E + < f & + + _ > θ E + < ohere, + + E _ + + Sho E bouded from belo, ce, x, θ, re ll bouded, E bouded.

31 Comme:.Wh coverge. Ech me, E eher uchged or decree mou. E bouded from belo. There lm E m decree. Afer fe umber of e, E ll o decree o mer h u eleced for ude. eher or _ + θ.the e he em coverge ble e. Wll reur o h e fer ome mll erurbo. I clled rcor h dffere rco b 3.Error fuco of BP lerg oher exmle of eerg/luov fuco. Becue I bouded from belo E> I mooocll o-creg W ude log grde dece of E

32 P: mxmum umber of rdom er of dmeo c be ored DHM of ode Hofeld obervo: Theorecl l: Cc Al of DHM P P.5,.5 P P, log log P/ decree becue lrger led o more erferece beee ored er. Some or o modf HM o cree cc o cloe o, W red o comued b Hebb rule.

33 M O Wor: Oe oble reo for he mll cc of HM h doe o hve hdde ode. Tr feed forrd eor h hdde ler b BP o eblh er uo-ocve. Recll: feedbc he ouu o u ler, mg dmc em. Sho ll coverge, d ored er become geue memore. I c ore m more er eem O^ I er comlee/recover cbl decree he cree # of urou rcor eem o cree exoell ouu hdde u ouu hdde u Auo-oco Heero-oco ouu hdde u

34 Archecure: Bdrecol AMBAM To ler of o-ler u: X-ler, Y-ler U: dcree hrehold, coug gmod c be eher br or bolr.

35 Wegh: T W Hebb/ouer roduc Smmerc: Cover br er o bolr he corucg W Recll: P m Bdrecol, eher b o recll Y or b Y Recurre: f _,... f _ here x + _ here x _ X o recll X Ude c be eher chroou HM or chroou chge ll Y u oe me, he ll X u he ex me + m x m f x _ +,... f x _ +

36 Al dcree ce Eerg fuco: lo Luov fuco L.5 XWY m The roof mlr o DHM Hold for boh chroou d chroou ude hold for DHM ol h chroou ude, due o lerl coeco. Sorge cc: x T + YW T X ο mx, m T XWY T

Introduction to Neural Networks Computing. CMSC491N/691N, Spring 2001

Introduction to Neural Networks Computing. CMSC491N/691N, Spring 2001 Iroduco o Neurl Neorks Compug CMSC49N/69N, Sprg 00 us: cvo/oupu: f eghs: X, Y j X Noos, j s pu u, for oher us, j pu sgl here f. s he cvo fuco for j from u o u j oher books use Y f _ j j j Y j X j Y j bs:

More information

Modeling and Predicting Sequences: HMM and (may be) CRF. Amr Ahmed Feb 25

Modeling and Predicting Sequences: HMM and (may be) CRF. Amr Ahmed Feb 25 Modelg d redcg Sequeces: HMM d m be CRF Amr Ahmed 070 Feb 25 Bg cure redcg Sgle Lbel Ipu : A se of feures: - Bg of words docume - Oupu : Clss lbel - Topc of he docume - redcg Sequece of Lbels Noo Noe:

More information

BEST PATTERN OF MULTIPLE LINEAR REGRESSION

BEST PATTERN OF MULTIPLE LINEAR REGRESSION ERI COADA GERMAY GEERAL M.R. SEFAIK AIR FORCE ACADEMY ARMED FORCES ACADEMY ROMAIA SLOVAK REPUBLIC IERAIOAL COFERECE of SCIEIFIC PAPER AFASES Brov 6-8 M BES PAER OF MULIPLE LIEAR REGRESSIO Corel GABER PEROLEUM-GAS

More information

Expectation and Moments

Expectation and Moments Her Sr d Joh W. Woods robbl Sscs d Rdom Vrbles or geers 4h ed. erso duco Ic.. ISB: 978----6 Cher 4 eco d omes Secos 4. eced Vlue o Rdom Vrble 5 O he Vld o quo 4.-8 8 4. Codol ecos Codol eco s Rdom Vrble

More information

GENESIS. God makes the world

GENESIS. God makes the world GENESIS 1 Go me he or 1 I he be Go me he b heve he erh everyh hh p he y. 2 There oh o he e erh. Noh ve here, oh *o ve here. There oy e eep er over he erh. There o h. I very r. The f Spr of Go move over

More information

Laplace Transform. Definition of Laplace Transform: f(t) that satisfies The Laplace transform of f(t) is defined as.

Laplace Transform. Definition of Laplace Transform: f(t) that satisfies The Laplace transform of f(t) is defined as. Lplce Trfor The Lplce Trfor oe of he hecl ool for olvg ordry ler dfferel equo. - The hoogeeou equo d he prculr Iegrl re olved oe opero. - The Lplce rfor cover he ODE o lgerc eq. σ j ple do. I he pole o

More information

P a g e 5 1 of R e p o r t P B 4 / 0 9

P a g e 5 1 of R e p o r t P B 4 / 0 9 P a g e 5 1 of R e p o r t P B 4 / 0 9 J A R T a l s o c o n c l u d e d t h a t a l t h o u g h t h e i n t e n t o f N e l s o n s r e h a b i l i t a t i o n p l a n i s t o e n h a n c e c o n n e

More information

Chapter 2. Review of Hydrodynamics and Vector Analysis

Chapter 2. Review of Hydrodynamics and Vector Analysis her. Ree o Hdrodmcs d Vecor Alss. Tlor seres L L L L ' ' L L " " " M L L! " ' L " ' I s o he c e romed he Tlor seres. O he oher hd ' " L . osero o mss -dreco: L L IN ] OUT [mss l [mss l] mss ccmled h me

More information

13. DYNAMIC ANALYSIS USING MODE SUPERPOSITION

13. DYNAMIC ANALYSIS USING MODE SUPERPOSITION . DYAMI AALYI UIG MODE UPEPOIIO he Mode hes used o Ucoule he Dmc Equlrum Equos eed o Be he Exc Free-Vro Mode hes. EQUAIO O BE OLVED { XE "Mode hes" }{ XE "Mode ueroso Alss" }{ XE "Pece-Wse Ler Lodg" }he

More information

Science & Technologies GENERAL BIRTH-DEATH PROCESS AND SOME OF THEIR EM (EXPETATION- MAXIMATION) ALGORITHM

Science & Technologies GENERAL BIRTH-DEATH PROCESS AND SOME OF THEIR EM (EXPETATION- MAXIMATION) ALGORITHM GEERAL BIRH-EAH ROCESS A SOME OF HEIR EM EXEAIO- MAXIMAIO) ALGORIHM Il Hl, Lz Ker, Ylldr Seer Se ery o eoo,, eoo Mcedo l.hl@e.ed.; lz.er@e.ed.; ylldr_@hol.co ABSRAC Brh d deh roce coo-e Mrco ch, h odel

More information

3. REVIEW OF PROPERTIES OF EIGENVALUES AND EIGENVECTORS

3. REVIEW OF PROPERTIES OF EIGENVALUES AND EIGENVECTORS . REVIEW OF PROPERTIES OF EIGENVLUES ND EIGENVECTORS. EIGENVLUES ND EIGENVECTORS We hll ow revew ome bc fct from mtr theory. Let be mtr. clr clled egevlue of f there et ozero vector uch tht Emle: Let 9

More information

The Lucas congruence for Stirling numbers of the second kind

The Lucas congruence for Stirling numbers of the second kind ACTA ARITHMETICA XCIV 2 The Luc cogruece for Srlg umber of he ecod kd by Robero Sáchez-Peregro Pdov Iroduco The umber roduced by Srlg 7 h Mehodu dfferel [], ubequely clled Srlg umber of he fr d ecod kd,

More information

Conquering kings their titles take ANTHEM FOR CONGREGATION AND CHOIR

Conquering kings their titles take ANTHEM FOR CONGREGATION AND CHOIR Coquerg gs her es e NTHEM FOR CONGREGTION ND CHOIR I oucg hs hm-hem, whch m be cuded Servce eher s Hm or s hem, he Cogrego m be referred o he No. of he Hm whch he words pper, d ved o o sgg he 1 s, 4 h,

More information

ONE APPROACH FOR THE OPTIMIZATION OF ESTIMATES CALCULATING ALGORITHMS A.A. Dokukin

ONE APPROACH FOR THE OPTIMIZATION OF ESTIMATES CALCULATING ALGORITHMS A.A. Dokukin Iero Jor "Iforo Theore & co" Vo 463 ONE PPROH FOR THE OPTIIZTION OF ETITE UTING GORITH Do rc: I h rce he ew roch for ozo of eo ccg gorh ggeed I c e ed for fdg he correc gorh of coexy he coex of gerc roch

More information

Integral Equations and their Relationship to Differential Equations with Initial Conditions

Integral Equations and their Relationship to Differential Equations with Initial Conditions Scece Refleco SR Vol 6 wwwscecereflecocom Geerl Leers Mhemcs GLM 6 3-3 Geerl Leers Mhemcs GLM Wese: hp://wwwscecereflecocom/geerl-leers--mhemcs/ Geerl Leers Mhemcs Scece Refleco Iegrl Equos d her Reloshp

More information

African Journal of Science and Technology (AJST) Science and Engineering Series Vol. 4, No. 2, pp GENERALISED DELETION DESIGNS

African Journal of Science and Technology (AJST) Science and Engineering Series Vol. 4, No. 2, pp GENERALISED DELETION DESIGNS Af Joul of See Tehology (AJST) See Egeeg See Vol. 4, No.,. 7-79 GENERALISED DELETION DESIGNS Mhel Ku Gh Joh Wylff Ohbo Dee of Mhe, Uvey of Nob, P. O. Bo 3097, Nob, Key ABSTRACT:- I h e yel gle ele fol

More information

STOCHASTIC CALCULUS I STOCHASTIC DIFFERENTIAL EQUATION

STOCHASTIC CALCULUS I STOCHASTIC DIFFERENTIAL EQUATION The Bk of Thld Fcl Isuos Polcy Group Que Models & Fcl Egeerg Tem Fcl Mhemcs Foudo Noe 8 STOCHASTIC CALCULUS I STOCHASTIC DIFFERENTIAL EQUATION. ก Through he use of ordry d/or prl deres, ODE/PDE c rele

More information

Calculation of Effective Resonance Integrals

Calculation of Effective Resonance Integrals Clculo of ffecve Resoce egrls S.B. Borzkov FLNP JNR Du Russ Clculo of e effecve oce egrl wc cludes e rel eerg deedece of euro flux des d correco o e euro cure e smle s eeded for ccure flux deermo d euro

More information

Policy optimization. Stochastic approach

Policy optimization. Stochastic approach Polcy opmzo Sochc pproch Dcree-me Mrkov Proce Sory Mrkov ch Sochc proce over fe e e S S {.. 2 S} Oe ep ro probbly: Prob j - p j Se ro me: geomerc drbuo Prob j T p j p - 2 Dcree-me Mrkov Proce Sory corollble

More information

I I M O I S K J H G. b gb g. Chapter 8. Problem Solutions. Semiconductor Physics and Devices: Basic Principles, 3 rd edition Chapter 8

I I M O I S K J H G. b gb g. Chapter 8. Problem Solutions. Semiconductor Physics and Devices: Basic Principles, 3 rd edition Chapter 8 emcouc hyscs evces: Bsc rcles, r eo Cher 8 oluos ul rolem oluos Cher 8 rolem oluos 8. he fwr s e ex f The e ex f e e f ex () () f f f f l G e f f ex f 59.9 m 60 m 0 9. m m 8. e ex we c wre hs s e ex h

More information

Analysis of the Preference Shift of. Customer Brand Selection. and Its Matrix Structure. -Expansion to the second order lag

Analysis of the Preference Shift of. Customer Brand Selection. and Its Matrix Structure. -Expansion to the second order lag Jourl of Compuo & Modellg vol. o. 6-9 ISS: 79-76 (pr) 79-88 (ole) Scepre Ld l of he Preferece Shf of Cuomer Brd Seleco d I Mr Srucure -Epo o he ecod order lg Kuhro Teu rc I ofe oerved h coumer elec he

More information

Interval Estimation. Consider a random variable X with a mean of X. Let X be distributed as X X

Interval Estimation. Consider a random variable X with a mean of X. Let X be distributed as X X ECON 37: Ecoomercs Hypohess Tesg Iervl Esmo Wh we hve doe so fr s o udersd how we c ob esmors of ecoomcs reloshp we wsh o sudy. The queso s how comforble re we wh our esmors? We frs exme how o produce

More information

Upper Bound For Matrix Operators On Some Sequence Spaces

Upper Bound For Matrix Operators On Some Sequence Spaces Suama Uer Bou formar Oeraors Uer Bou For Mar Oeraors O Some Sequece Saces Suama Dearme of Mahemacs Gaah Maa Uersy Yogyaara 558 INDONESIA Emal: suama@ugmac masomo@yahoocom Isar D alam aer aa susa masalah

More information

Introduction to Laplace Transforms October 25, 2017

Introduction to Laplace Transforms October 25, 2017 Iroduco o Lplc Trform Ocobr 5, 7 Iroduco o Lplc Trform Lrr ro Mchcl Egrg 5 Smr Egrg l Ocobr 5, 7 Oul Rvw l cl Wh Lplc rform fo of Lplc rform Gg rform b gro Fdg rform d vr rform from bl d horm pplco o dffrl

More information

State The position of school d i e t i c i a n, a created position a t S t a t e,

State The position of school d i e t i c i a n, a created position a t S t a t e, P G E 0 E C O E G E E FRDY OCOBER 3 98 C P && + H P E H j ) ) C jj D b D x b G C E Ob 26 C Ob 6 R H E2 7 P b 2 b O j j j G C H b O P G b q b? G P P X EX E H 62 P b 79 P E R q P E x U C Ob ) E 04 D 02 P

More information

Chapter Simpson s 1/3 Rule of Integration. ( x)

Chapter Simpson s 1/3 Rule of Integration. ( x) Cper 7. Smpso s / Rule o Iegro Aer redg s per, you sould e le o. derve e ormul or Smpso s / rule o egro,. use Smpso s / rule o solve egrls,. develop e ormul or mulple-segme Smpso s / rule o egro,. use

More information

Nield- Kuznetsov Functions of the First- and Second Kind

Nield- Kuznetsov Functions of the First- and Second Kind IOSR Jourl of led Phscs IOSR-JP e-issn: 78-486.Volue 8 Issue Ver. III M. - Ju. 6 PP 47-56.osrourls.or S.M. lzhr * I. Gdour M.H. Hd + De. of Mhecs d Sscs Uvers of Ne rusc P.O. ox 55 S Joh Ne rusc CND EL

More information

Decompression diagram sampler_src (source files and makefiles) bin (binary files) --- sh (sample shells) --- input (sample input files)

Decompression diagram sampler_src (source files and makefiles) bin (binary files) --- sh (sample shells) --- input (sample input files) . Iroduco Probblsc oe-moh forecs gudce s mde b 50 esemble members mproved b Model Oupu scs (MO). scl equo s mde b usg hdcs d d observo d. We selec some prmeers for modfg forecs o use mulple regresso formul.

More information

4. Runge-Kutta Formula For Differential Equations

4. Runge-Kutta Formula For Differential Equations NCTU Deprme o Elecrcl d Compuer Egeerg 5 Sprg Course by Pro. Yo-Pg Ce. Ruge-Ku Formul For Derel Equos To solve e derel equos umerclly e mos useul ormul s clled Ruge-Ku ormul

More information

4. Runge-Kutta Formula For Differential Equations. A. Euler Formula B. Runge-Kutta Formula C. An Example for Fourth-Order Runge-Kutta Formula

4. Runge-Kutta Formula For Differential Equations. A. Euler Formula B. Runge-Kutta Formula C. An Example for Fourth-Order Runge-Kutta Formula NCTU Deprme o Elecrcl d Compuer Egeerg Seor Course By Pro. Yo-Pg Ce. Ruge-Ku Formul For Derel Equos A. Euler Formul B. Ruge-Ku Formul C. A Emple or Four-Order Ruge-Ku Formul

More information

Example: Two Stochastic Process u~u[0,1]

Example: Two Stochastic Process u~u[0,1] Co o Slo o Coco S Sh EE I Gholo h@h. ll Sochc Slo Dc Slo l h PLL c Mo o coco w h o c o Ic o Co B P o Go E A o o Po o Th h h o q o ol o oc o lco q ccc lco l Bc El: Uo Dbo Ucol Sl Ab bo col l G col G col

More information

Unscented Transformation Unscented Kalman Filter

Unscented Transformation Unscented Kalman Filter Usceed rsformo Usceed Klm Fler Usceed rcle Fler Flerg roblem Geerl roblem Seme where s he se d s he observo Flerg s he problem of sequell esmg he ses (prmeers or hdde vrbles) of ssem s se of observos become

More information

10.2 Series. , we get. which is called an infinite series ( or just a series) and is denoted, for short, by the symbol. i i n

10.2 Series. , we get. which is called an infinite series ( or just a series) and is denoted, for short, by the symbol. i i n 0. Sere I th ecto, we wll troduce ere tht wll be dcug for the ret of th chpter. Wht ere? If we dd ll term of equece, we get whch clled fte ere ( or jut ere) d deoted, for hort, by the ymbol or Doe t mke

More information

Stat 6863-Handout 5 Fundamentals of Interest July 2010, Maurice A. Geraghty

Stat 6863-Handout 5 Fundamentals of Interest July 2010, Maurice A. Geraghty S 6863-Hou 5 Fuels of Ieres July 00, Murce A. Gerghy The pror hous resse beef cl occurreces, ous, ol cls e-ulero s ro rbles. The fl copoe of he curl oel oles he ecooc ssupos such s re of reur o sses flo.

More information

1 n. w = How much information can the network store? 4. RECURRENT NETWORKS. One stored pattern, x (1) : Since the elements of the vector x (1) should

1 n. w = How much information can the network store? 4. RECURRENT NETWORKS. One stored pattern, x (1) : Since the elements of the vector x (1) should 4. RECURRENT NETWORKS Ho much formao ca he eor sore? Ths chaper preses some ypes of recurre eural eors: Frs, eors h (srog egh cosras are reaed, eemplfed by Hopfeld eors, folloed by a se of recurre eors

More information

Bipartite Matching. Matching. Bipartite Matching. Maxflow Formulation

Bipartite Matching. Matching. Bipartite Matching. Maxflow Formulation Mching Inpu: undireced grph G = (V, E). Biprie Mching Inpu: undireced, biprie grph G = (, E).. Mching Ern Myr, Hrld äcke Biprie Mching Inpu: undireced, biprie grph G = (, E). Mflow Formulion Inpu: undireced,

More information

FORCED VIBRATION of MDOF SYSTEMS

FORCED VIBRATION of MDOF SYSTEMS FORCED VIBRAION of DOF SSES he respose of a N DOF sysem s govered by he marx equao of moo: ] u C] u K] u 1 h al codos u u0 ad u u 0. hs marx equao of moo represes a sysem of N smulaeous equaos u ad s me

More information

Reinforcement Learning

Reinforcement Learning Reiforceme Corol lerig Corol polices h choose opiml cios Q lerig Covergece Chper 13 Reiforceme 1 Corol Cosider lerig o choose cios, e.g., Robo lerig o dock o bery chrger o choose cios o opimize fcory oupu

More information

CS344: Introduction to Artificial Intelligence

CS344: Introduction to Artificial Intelligence C344: Iroduco o Arfcal Iellgece Puhpa Bhaacharyya CE Dep. IIT Bombay Lecure 3 3 32 33: Forward ad bacward; Baum elch 9 h ad 2 March ad 2 d Aprl 203 Lecure 27 28 29 were o EM; dae 2 h March o 8 h March

More information

Week 8 Lecture 3: Problems 49, 50 Fourier analysis Courseware pp (don t look at French very confusing look in the Courseware instead)

Week 8 Lecture 3: Problems 49, 50 Fourier analysis Courseware pp (don t look at French very confusing look in the Courseware instead) Week 8 Lecure 3: Problems 49, 5 Fourier lysis Coursewre pp 6-7 (do look Frech very cofusig look i he Coursewre ised) Fourier lysis ivolves ddig wves d heir hrmoics, so i would hve urlly followed fer he

More information

Lecture 3 summary. C4 Lecture 3 - Jim Libby 1

Lecture 3 summary. C4 Lecture 3 - Jim Libby 1 Lecue su Fes of efeece Ivce ude sfoos oo of H wve fuco: d-fucos Eple: e e - µ µ - Agul oeu s oo geeo Eule gles Geec slos cosevo lws d Noehe s heoe C4 Lecue - Lbb Fes of efeece Cosde fe of efeece O whch

More information

Parameter Estimation and Hypothesis Testing of Two Negative Binomial Distribution Population with Missing Data

Parameter Estimation and Hypothesis Testing of Two Negative Binomial Distribution Population with Missing Data Avlble ole wwwsceceeccom Physcs Poce 0 475 480 0 Ieol Cofeece o Mecl Physcs Bomecl ee Pmee smo Hyohess es of wo Neve Boml Dsbuo Poulo wh Mss D Zhwe Zho Collee of MhemcsJl Noml UvesyS Ch zhozhwe@6com Absc

More information

The Products of Regularly Solvable Operators with Their Spectra in Direct Sum Spaces

The Products of Regularly Solvable Operators with Their Spectra in Direct Sum Spaces Advces Pure Mhemcs 3 3 45-49 h://dxdoorg/436/m3346 Pulshed Ole July 3 (h://wwwscrorg/ourl/m) he Producs of Regulrly Solvle Oerors wh her Secr Drec Sum Sces Sohy El-Syed Irhm Derme of Mhemcs Fculy of Scece

More information

The MacWilliams Identity of the Linear Codes over the Ring F p +uf p +vf p +uvf p

The MacWilliams Identity of the Linear Codes over the Ring F p +uf p +vf p +uvf p Reearch Joural of Aled Scece Eeer ad Techoloy (6): 28-282 22 ISSN: 2-6 Maxwell Scefc Orazao 22 Submed: March 26 22 Acceed: Arl 22 Publhed: Auu 5 22 The MacWllam Idey of he Lear ode over he R F +uf +vf

More information

Real-Time Systems. Example: scheduling using EDF. Feasibility analysis for EDF. Example: scheduling using EDF

Real-Time Systems. Example: scheduling using EDF. Feasibility analysis for EDF. Example: scheduling using EDF EDA/DIT6 Real-Tme Sysems, Chalmers/GU, 0/0 ecure # Updaed February, 0 Real-Tme Sysems Specfcao Problem: Assume a sysem wh asks accordg o he fgure below The mg properes of he asks are gve he able Ivesgae

More information

Machine Learning. Hopfield networks. Prof. Dr. Volker Sperschneider

Machine Learning. Hopfield networks. Prof. Dr. Volker Sperschneider Mache Learg Hopfed eor Prof. Dr. Voer Spercheder AG Machee Lere ud Naürchprachche Seme Iu für Iforma Techche Fauä Aber-Ludg-Uverä Freburg percheder@forma.u-freburg.de 30.05.3 Hopfed eor I. Movao II. Bac

More information

TEACHERS ASSESS STUDENT S MATHEMATICAL CREATIVITY COMPETENCE IN HIGH SCHOOL

TEACHERS ASSESS STUDENT S MATHEMATICAL CREATIVITY COMPETENCE IN HIGH SCHOOL Jourl o See d rs Yer 5, No., pp. 5-, 5 ORIGINL PPER TECHERS SSESS STUDENT S MTHEMTICL CRETIVITY COMPETENCE IN HIGH SCHOOL TRN TRUNG TINH Musrp reeved: 9..5; eped pper:..5; Pulsed ole:..5. sr. ssessme s

More information

8. Queueing systems lect08.ppt S Introduction to Teletraffic Theory - Fall

8. Queueing systems lect08.ppt S Introduction to Teletraffic Theory - Fall 8. Queueg sysems lec8. S-38.45 - Iroduco o Teleraffc Theory - Fall 8. Queueg sysems Coes Refresher: Smle eleraffc model M/M/ server wag laces M/M/ servers wag laces 8. Queueg sysems Smle eleraffc model

More information

Hygienic Cable Glands

Hygienic Cable Glands ygc bl Gld followg h cll WA l h Mufcug h l oo c y Bocholog du hcl du: vodg buld-u cy. Gl bl ygc l food d d ckgg of ology y o o d u of ud ll ld hcucl wh hy ovd h f u o h cl o h o dh ooh fh No hd cod o d

More information

Maximum likelihood estimate of phylogeny. BIOL 495S/ CS 490B/ MATH 490B/ STAT 490B Introduction to Bioinformatics April 24, 2002

Maximum likelihood estimate of phylogeny. BIOL 495S/ CS 490B/ MATH 490B/ STAT 490B Introduction to Bioinformatics April 24, 2002 Mmm lkelhood eme of phylogey BIO 9S/ S 90B/ MH 90B/ S 90B Iodco o Bofomc pl 00 Ovevew of he pobblc ppoch o phylogey o k ee ccodg o he lkelhood d ee whee d e e of eqece d ee by ee wh leve fo he eqece. he

More information

Efficient Estimators for Population Variance using Auxiliary Information

Efficient Estimators for Population Variance using Auxiliary Information Global Joural of Mahemacal cece: Theor ad Praccal. IN 97-3 Volume 3, Number (), pp. 39-37 Ieraoal Reearch Publcao Houe hp://www.rphoue.com Effce Emaor for Populao Varace ug Aular Iformao ubhah Kumar Yadav

More information

Adaptive Deconvolution and Cross Equalization

Adaptive Deconvolution and Cross Equalization Adpve Decovoluo Dr. M. urh ury er Roc Sold ges Adpve Decovoluo d Cross Equlzo By: Dr. M. urh ury er.er@rocsoldges.co roduco: Augus 998 Adpve lerg hve bee roduced by Wdro, hch ler led o he develope o eurl

More information

K3 p K2 p Kp 0 p 2 p 3 p

K3 p K2 p Kp 0 p 2 p 3 p Mah 80-00 Mo Ar 0 Chaer 9 Fourier Series ad alicaios o differeial equaios (ad arial differeial equaios) 9.-9. Fourier series defiiio ad covergece. The idea of Fourier series is relaed o he liear algebra

More information

Solution set Stat 471/Spring 06. Homework 2

Solution set Stat 471/Spring 06. Homework 2 oluo se a 47/prg 06 Homework a Whe he upper ragular elemes are suppressed due o smmer b Le Y Y Y Y A weep o he frs colum o oba: A ˆ b chagg he oao eg ad ec YY weep o he secod colum o oba: Aˆ YY weep o

More information

The Covenant Renewed. Family Journal Page. creation; He tells us in the Bible.)

The Covenant Renewed. Family Journal Page. creation; He tells us in the Bible.) i ell orie o go ih he picure. L, up ng i gro ve el ur Pren, ho phoo picure; u oher ell ee hey (T l. chi u b o on hi pge y ur ki kn pl. (We ee Hi i H b o b o kn e hem orie.) Compre h o ho creion; He ell

More information

Xidian University Liu Congfeng Page 1 of 49

Xidian University Liu Congfeng Page 1 of 49 dom Sgl Processg Cher4 dom Processes Cher 4 dom Processes Coes 4 dom Processes... 4. Deo o dom Process... 4. Chrcerzo o dom Process...4 4.. ol Chrcerzo o dom Process...4 4.. Frs-Order Deses o dom Process...5

More information

Transformations. Ordered set of numbers: (1,2,3,4) Example: (x,y,z) coordinates of pt in space. Vectors

Transformations. Ordered set of numbers: (1,2,3,4) Example: (x,y,z) coordinates of pt in space. Vectors Trnformion Ordered e of number:,,,4 Emple:,,z coordine of p in pce. Vecor If, n i i, K, n, i uni ecor Vecor ddiion +w, +, +, + V+w w Sclr roduc,, Inner do roduc α w. w +,.,. The inner produc i SCLR!. w,.,

More information

QR factorization. Let P 1, P 2, P n-1, be matrices such that Pn 1Pn 2... PPA

QR factorization. Let P 1, P 2, P n-1, be matrices such that Pn 1Pn 2... PPA QR facorzao Ay x real marx ca be wre as AQR, where Q s orhogoal ad R s upper ragular. To oba Q ad R, we use he Householder rasformao as follows: Le P, P, P -, be marces such ha P P... PPA ( R s upper ragular.

More information

Brownian Motion and Stochastic Calculus. Brownian Motion and Stochastic Calculus

Brownian Motion and Stochastic Calculus. Brownian Motion and Stochastic Calculus Browa Moo Sochasc Calculus Xogzh Che Uversy of Hawa a Maoa earme of Mahemacs Seember, 8 Absrac Ths oe s abou oob decomoso he bascs of Suare egrable margales Coes oob-meyer ecomoso Suare Iegrable Margales

More information

Midterm Exam. Tuesday, September hour, 15 minutes

Midterm Exam. Tuesday, September hour, 15 minutes Ecoomcs of Growh, ECON560 Sa Fracsco Sae Uvers Mchael Bar Fall 203 Mderm Exam Tuesda, Sepember 24 hour, 5 mues Name: Isrucos. Ths s closed boo, closed oes exam. 2. No calculaors of a d are allowed. 3.

More information

Competitive Facility Location Problem with Demands Depending on the Facilities

Competitive Facility Location Problem with Demands Depending on the Facilities Aa Pacc Maageme Revew 4) 009) 5-5 Compeve Facl Locao Problem wh Demad Depedg o he Facle Shogo Shode a* Kuag-Yh Yeh b Hao-Chg Ha c a Facul of Bue Admrao Kobe Gau Uver Japa bc Urba Plag Deparme Naoal Cheg

More information

CONSTACYCLIC CODES OF LENGTH OVER A FINITE FIELD

CONSTACYCLIC CODES OF LENGTH OVER A FINITE FIELD Jorl o Algbr Nbr Tory: Ac Alco Vol 5 Nbr 6 Pg 4-64 Albl ://ccc.co. DOI: ://.o.org/.864/_753 ONSTAYLI ODES OF LENGTH OVER A FINITE FIELD AITA SAHNI POONA TRAA SEHGAL r or Ac Sy c Pb Ury gr 64 I -l: 5@gl.co

More information

The Poisson Process Properties of the Poisson Process

The Poisson Process Properties of the Poisson Process Posso Processes Summary The Posso Process Properes of he Posso Process Ierarrval mes Memoryless propery ad he resdual lfeme paradox Superposo of Posso processes Radom seleco of Posso Pos Bulk Arrvals ad

More information

Chapter #3 EEE Subsea Control and Communication Systems

Chapter #3 EEE Subsea Control and Communication Systems EEE 87 Chter #3 EEE 87 Sube Cotrol d Commuictio Sytem Cloed loo ytem Stedy tte error PID cotrol Other cotroller Chter 3 /3 EEE 87 Itroductio The geerl form for CL ytem: C R ', where ' c ' H or Oe Loo (OL)

More information

EE 6885 Statistical Pattern Recognition

EE 6885 Statistical Pattern Recognition EE 6885 Sascal Paer Recogo Fall 005 Prof. Shh-Fu Chag hp://.ee.columba.edu/~sfchag Lecure 8 (/8/05 8- Readg Feaure Dmeso Reduco PCA, ICA, LDA, Chaper 3.8, 0.3 ICA Tuoral: Fal Exam Aapo Hyväre ad Erkk Oja,

More information

Learning of Graphical Models Parameter Estimation and Structure Learning

Learning of Graphical Models Parameter Estimation and Structure Learning Learg of Grahal Models Parameer Esmao ad Sruure Learg e Fukumzu he Isue of Sasal Mahemas Comuaoal Mehodology Sasal Iferee II Work wh Grahal Models Deermg sruure Sruure gve by modelg d e.g. Mxure model

More information

TWO INTERFACIAL COLLINEAR GRIFFITH CRACKS IN THERMO- ELASTIC COMPOSITE MEDIA

TWO INTERFACIAL COLLINEAR GRIFFITH CRACKS IN THERMO- ELASTIC COMPOSITE MEDIA WO INERFIL OLLINER GRIFFIH RS IN HERMO- ELSI OMOSIE MEDI h m MISHR S DS * Deme o Mheml See I Ie o eholog BHU V-5 I he oee o he le o he e e o eeg o o olle Gh e he ee o he wo ohoo mel e e e emee el. he olem

More information

SLOW INCREASING FUNCTIONS AND THEIR APPLICATIONS TO SOME PROBLEMS IN NUMBER THEORY

SLOW INCREASING FUNCTIONS AND THEIR APPLICATIONS TO SOME PROBLEMS IN NUMBER THEORY VOL. 8, NO. 7, JULY 03 ISSN 89-6608 ARPN Jourl of Egieerig d Applied Sciece 006-03 Ai Reerch Publihig Nework (ARPN). All righ reerved. www.rpjourl.com SLOW INCREASING FUNCTIONS AND THEIR APPLICATIONS TO

More information

T h e C S E T I P r o j e c t

T h e C S E T I P r o j e c t T h e P r o j e c t T H E P R O J E C T T A B L E O F C O N T E N T S A r t i c l e P a g e C o m p r e h e n s i v e A s s es s m e n t o f t h e U F O / E T I P h e n o m e n o n M a y 1 9 9 1 1 E T

More information

A L A BA M A L A W R E V IE W

A L A BA M A L A W R E V IE W A L A BA M A L A W R E V IE W Volume 52 Fall 2000 Number 1 B E F O R E D I S A B I L I T Y C I V I L R I G HT S : C I V I L W A R P E N S I O N S A N D TH E P O L I T I C S O F D I S A B I L I T Y I N

More information

Review for the Midterm Exam.

Review for the Midterm Exam. Review for he iderm Exm Rememer! Gross re e re Vriles suh s,, /, p / p, r, d R re gross res 2 You should kow he disiio ewee he fesile se d he udge se, d kow how o derive hem The Fesile Se Wihou goverme

More information

Multivariate Regression: A Very Powerful Forecasting Method

Multivariate Regression: A Very Powerful Forecasting Method Archves of Busess Reserch Vol., No. Pulco De: Jue. 5, 8 DOI:.78/r..7. Vslooulos. (8). Mulvre Regresso: A Very Powerful Forecsg Mehod. Archves of Busess Reserch, (), 8. Mulvre Regresso: A Very Powerful

More information

Least Squares Fitting (LSQF) with a complicated function Theexampleswehavelookedatsofarhavebeenlinearintheparameters

Least Squares Fitting (LSQF) with a complicated function Theexampleswehavelookedatsofarhavebeenlinearintheparameters Leas Squares Fg LSQF wh a complcaed fuco Theeampleswehavelookedasofarhavebeelearheparameers ha we have bee rg o deerme e.g. slope, ercep. For he case where he fuco s lear he parameers we ca fd a aalc soluo

More information

176 5 t h Fl oo r. 337 P o ly me r Ma te ri al s

176 5 t h Fl oo r. 337 P o ly me r Ma te ri al s A g la di ou s F. L. 462 E l ec tr on ic D ev el op me nt A i ng er A.W.S. 371 C. A. M. A l ex an de r 236 A d mi ni st ra ti on R. H. (M rs ) A n dr ew s P. V. 326 O p ti ca l Tr an sm is si on A p ps

More information

graph of unit step function t

graph of unit step function t .5 Piecewie coninuou forcing funcion...e.g. urning he forcing on nd off. The following Lplce rnform meril i ueful in yem where we urn forcing funcion on nd off, nd when we hve righ hnd ide "forcing funcion"

More information

A NOTE ON THE APPLICATION OF THE GUERMOND-PASQUETTI MASS LUMPING CORRECTION TECHNIQUE FOR CONVECTION-DIFFUSION PROBLEMS ( ) SERGII V.

A NOTE ON THE APPLICATION OF THE GUERMOND-PASQUETTI MASS LUMPING CORRECTION TECHNIQUE FOR CONVECTION-DIFFUSION PROBLEMS ( ) SERGII V. NTE N THE PPLICTIN F THE UERMND-PSQUETTI MSS LUMPIN CRRECTIN TECHNIQUE FR CNVECTIN-DIFFUSIN PRLEMS Prer submed o Elsever jourl 0 My 0 SERII V. SIRYK Nol Teccl Uversy of Ure "Igor Sorsy Kyv Polyecc Isue",

More information

Reliability Equivalence of a Parallel System with Non-Identical Components

Reliability Equivalence of a Parallel System with Non-Identical Components Ieraoa Mahemaca Forum 3 8 o. 34 693-7 Reaby Equvaece of a Parae Syem wh No-Ideca ompoe M. Moaer ad mmar M. Sarha Deparme of Sac & O.R. oege of Scece Kg Saud Uvery P.O.ox 455 Ryadh 45 Saud raba aarha@yahoo.com

More information

θ = θ Π Π Parametric counting process models θ θ θ Log-likelihood: Consider counting processes: Score functions:

θ = θ Π Π Parametric counting process models θ θ θ Log-likelihood: Consider counting processes: Score functions: Paramerc coug process models Cosder coug processes: N,,..., ha cou he occurreces of a eve of eres for dvduals Iesy processes: Lelhood λ ( ;,,..., N { } λ < Log-lelhood: l( log L( Score fucos: U ( l( log

More information

_ J.. C C A 551NED. - n R ' ' t i :. t ; . b c c : : I I .., I AS IEC. r '2 5? 9

_ J.. C C A 551NED. - n R ' ' t i :. t ; . b c c : : I I .., I AS IEC. r '2 5? 9 C C A 55NED n R 5 0 9 b c c \ { s AS EC 2 5? 9 Con 0 \ 0265 o + s ^! 4 y!! {! w Y n < R > s s = ~ C c [ + * c n j R c C / e A / = + j ) d /! Y 6 ] s v * ^ / ) v } > { ± n S = S w c s y c C { ~! > R = n

More information

INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY

INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY [Mjuh, : Jury, 0] ISSN: -96 Scefc Jourl Impc Fcr: 9 ISRA, Impc Fcr: IJESRT INTERNATIONAL JOURNAL OF ENINEERIN SCIENCES & RESEARCH TECHNOLOY HAMILTONIAN LACEABILITY IN MIDDLE RAPHS Mjuh*, MurlR, B Shmukh

More information

P a g e 3 6 of R e p o r t P B 4 / 0 9

P a g e 3 6 of R e p o r t P B 4 / 0 9 P a g e 3 6 of R e p o r t P B 4 / 0 9 p r o t e c t h um a n h e a l t h a n d p r o p e r t y fr om t h e d a n g e rs i n h e r e n t i n m i n i n g o p e r a t i o n s s u c h a s a q u a r r y. J

More information

Chapter #2 EEE Subsea Control and Communication Systems

Chapter #2 EEE Subsea Control and Communication Systems EEE 87 Chpter # EEE 87 Sube Cotrol d Commuictio Sytem Trfer fuctio Pole loctio d -ple Time domi chrcteritic Extr pole d zero Chpter /8 EEE 87 Trfer fuctio Lplce Trform Ued oly o LTI ytem Differetil expreio

More information

Introduction to mathematical Statistics

Introduction to mathematical Statistics Itroducto to mthemtcl ttstcs Fl oluto. A grou of bbes ll of whom weghed romtely the sme t brth re rdomly dvded to two grous. The bbes smle were fed formul A; those smle were fed formul B. The weght gs

More information

1. Consider an economy of identical individuals with preferences given by the utility function

1. Consider an economy of identical individuals with preferences given by the utility function CO 755 Problem Se e Cbrer. Cosder ecoomy o decl dduls wh reereces e by he uly uco U l l Pre- rces o ll hree oods re ormled o oe. Idduls suly ood lbor < d cosume oods d. The oerme c mose d lorem es o oods

More information

ASYMPTOTIC BEHAVIOR OF SOLUTIONS OF DISCRETE EQUATIONS ON DISCRETE REAL TIME SCALES

ASYMPTOTIC BEHAVIOR OF SOLUTIONS OF DISCRETE EQUATIONS ON DISCRETE REAL TIME SCALES ASYPTOTI BEHAVIOR OF SOLUTIONS OF DISRETE EQUATIONS ON DISRETE REAL TIE SALES J. Dlí B. Válvíová 2 Bro Uversy of Tehology Bro zeh Repul 2 Deprme of heml Alyss d Appled hems Fuly of See Uversy of Zl Žl

More information

Continuous Time Markov Chains

Continuous Time Markov Chains Couous me Markov chas have seay sae probably soluos f a oly f hey are ergoc, us lke scree me Markov chas. Fg he seay sae probably vecor for a couous me Markov cha s o more ffcul ha s he scree me case,

More information

4.8 Improper Integrals

4.8 Improper Integrals 4.8 Improper Inegrls Well you ve mde i hrough ll he inegrion echniques. Congrs! Unforunely for us, we sill need o cover one more inegrl. They re clled Improper Inegrls. A his poin, we ve only del wih inegrls

More information

Cyclone. Anti-cyclone

Cyclone. Anti-cyclone Adveco Cycloe A-cycloe Lorez (963) Low dmesoal aracors. Uclear f hey are a good aalogy o he rue clmae sysem, bu hey have some appealg characerscs. Dscusso Is he al codo balaced? Is here a al adjusme

More information

Chapter Unary Matrix Operations

Chapter Unary Matrix Operations Chpter 04.04 Ury trx Opertos After redg ths chpter, you should be ble to:. kow wht ury opertos mes,. fd the trspose of squre mtrx d t s reltoshp to symmetrc mtrces,. fd the trce of mtrx, d 4. fd the ermt

More information

Isotropic Non-Heisenberg Magnet for Spin S=1

Isotropic Non-Heisenberg Magnet for Spin S=1 Ierol Jourl of Physcs d Applcos. IN 974- Volume, Number (, pp. 7-4 Ierol Reserch Publco House hp://www.rphouse.com Isoropc No-Heseberg Mge for p = Y. Yousef d Kh. Kh. Mumov.U. Umrov Physcl-Techcl Isue

More information

The Linear Regression Of Weighted Segments

The Linear Regression Of Weighted Segments The Lear Regresso Of Weghed Segmes George Dael Maeescu Absrac. We proposed a regresso model where he depede varable s made o up of pos bu segmes. Ths suao correspods o he markes hroughou he da are observed

More information

National Conference on Recent Trends in Synthesis and Characterization of Futuristic Material in Science for the Development of Society

National Conference on Recent Trends in Synthesis and Characterization of Futuristic Material in Science for the Development of Society ABSTRACT Naoa Coferece o Rece Tred Syhe ad Characerzao of Fuurc Maera Scece for he Deveome of Socey (NCRDAMDS-208) I aocao wh Ieraoa Joura of Scefc Reearch Scece ad Techoogy Some New Iegra Reao of I- Fuco

More information

Comparison of the Bayesian and Maximum Likelihood Estimation for Weibull Distribution

Comparison of the Bayesian and Maximum Likelihood Estimation for Weibull Distribution Joural of Mahemacs ad Sascs 6 (2): 1-14, 21 ISSN 1549-3644 21 Scece Publcaos Comarso of he Bayesa ad Maxmum Lkelhood Esmao for Webull Dsrbuo Al Omar Mohammed Ahmed, Hadeel Salm Al-Kuub ad Noor Akma Ibrahm

More information

A Remark on Generalized Free Subgroups. of Generalized HNN Groups

A Remark on Generalized Free Subgroups. of Generalized HNN Groups Ieraoal Mahemacal Forum 5 200 o 503-509 A Remar o Geeralzed Free Subroup o Geeralzed HNN Group R M S Mahmood Al Ho Uvery Abu Dhab POBo 526 UAE raheedmm@yahoocom Abrac A roup ermed eeralzed ree roup a ree

More information

Nonlocal Boundary Value Problem for Nonlinear Impulsive q k Symmetric Integrodifference Equation

Nonlocal Boundary Value Problem for Nonlinear Impulsive q k Symmetric Integrodifference Equation OSR ol o Mec OSR-M e-ssn: 78-578 -SSN: 9-765X Vole e Ve M - A 7 PP 95- wwwojolog Nolocl Bo Vle Poble o Nole lve - Sec egoeece Eo Log Ceg Ceg Ho * Yeg He ee o Mec Yb Uve Yj PR C Abc: A oe ole lve egoeece

More information

THE LOWELL LEDGER, INDEPENDENT NOT NEUTRAL.

THE LOWELL LEDGER, INDEPENDENT NOT NEUTRAL. E OE EDGER DEEDE O EUR FO X O 2 E RUO OE G DY OVEER 0 90 O E E GE ER E ( - & q \ G 6 Y R OY F EEER F YOU q --- Y D OVER D Y? V F F E F O V F D EYR DE OED UDER EDOOR OUE RER (E EYEV G G R R R :; - 90 R

More information

Explicit Representation of Green s Function for Linear Fractional. Differential Operator with Variable Coefficients

Explicit Representation of Green s Function for Linear Fractional. Differential Operator with Variable Coefficients KSU-MH--E-R-: Verso 3 Epc Represeo of Gree s uco for er rco ffere Operor w Vrbe Coeffces Mog-H K d Hog-Co O cu of Mecs K Sug Uvers Pogg P R Kore Correspodg uor e-: oogco@ooco bsrc We provde epc represeos

More information

Optimality of Myopic Policy for a Class of Monotone Affine Restless Multi-Armed Bandit

Optimality of Myopic Policy for a Class of Monotone Affine Restless Multi-Armed Bandit Univeriy of Souhern Cliforni Opimliy of Myopic Policy for Cl of Monoone Affine Rele Muli-Armed Bndi Pri Mnourifrd USC Tr Jvidi UCSD Bhkr Krihnmchri USC Dec 0, 202 Univeriy of Souhern Cliforni Inroducion

More information

Calculus 241, section 12.2 Limits/Continuity & 12.3 Derivatives/Integrals notes by Tim Pilachowski r r r =, with a domain of real ( )

Calculus 241, section 12.2 Limits/Continuity & 12.3 Derivatives/Integrals notes by Tim Pilachowski r r r =, with a domain of real ( ) Clculu 4, econ Lm/Connuy & Devve/Inel noe y Tm Plchow, wh domn o el Wh we hve o : veco-vlued uncon, ( ) ( ) ( ) j ( ) nume nd ne o veco The uncon, nd A w done wh eul uncon ( x) nd connuy e he componen

More information

Modification of raised cosine weighting functions family

Modification of raised cosine weighting functions family Compuol Mehod d Expermel Meureme XIV 9 Modfco of red coe eghg fuco fmly C. Lek,. Klec & J. Perk Deprme of Elecroc, Mlry Uvery of echology, Pold brc Modfco of he ko fmly of red coe eghg fuco h he poer of

More information