Leader-Following Consensus of Nonlinear Multi-Agent Systems Based on Parameterized Lyapunov Function

Size: px
Start display at page:

Download "Leader-Following Consensus of Nonlinear Multi-Agent Systems Based on Parameterized Lyapunov Function"

Transcription

1 ODRES JOURL OF ELECRICL EGIEERIG VOL 5 O 2 SUER 25 3 Leade-Followg Cosesus of olea ul-ge Sysems Based o Paameezed Lyauov Fuco Pegah aba Saad 2 ohammad ehd ada okha Shasadegh Behouz Safaeada bsac hs ae sudes he cosesus oblem of olea leade-followg mul-age sysems (S). o do hs he eo dyamcs bewee he leade age ad followe oes ae descbed va a akag-sugeo (S) fuzzy model. If he obaed S fuzzy model s sable he all of he olea ages each cosesus. he cosesus oblem s vesgaed based o he aameezed o fuzzy Lyauov fuco combed wh a echque of oducg slack maces. he slack maces cause o decoule he Lyauov maces fom sysems oes ad heefoe suffce cosesus codos ae obaed ems of lea max equales (LIs). he oosed slack maces add a exa degee-of-feedom o he LI codos ad also decease he cosevaveess of he LI-based codos. Fally ode o llusae he effecveess ad mes of he oosed mehod a umecal examle fo he cosesus oblem of olea leade-followe S wh hee followes s solved. Idex ems olea mul-age sysems Cosesus akag- Sugeo (-S) fuzzy model Paameezed Lyauov fuco Lea ax Iequaly (LI). F I. IRODUCIO uzzy model based (FB) cool ovdes a famewok o desg a olea cool saegy fo a geeal class of olea sysems []. he hyscal heomea ae heely olea. I ode o ovecome he comlexy of he sysem oleay akag ad Sugeo ( 985) eseed a sysemac mul-modelg aoach called akag-sugeo (S) fuzzy model. he S fuzzy model lays a ccal ole FB cool []. I S fuzzy model whch oduced va fuzzy IF-HE ules he smooh olea sysem s eeseed va some local subsysems. he by fuzzy bledg of he local subsysems he oveall fuzzy model wll be acheved a covex sucue. he S fuzzy model ca be cosuced va he defcao mehods based o he u-ouu daa o ca be deved fom he exsece smooh olea sysem equaos [2]. I ode o cool he olea sysem based o he S fuzzy model seveal fuzzy cool mehods have bee eseed such as aallel dsbued comesao (PDC) o o-aallel dsbued comesao (o-pdc) [3]. Fuhemoe suffce sably codos of he closed-loo sysem wll be obaed based o he Lyauov dec mehod ems of LIs [3]. Oe of he commo aoaches o fomulae sably aalyss of he S fuzzy sysems s Lyauov dec mehod. he suffce sably codos ae coveed o LIs ad solved va covex omzao echques [2]. I some suaos may be he S fuzzy sysem s sable bu he commo quadac Lyauov fuco (QLF) does o exs [4]. hus seveal kds of LFs ae eseed leaue such as aameezed o fuzzy LF (FLF) [5] ax- LF [6] ad ecewse LF [7]. he ecewse LF s acheved based o a combao of some seaae quadac LFs whee each of hese quadac LFs s vald a acula ego. hus he ecewse LF suffes fom he oblem of dscouy bouday os of each ego. he FLF s also kow as bassdeede LF ad o-quadac LF. he FLF s acheved based o fuzzy aggegao of some quadac LFs [4]. ul-age sysems (S) ae cosuced by mulle ecoecg of ellge subsysems called ages. he ages o ogehe o sudy he oblems ha ae usually vey dffcul o eve mossble fo each age o solve [8]. Cosesus of Ss has aaced los of aeos as a ew feld of eseach. he cosesus oblem s vesgaed vaous backgoud of eseach such as: cool oboc bology ad comue scece backgouds. Roughly seakg f he ages of S each a ageeme o a secfc ceo he he cosesus oblem s feasble [9]. ccodg o he cool egeeg o of vew he cosesus oblem s coollg of he ages such ha he cosesus codos ae sasfed. Cosesus of Ss s a omsg eseach oc dug ece yeas. Cool heoy lays a moa ole fo solvg a cosesus oblem. Seveal kds of coveoal cool oocol ae aled o S o solve he oblem of cosesus such as: H cool oocol [] Pg cool [] ad samled-daa cool [2]. Desgg cool oocol fo lea dscee-me ages o solve he cosesus oblem s cosdeed [3]. oeove he cosesus oblem s suded fo Ss ha he dyamc behavo of he ages ae lea [4 H 5] ad olea [6]. Ref. [6] sudes a leadefollowg cosesus oblem of olea Ss. he cosesus oblem s o aoachs he olea followe ages o he ufoced leade age. hus he olea eo dyamcs bewee he followe ages ad ufoced leade ausc eceved Jue 25 25; acceed Seembe Deame of Eleccal ad Elecocs Egeeg Shaz Uvesy of echology Shaz Ia. {.aba m.mada shasadegh

2 SDI e al LEDER-FOLLOWIG COSESUS OF OLIER ULI-GE SYSES 3 age ae acheved. Subsequely he S fuzzy model of olea eo dyamcs ae calculaed ad he suffce sably codos ae deved ems of LIs. s afoemeoed Ref. [4] vesgaes he oblem of sably aalyss of S fuzzy sysems based o FLF ad oducg some slack maces. Slack maces geeae a degee of feedom LI codos ad have a dec effec o coveg he sably codos o he LI oes [4]. Wheeas by ceasg he umbe of ages he dmeso of LI codos wll be ceased ad subsequely he feasbly of LI codos wll be coveed o a challegg oblem. ccodg o he auhos bes kowledge hs ae es he fs aem o aalyze he cosesus oblem of olea Ss wh FLF ad slack maces. I hs ae we seek o solve he cosesus cool oblem moe elax scheme by usg he FLF ad oducg slack maces. Fs defe he leade-followg as he cosesus oblem. Secod he olea eo dyamcs bewee he ages of olea ufoced leades ad he followes wll be acheved. hus he cosesus oblem wll be coveed o he sably aalyss oe. hd based o he olea eo dyamcs he exac S fuzzy model wll be calculaed va seco olea aoach. Fouh ode o aalyze he sably of he S fuzzy model based closed-loo sysem he FLF ad some ew ull ems wll be defed ad suffce sably codos wll be deved ems of LIs. he ma cobuos of hs ae ca be classfed as follows:. he FLF wll be used o solve he cosesus oblem. 2. Some ew ull ems wll be defed. Slack maces ull ems cease he degee of feedom ad also coveed he sably codos o he LI oes. 3. he cool oocol wll be desged. 4. Comae o he ece ublshed woks [6 8-2] he LIbased sably codos ae moe elaxed. Fally he oosed aoach s aled o he cosesus oblem of olea leade-followe S wh hee followes. he emade of hs ae s ogazed as follows. Seco II s dvded o wo as. Fs a eses some basc coces of gah heoy ad secod a dscusses abou oblem fomulao. he ma esuls ae gve Seco III. I seco IV smulaos ae caed ou o llusae he effecveess of he ma esuls. Fally coclusos ae daw seco V. I he cue ae he suesc sads fo max asose deoes he -dmesoal Eucldea sace ad dag sads fo a block-dagoal max. I symmec block maces s used o eese a em ha s duced by I symmey s a dey max of dmeso ad deoes he Koecke oduc. 2. PRELIIRIES D PROBLE DESCRIPIO. Basc Coces o Gah heoy S cossg ages eeseed by a udeced gah G V G v v 2 v cosss of a veex se a edge v v se E G : E G v v v v V G meas ha age. If ca sed s fomao o he age ad vce vesa. I ohe wods hee s a deced coeco fom ode o ode. lso a adacecy max s defed such ha fo a deced coeco fom ode o ode f v v E G. Fuhemoe s suosed ha a he eghbo se of ode s deoed v V G : v v E G by. he Lalaca max assocaed wh G s defed as follows: ak l () k k a gah coag he leade ad all followes s eeseed by G. B. Poblem Fomulao Cosde a gou of followe ages ad oe leade. he dyamc of each followe age s gve by... x f x u (2) v a x f x whee s he sae of age (ode) s a olea couously dffeeable veco fuco eeseg he sc olea dyamcs of he -h age u ad s he cool oocol o be desged. I s assumed ha he leade has he followg olea mevayg dyamc s f s (3) s whee s he saes veco of he leade ad should be acked by all he followes. I ca be a equlbum o a chaoc ob o a eodc ob [6]. e ssumg ha deoes he eo bewee he saes of he followes ad he saes of he leade.e. e x s he eo dyamcs ca be eeseed as e f x f s u... Cosde a dsbued cosesus oocol as follows: (4)

3 ODRES JOURL OF ELECRICL EGIEERIG VOL 5 O 2 SUER 25 cd s () x ()... u c a x x (5) whee deoes he eghbog se of ode s he coulg segh R s he feedback ga max o be desged also should be osve defe [6]. eeses he coulg segh of he fomao flowg fom ode o ode ad a a whe access o he leade s sae fomao d d a c. If age has ad ohewse. Based o he S fuzzy model oduced [6] we ewe g x e f x f s g x e h x e as whee ca be aoxmaed by a S fuzzy model wh he chose emse vaables meawhle h x e cao be aoxmaed bu ca be esaed as x a oduc of bouded me-vayg max ad e. he fuzzy model of (4) s eeseed as he followg ules: Rule : If k s k he s ad 2 u e e x e s 2 ad ad 2 k whee s he emse 2... vaable veco fo ad l 2... k eeses he fuzzy ses s he umbe of f-he ules ad s he cosa max. he comac fom of he fuzzy model s as follows: whee l e h e x e u... h l l l l l whee l. he k (6) (7) s he gade of he membesh of l h h ssumo : x x Q osve defe max ad ca be we as 3. HE I RESULS whee Q Q R R. 32 (8) s a I hs seco based o he S fuzzy modelg he cosesus oblem of olea S s vesgaed. he eo dyamcs of (6) ca be we as follows: e ( h )e x e cl e whee I x I x 2 e e e... e L L dag d d 2... d o oba ew sably codos fo S cosde he followg ull max dees ha ae obaed fom (7) ad (8): (9) h h h e 3e () e e 2 e h cl e () has a lea elao wh 2 I s assumed ha 2 whee s a abay kow scala umbe 3 ad s a symmec max wh aoae dmeso. heoem : he followe ages as descbed (2) ca each cosesus wh he leade f G s coeced o equvalely a leas oe age each coeced comoe of G has access o he sae fomao of he leade ad fuhe hee exs symmec maces ad 3 ay max osve defe max ad scala such ha he followg LIs... hold fo. (2) P 3 P c I P (3)

4 SDI e al LEDER-FOLLOWIG COSESUS OF OLIER ULI-GE SYSES 33 H H 2 2 H I P.5 I (I R R) c L S H E (I ( I R R)) c L S whee E I P 3 I S (4) Poof: he followg aameezed Lyauov fuco caddae s chose V h e P e (5) V By dffeeag oe has h q V e Pq e h e Pe q q hq e I P q e he I P e (6) h If whee... 3 ad I P he V() h Z (2) e e H 3 H 4 H I P cl 3 2 H 4 E 2 cl V If he fo ay By usg assumo defg I emembeg he cosa H 5 H oe has H I P (.5 I (I R R) cl ) H E (I ( I R R)) ( cl ) ad (2) By usg he dees ()-() oe has hq V e I Pq e q q h e I P e hq e 3 e { e 2e e h cl e } q q q h h ( ( e I P e) h q e I P e e 3e q 2 e e e cl e Defe E I P 3 (7) (8) (9) o cove (2) o LI fuhe maulaos ae equed o do o he blea em ( cl ) of (2) as follows ( cl ) ( I ) ( cl ) cl Defe he ew decso vaable S oe has ( cl ) cl cl S Cosequely (2) s obaed as H H 2 2 H I P.5 I (I R R) c L S H E (I ( I R R)) c L S (22) heefoe he eos covege o zeo ad he cosesus s acheved. he oof s comleed. 4. ILLUSRIVE EXPLE I hs seco a umecal examle s gve o show he effecveess of he heoecal esuls.

5 ODRES JOURL OF ELECRICL EGIEERIG VOL 5 O 2 SUER 25 Examle: Cosde he mul-age ewok Fg. cossg of decal sysems [6]. he dyamcs of each ode s descbed by he followg chaoc equao x f x u (23) whee... x x 2 x x bx f x x 2x 2 x x 3 (24) 2 3 By choosg he values of 28 8 / 3 2 b fo chaos o emege he sysem (23) becomes he Loez sysem. he eo dyamcs sysem he dffeece bewee (23) ad he s f s leade e ca be deved as [7]: e e 2 e 2e 2 x e 3 e e 3 bx x e e e x e u x 2 3 (25) ek b k 2 ek x x 3 x 2 x x Remembeg assumo s bouded by Q R R whch 6 4 R. x e ca be calculaed accodg o (6) ad he membesh fucos ae cosdeed as [2]. Hee s suosed ha k d ad ohes ha meas oly age oe has access o he saes fomao of he leade. Solvg he LIs (-3) he ga max s obaed as follows: Fg. 2 shows he saes of he ages usg ad adomly chose al codos. I s cocluded fom Fg. 2 ha he cosesus ake lace quckly ad he followe ages ack he leade s saes fo he fuue. d k 34 fo Fg.. Coeced ewok oology. Hee we choose 3. he S fuzzy model (6) s used fo modelg he olea S as followg: Rule k : x e f s ad s he e ke x e u (26) k 2 whee x 2 e e e e 5 ad e 2 5 he augmeed eo sysem s as follows 4 e x e e x e u whee ek (27) 5. COCLUSIOS hs ae has cosdeed a cosesus cool oblem of leade-followg olea Ss. Ially he olea eo dyamcs bewee he leade age ad olea followe ages wee calculaed. hus he cosesus oblem of leadefollowg sysem chaged o he sably aalyss of he eo dyamc sysem. he he exac S fuzzy model of eo dyamc acheved va he coce of seco oleay. Suffce asymocally sably codos obaed based o he FLF ems of LIs. oeove based o he behavo of he closed-loo sysem some ew ull ems oosed. he slack maces defed ull ems had some advaages such as: ceasg he degee of feedom coveg he suffce sably codos o he LI oes decoulg he sysem maces fom he Lyauov oes ad also geeae moe elax codos. LI codos wee acheved by ulzg he Koecke oduc. he smulao esuls wee show he effecveess of he oosed aoach.

6 SDI e al LEDER-FOLLOWIG COSESUS OF OLIER ULI-GE SYSES 35 x s (a) x 2 s 2 (b) x 3 s 3 (c) Fg. 2. Cosesus of mul-age ewok ad saes evoluos of he x 23 followes ( s 23 ) ad he leade ( ). 6. REFERECES [] H. K. Lam L. Wu ad Y. Zhao "Lea max equales-based membesh fuco-deede sably aalyss fo oaallel dsbued comesao fuzzy-model-based cool sysems" IE Cool heoy & lcaos vol. 8 o [2] K. aaka ad H. O. Wag "Fuzzy cool sysems desg ad aalyss: a lea max equaly aoach" Joh Wley ewyok 2. [3] W. J. Chag C. C. Ku ad C. H. Chag "PDC ad o-pdc fuzzy cool wh elaxed sably codos fo couousme mullcave osed fuzzy sysems" Joual of he Fakl Isue vol. 349 o [4] L.. ozell R.. Palhaes ad G. S. vella " sysemac aoach o move mulle Lyauov fuco sably ad sablzao codos fo fuzzy sysems" Ifomao Sceces vol. 79 o [5]. Vafamad ad. Sha Sadegh "oe elaxed o-quadac sablzao codos fo S fuzzy cool sysems usg LI ad GEVP" Ieaoal of Joual of Cool uomao ad Sysems vol. 3 o [6]. Yamaguch ad H. O. Wag "Sably aalyss of olea sysems va mulle mxed max-m based Lyauov fucos" Fuzzy sysems (FUZZY) 2 IEEE eaoal Cofeece o [7].. l-radhaw ad D. gel "ew aoach o he sably of chemcal eaco ewoks: Pecewse lea aes Lyauov fucos" IEEE asacos o uomac Cool vol. 6 o [8] K. Ragab. Helmy ad. Hassae "Develog advaced web sevces hough P2P comug ad auoomous ages: eds ad Iovaos" Heshey ew Yok: 2. [9] P. L ad Y.. Ja "Robus H cosesus aalyss of a class of secod ode mul-age sysems wh uceay" IE Cool heoy & lcaos vol. 4 o [] Z. L Z. Dua G. Che ad L. Huag "Cosesus of mul age sysems ad sychozao of comlex ewoks: ufed vewo" IEEE asaco o Ccus ad Sysems I: Regula Paes vol. 57 o [] F. Che Z. Che L. Xag Z. Lu ad Z. Yua "Reachg a cosesus va g cool" uomaca vol. 45 o [2] Y. Zhag ad Y. P. a "Cosesus of daa-samled mulage sysems wh adom commucao delay ad acke loss" IEEE asacos o uomac Cool vol. 55 o [3] Y. Hu J. Lam ad J. Lag "Cosesus of mul-age sysems wh Luebege obseves" Joual of he Fakl Isue vol. 35 o [4] Y. Wu X. He S. Lu ad L. Xe "Cosesus of dscee-me mul-age sysems wh advesaes ad me delays" Ieaoal Joual of Geeal Sysems vol. 43 o [5] D. Xe Q. Lu L. Lv ad S. L "ecessay ad suffce codo fo he gou cosesus of mul-age sysems" led ahemacs ad Comuao vol [6] Y. Zhao B. L J. Q H. Gao ad H. R. Kam "H cosesus ad sychozao of olea sysems based o a ovel fuzzy model" IEEE asacos o Cybeecs vol. 43 o [7] J. L ad J. L "dave fuzzy eave leag cool wh al-sae leag fo coodao cool of leade-followg mul-age sysems" Fuzzy ses ad Sysems vol [8] X. Y D. Yue ad S. Hu "Cosesus of facoal-ode heeogeeous mul-age sysems" IE Cool heoy & lcaos vol. 7 o [9] W. Xog W. Yu J. Lu ad X. Yu "Fuzzy modellg ad cosesus of olea mulage sysems wh vaable sucue" IEEE asaco o Ccus ad Sysems I: Regula aes vol. 6 o [2] L. Zhao ad Y. Ja "Fe-me cosesus fo secod-ode sochasc mul-age sysems wh olea dyamcs" led ahemacs ad Comuao vol

Exponential Synchronization of the Hopfield Neural Networks with New Chaotic Strange Attractor

Exponential Synchronization of the Hopfield Neural Networks with New Chaotic Strange Attractor ITM Web of Cofeeces, 0509 (07) DOI: 0.05/ mcof/070509 ITA 07 Expoeal Sychozao of he Hopfeld Neual Newos wh New Chaoc Sage Aaco Zha-J GUI, Ka-Hua WANG* Depame of Sofwae Egeeg, Haa College of Sofwae Techology,qogha,

More information

Fault-tolerant Output Feedback Control for a Class of Multiple Input Fuzzy Bilinear Systems

Fault-tolerant Output Feedback Control for a Class of Multiple Input Fuzzy Bilinear Systems Sesos & asduces Vol 7 Issue 6 Jue 04 pp 47-5 Sesos & asduces 04 by IFSA Publshg S L hp://wwwsesospoalco Faul-olea Oupu Feedbac Cool fo a Class of Mulple Ipu Fuzzy Blea Syses * YU Yag WAG We School of Eleccal

More information

The Stability of High Order Max-Type Difference Equation

The Stability of High Order Max-Type Difference Equation Aled ad Comuaoal Maemacs 6; 5(): 5-55 ://wwwsceceulsggoucom/j/acm do: 648/jacm653 ISSN: 38-565 (P); ISSN: 38-563 (Ole) Te Saly of g Ode Ma-Tye Dffeece Equao a Ca-og * L Lue Ta Xue Scool of Maemacs ad Sascs

More information

Fakultas Matematika dan Ilmu Pengetahuan Alam, Institut Teknologi Bandung, Bandung, 40132, Indonesia *

Fakultas Matematika dan Ilmu Pengetahuan Alam, Institut Teknologi Bandung, Bandung, 40132, Indonesia * MacWllams Equvalece Theoem fo he Lee Wegh ove Z 4 leams Baa * Fakulas Maemaka da Ilmu Pegeahua lam, Isu Tekolog Badug, Badug, 403, Idoesa * oesodg uho: baa@mahbacd BSTRT Fo codes ove felds, he MacWllams

More information

DUALITY IN MULTIPLE CRITERIA AND MULTIPLE CONSTRAINT LEVELS LINEAR PROGRAMMING WITH FUZZY PARAMETERS

DUALITY IN MULTIPLE CRITERIA AND MULTIPLE CONSTRAINT LEVELS LINEAR PROGRAMMING WITH FUZZY PARAMETERS Ida Joual of Fudameal ad ppled Lfe Sceces ISSN: 223 6345 (Ole) Ope ccess, Ole Ieaoal Joual valable a www.cbech.o/sp.ed/ls/205/0/ls.hm 205 Vol.5 (S), pp. 447-454/Noua e al. Reseach cle DULITY IN MULTIPLE

More information

- 1 - Processing An Opinion Poll Using Fuzzy Techniques

- 1 - Processing An Opinion Poll Using Fuzzy Techniques - - Pocessg A Oo Poll Usg Fuzzy Techues by Da Peu Vaslu ABSTRACT: I hs ae we deal wh a mul cea akg oblem, based o fuzzy u daa : he uose s o comae he effec of dffee mecs defed o he sace of fuzzy umbes o

More information

ON TOTAL TIME ON TEST TRANSFORM ORDER ABSTRACT

ON TOTAL TIME ON TEST TRANSFORM ORDER ABSTRACT V M Chacko E CONVE AND INCREASIN CONVE OAL IME ON ES RANSORM ORDER R&A # 4 9 Vol. Decembe ON OAL IME ON ES RANSORM ORDER V. M. Chacko Depame of Sascs S. homas Collee hss eala-68 Emal: chackovm@mal.com

More information

Suppose we have observed values t 1, t 2, t n of a random variable T.

Suppose we have observed values t 1, t 2, t n of a random variable T. Sppose we have obseved vales, 2, of a adom vaable T. The dsbo of T s ow o belog o a cea ype (e.g., expoeal, omal, ec.) b he veco θ ( θ, θ2, θp ) of ow paamees assocaed wh s ow (whee p s he mbe of ow paamees).

More information

Shrinkage Estimators for Reliability Function. Mohammad Qabaha

Shrinkage Estimators for Reliability Function. Mohammad Qabaha A - Najah Uv. J. es. (N. Sc.) Vol. 7 3 Shkage Esmaos fo elably Fuco مقدرات التقلص لدالة الفاعلية ohammad Qabaha محمد قبها Depame of ahemacs Faculy of Scece A-Najah Naoal Uvesy alese E-mal: mohqabha@mal.ajah.edu

More information

Asymptotic Behavior of Solutions of Nonlinear Delay Differential Equations With Impulse

Asymptotic Behavior of Solutions of Nonlinear Delay Differential Equations With Impulse P a g e Vol Issue7Ver,oveber Global Joural of Scece Froer Research Asypoc Behavor of Soluos of olear Delay Dffereal Equaos Wh Ipulse Zhag xog GJSFR Classfcao - F FOR 3 Absrac Ths paper sudes he asypoc

More information

On the Quasi-Hyperbolic Kac-Moody Algebra QHA7 (2)

On the Quasi-Hyperbolic Kac-Moody Algebra QHA7 (2) Ieaoal Reeach Joual of Egeeg ad Techology (IRJET) e-issn: 9 - Volume: Iue: May- www.e.e -ISSN: 9-7 O he Qua-Hyebolc Kac-Moody lgeba QH7 () Uma Mahewa., Khave. S Deame of Mahemac Quad-E-Mllah Goveme College

More information

ANSWERS TO ODD NUMBERED EXERCISES IN CHAPTER 2

ANSWERS TO ODD NUMBERED EXERCISES IN CHAPTER 2 Joh Rley Novembe ANSWERS O ODD NUMBERED EXERCISES IN CHAPER Seo Eese -: asvy (a) Se y ad y z follows fom asvy ha z Ehe z o z We suppose he lae ad seek a oado he z Se y follows by asvy ha z y Bu hs oads

More information

MULTI-OBJECTIVE GEOMETRIC PROGRAMMING PROBLEM AND ITS APPLICATIONS

MULTI-OBJECTIVE GEOMETRIC PROGRAMMING PROBLEM AND ITS APPLICATIONS Yugoslav Joual of Opeaos Reseach Volume (), Numbe, -7 DOI:.98/YJORI MULTI-OBJECTIVE GEOMETRIC PROGRAMMING PROBLEM AND ITS APPLICATIONS Sahdul ISLAM Depame of Mahemacs, Guskaa Mahavdyalaya, Guskaa, Budwa

More information

( ) ( ) Weibull Distribution: k ti. u u. Suppose t 1, t 2, t n are times to failure of a group of n mechanisms. The likelihood function is

( ) ( ) Weibull Distribution: k ti. u u. Suppose t 1, t 2, t n are times to failure of a group of n mechanisms. The likelihood function is Webll Dsbo: Des Bce Dep of Mechacal & Idsal Egeeg The Uvesy of Iowa pdf: f () exp Sppose, 2, ae mes o fale of a gop of mechasms. The lelhood fco s L ( ;, ) exp exp MLE: Webll 3//2002 page MLE: Webll 3//2002

More information

CONTROL ROUTH ARRAY AND ITS APPLICATIONS

CONTROL ROUTH ARRAY AND ITS APPLICATIONS 3 Asa Joual of Cool, Vol 5, No, pp 3-4, Mach 3 CONTROL ROUTH ARRAY AND ITS APPLICATIONS Dazha Cheg ad TJTa Bef Pape ABSTRACT I hs pape he Rouh sably ceo [6] has bee developed o cool Rouh aay Soe foulas

More information

Professor Wei Zhu. 1. Sampling from the Normal Population

Professor Wei Zhu. 1. Sampling from the Normal Population AMS570 Pofesso We Zhu. Samplg fom the Nomal Populato *Example: We wsh to estmate the dstbuto of heghts of adult US male. It s beleved that the heght of adult US male follows a omal dstbuto N(, ) Def. Smple

More information

( m is the length of columns of A ) spanned by the columns of A : . Select those columns of B that contain a pivot; say those are Bi

( m is the length of columns of A ) spanned by the columns of A : . Select those columns of B that contain a pivot; say those are Bi Assgmet /MATH 47/Wte Due: Thusday Jauay The poblems to solve ae umbeed [] to [] below Fst some explaatoy otes Fdg a bass of the colum-space of a max ad povg that the colum ak (dmeso of the colum space)

More information

The Solutions of Initial Value Problems for Nonlinear Fourth-Order Impulsive Integro-Differential Equations in Banach Spaces

The Solutions of Initial Value Problems for Nonlinear Fourth-Order Impulsive Integro-Differential Equations in Banach Spaces WSEAS TRANSACTIONS o MATHEMATICS Zhag Lglg Y Jgy Lu Juguo The Soluos of Ial Value Pobles fo Nolea Fouh-Ode Ipulsve Iego-Dffeeal Equaos Baach Spaces Zhag Lglg Y Jgy Lu Juguo Depae of aheacs of Ta Yua Uvesy

More information

Two kinds of B-basis of the algebraic hyperbolic space *

Two kinds of B-basis of the algebraic hyperbolic space * 75 L e al. / J Zhejag Uv SCI 25 6A(7):75-759 Joual of Zhejag Uvesy SCIECE ISS 9-395 h://www.zju.edu.c/jzus E-al: jzus@zju.edu.c Two ds of B-bass of he algebac hyebolc sace * LI Ya-jua ( 李亚娟 ) WAG Guo-zhao

More information

Key words: Fractional difference equation, oscillatory solutions,

Key words: Fractional difference equation, oscillatory solutions, OSCILLATION PROPERTIES OF SOLUTIONS OF FRACTIONAL DIFFERENCE EQUATIONS Musafa BAYRAM * ad Ayd SECER * Deparme of Compuer Egeerg, Isabul Gelsm Uversy Deparme of Mahemacal Egeerg, Yldz Techcal Uversy * Correspodg

More information

Technical Appendix for Inventory Management for an Assembly System with Product or Component Returns, DeCroix and Zipkin, Management Science 2005.

Technical Appendix for Inventory Management for an Assembly System with Product or Component Returns, DeCroix and Zipkin, Management Science 2005. Techc Appedx fo Iveoy geme fo Assemy Sysem wh Poduc o Compoe eus ecox d Zp geme Scece 2005 Lemm µ µ s c Poof If J d µ > µ he ˆ 0 µ µ µ µ µ µ µ µ Sm gumes essh he esu f µ ˆ > µ > µ > µ o K ˆ If J he so

More information

Computation of an Over-Approximation of the Backward Reachable Set using Subsystem Level Set Functions

Computation of an Over-Approximation of the Backward Reachable Set using Subsystem Level Set Functions Compuao o a Ove-Appomao o he Backwad Reachable Se usg Subsysem Level Se Fucos Duša M Spaov, Iseok Hwag, ad Clae J oml Depame o Aeoaucs ad Asoaucs Saod Uvesy Saod, CA 94305-4035, USA E-mal: {dusko, shwag,

More information

Synchronization of Complex Network System with Time-Varying Delay Via Periodically Intermittent Control

Synchronization of Complex Network System with Time-Varying Delay Via Periodically Intermittent Control Sychrozao of Complex ework Sysem wh me-varyg Delay Va Perodcally Ierme Corol JIAG Ya Deparme of Elecrcal ad Iformao Egeerg Hua Elecrcal College of echology Xaga 4, Cha Absrac he sychrozao corol problem

More information

EMA5001 Lecture 3 Steady State & Nonsteady State Diffusion - Fick s 2 nd Law & Solutions

EMA5001 Lecture 3 Steady State & Nonsteady State Diffusion - Fick s 2 nd Law & Solutions EMA5 Lecue 3 Seady Sae & Noseady Sae ffuso - Fck s d Law & Soluos EMA 5 Physcal Popees of Maeals Zhe heg (6) 3 Noseady Sae ff Fck s d Law Seady-Sae ffuso Seady Sae Seady Sae = Equlbum? No! Smlay: Sae fuco

More information

Chapter 3: Maximum-Likelihood & Bayesian Parameter Estimation (part 1)

Chapter 3: Maximum-Likelihood & Bayesian Parameter Estimation (part 1) Aoucemes Reags o E-reserves Proec roosal ue oay Parameer Esmao Bomercs CSE 9-a Lecure 6 CSE9a Fall 6 CSE9a Fall 6 Paer Classfcao Chaer 3: Mamum-Lelhoo & Bayesa Parameer Esmao ar All maerals hese sles were

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

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 Eigenvalue Problem of the Symmetric Toeplitz Matrix

The Eigenvalue Problem of the Symmetric Toeplitz Matrix he Egevalue Poblem of he Smmec oelz Max bsac I hs assgme, he mehods ad algohms fo solvg he egevalue oblem of smmec oelz max ae suded. Fs he oelz ssem s oduced. he he mehods ha ca localze he egevalues of

More information

Stabilization of LTI Switched Systems with Input Time Delay. Engineering Letters, 14:2, EL_14_2_14 (Advance online publication: 16 May 2007) Lin Lin

Stabilization of LTI Switched Systems with Input Time Delay. Engineering Letters, 14:2, EL_14_2_14 (Advance online publication: 16 May 2007) Lin Lin Egeerg Leers, 4:2, EL_4_2_4 (Advace ole publcao: 6 May 27) Sablzao of LTI Swched Sysems wh Ipu Tme Delay L L Absrac Ths paper deals wh sablzao of LTI swched sysems wh pu me delay. A descrpo of sysems sablzao

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

1. INTRODUCTION In this paper, we consider a general ninth order linear boundary value problem (1) subject to boundary conditions

1. INTRODUCTION In this paper, we consider a general ninth order linear boundary value problem (1) subject to boundary conditions NUMERICAL SOLUTION OF NINTH ORDER BOUNDARY VALUE PROBLEMS BY PETROV-GALERKIN METHOD WITH QUINTIC B-SPLINES AS BASIS FUNCTIONS AND SEXTIC B-SPLINES AS WEIGHT FUNCTIONS K. N. S. Kas Vswaaham a S. V. Kamay

More information

Average Consensus in Networks of Multi-Agent with Multiple Time-Varying Delays

Average Consensus in Networks of Multi-Agent with Multiple Time-Varying Delays I. J. Commucaos ewor ad Sysem Sceces 3 96-3 do:.436/jcs..38 Publshed Ole February (hp://www.scrp.org/joural/jcs/). Average Cosesus ewors of Mul-Age wh Mulple me-varyg Delays echeg ZHAG Hu YU Isue of olear

More information

VARIATIONAL ITERATION METHOD FOR DELAY DIFFERENTIAL-ALGEBRAIC EQUATIONS. Hunan , China,

VARIATIONAL ITERATION METHOD FOR DELAY DIFFERENTIAL-ALGEBRAIC EQUATIONS. Hunan , China, Mahemacal ad Compuaoal Applcaos Vol. 5 No. 5 pp. 834-839. Assocao for Scefc Research VARIATIONAL ITERATION METHOD FOR DELAY DIFFERENTIAL-ALGEBRAIC EQUATIONS Hoglag Lu Aguo Xao Yogxag Zhao School of Mahemacs

More information

(1) Cov(, ) E[( E( ))( E( ))]

(1) Cov(, ) E[( E( ))( E( ))] Impac of Auocorrelao o OLS Esmaes ECON 3033/Evas Cosder a smple bvarae me-seres model of he form: y 0 x The four key assumpos abou ε hs model are ) E(ε ) = E[ε x ]=0 ) Var(ε ) =Var(ε x ) = ) Cov(ε, ε )

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

A New Approach to Probabilistic Load Flow

A New Approach to Probabilistic Load Flow INDIAN INSTITUTE OF TECHNOLOGY, KHARAGPUR 73, DECEMBER 79, 837 A New Appoach o Pobablsc Load Flow T K Basu, R B Msa ad Puob Paoway Absac: Ths pape descbes a ew appoach o modellg of asmsso le uceaes usg

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

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

14. Poisson Processes

14. Poisson Processes 4. Posso Processes I Lecure 4 we roduced Posso arrvals as he lmg behavor of Bomal radom varables. Refer o Posso approxmao of Bomal radom varables. From he dscusso here see 4-6-4-8 Lecure 4 " arrvals occur

More information

Redundancy System Fault Sampling Under Imperfect Maintenance

Redundancy System Fault Sampling Under Imperfect Maintenance A publcao of CHEMICAL EGIEERIG TRASACTIOS VOL. 33, 03 Gues Edors: Erco Zo, Pero Barald Copyrgh 03, AIDIC Servz S.r.l., ISB 978-88-95608-4-; ISS 974-979 The Iala Assocao of Chemcal Egeerg Ole a: www.adc./ce

More information

Delay-Dependent Robust Asymptotically Stable for Linear Time Variant Systems

Delay-Dependent Robust Asymptotically Stable for Linear Time Variant Systems Delay-Depede Robus Asypocally Sable for Lear e Vara Syses D. Behard, Y. Ordoha, S. Sedagha ABSRAC I hs paper, he proble of delay depede robus asypocally sable for ucera lear e-vara syse wh ulple delays

More information

Generalisation on the Zeros of a Family of Complex Polynomials

Generalisation on the Zeros of a Family of Complex Polynomials Ieol Joul of hemcs esech. ISSN 976-584 Volume 6 Numbe 4. 93-97 Ieol esech Publco House h://www.house.com Geelso o he Zeos of Fmly of Comlex Polyomls Aee sgh Neh d S.K.Shu Deme of hemcs Lgys Uvesy Fdbd-

More information

Comparing Different Estimators for Parameters of Kumaraswamy Distribution

Comparing Different Estimators for Parameters of Kumaraswamy Distribution Compaig Diffee Esimaos fo Paamees of Kumaaswamy Disibuio ا.م.د نذير عباس ابراهيم الشمري جامعة النهرين/بغداد-العراق أ.م.د نشات جاسم محمد الجامعة التقنية الوسطى/بغداد- العراق Absac: This pape deals wih compaig

More information

Available online at J. Math. Comput. Sci. 2 (2012), No. 4, ISSN:

Available online at   J. Math. Comput. Sci. 2 (2012), No. 4, ISSN: Available olie a h://scik.og J. Mah. Comu. Sci. 2 (22), No. 4, 83-835 ISSN: 927-537 UNBIASED ESTIMATION IN BURR DISTRIBUTION YASHBIR SINGH * Deame of Saisics, School of Mahemaics, Saisics ad Comuaioal

More information

The algebraic immunity of a class of correlation immune H Boolean functions

The algebraic immunity of a class of correlation immune H Boolean functions Ieraoal Coferece o Advaced Elecroc Scece ad Techology (AEST 06) The algebrac mmuy of a class of correlao mmue H Boolea fucos a Jgla Huag ad Zhuo Wag School of Elecrcal Egeerg Norhwes Uversy for Naoales

More information

Applying Eyring s Model to Times to Breakdown of Insulating Fluid

Applying Eyring s Model to Times to Breakdown of Insulating Fluid Ieaoal Joual of Pefomably Egeeg, Vol. 8, No. 3, May 22, pp. 279-288. RAMS Cosulas Ped Ida Applyg Eyg s Model o Tmes o Beakdow of Isulag Flud DANIEL I. DE SOUZA JR. ad R. ROCHA Flumese Fed. Uvesy, Cvl Egeeg

More information

= y and Normed Linear Spaces

= y and Normed Linear Spaces 304-50 LINER SYSTEMS Lectue 8: Solutos to = ad Nomed Lea Spaces 73 Fdg N To fd N, we eed to chaacteze all solutos to = 0 Recall that ow opeatos peseve N, so that = 0 = 0 We ca solve = 0 ecusvel backwads

More information

Continuous Indexed Variable Systems

Continuous Indexed Variable Systems Ieraoal Joural o Compuaoal cece ad Mahemacs. IN 0974-389 Volume 3, Number 4 (20), pp. 40-409 Ieraoal Research Publcao House hp://www.rphouse.com Couous Idexed Varable ysems. Pouhassa ad F. Mohammad ghjeh

More information

are positive, and the pair A, B is controllable. The uncertainty in is introduced to model control failures.

are positive, and the pair A, B is controllable. The uncertainty in is introduced to model control failures. Lectue 4 8. MRAC Desg fo Affe--Cotol MIMO Systes I ths secto, we cosde MRAC desg fo a class of ult-ut-ult-outut (MIMO) olea systes, whose lat dyacs ae lealy aaetezed, the ucetates satsfy the so-called

More information

Fairing of Parametric Quintic Splines

Fairing of Parametric Quintic Splines ISSN 46-69 Eglad UK Joual of Ifomato ad omputg Scece Vol No 6 pp -8 Fag of Paametc Qutc Sples Yuau Wag Shagha Isttute of Spots Shagha 48 ha School of Mathematcal Scece Fuda Uvesty Shagha 4 ha { P t )}

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

The Mean Residual Lifetime of (n k + 1)-out-of-n Systems in Discrete Setting

The Mean Residual Lifetime of (n k + 1)-out-of-n Systems in Discrete Setting Appled Mahemacs 4 5 466-477 Publshed Ole February 4 (hp//wwwscrporg/oural/am hp//dxdoorg/436/am45346 The Mea Resdual Lfeme of ( + -ou-of- Sysems Dscree Seg Maryam Torab Sahboom Deparme of Sascs Scece ad

More information

The Nehari Manifold for a Class of Elliptic Equations of P-laplacian Type. S. Khademloo and H. Mohammadnia. afrouzi

The Nehari Manifold for a Class of Elliptic Equations of P-laplacian Type. S. Khademloo and H. Mohammadnia. afrouzi Wold Alied cieces Joal (8): 898-95 IN 88-495 IDOI Pblicaios = h x g x x = x N i W whee is a eal aamee is a boded domai wih smooh boday i R N 3 ad< < INTRODUCTION Whee s ha is s = I his ae we ove he exisece

More information

Partial Molar Properties of solutions

Partial Molar Properties of solutions Paral Molar Properes of soluos A soluo s a homogeeous mxure; ha s, a soluo s a oephase sysem wh more ha oe compoe. A homogeeous mxures of wo or more compoes he gas, lqud or sold phase The properes of a

More information

RECAPITULATION & CONDITIONAL PROBABILITY. Number of favourable events n E Total number of elementary events n S

RECAPITULATION & CONDITIONAL PROBABILITY. Number of favourable events n E Total number of elementary events n S Fomulae Fo u Pobablty By OP Gupta [Ida Awad We, +91-9650 350 480] Impotat Tems, Deftos & Fomulae 01 Bascs Of Pobablty: Let S ad E be the sample space ad a evet a expemet espectvely Numbe of favouable evets

More information

SECURITY EVALUATION FOR SNOW 2.0-LIKE STREAM CIPHERS AGAINST CORRELATION ATTACKS OVER EXTENSION FIELDS

SECURITY EVALUATION FOR SNOW 2.0-LIKE STREAM CIPHERS AGAINST CORRELATION ATTACKS OVER EXTENSION FIELDS SECURIY EVALUAION FOR SNOW.-LIKE SREAM CIPHERS AGAINS CORRELAION AACKS OVER EXENSION FIELDS A. N. Alekseychk * S. M. Koshok ** M. V. Poemsky *** Ise of Secal Commcao ad Ifomao Secy Naoal echcal Uvesy of

More information

A Recurrent Neural Network to Identify Efficient Decision Making Units in Data Envelopment Analysis

A Recurrent Neural Network to Identify Efficient Decision Making Units in Data Envelopment Analysis Avalable Ole a h://rm.srbau.ac.r Vol.1 No.3 Auum 15 Joural of Ne Researches Mahemacs Scece ad Research Brach (IAU) A Recurre Neural Neork o Idefy Effce Decso Makg Us Daa Evelome Aalyss A. Ghomash a G.

More information

GREEN S FUNCTION FOR HEAT CONDUCTION PROBLEMS IN A MULTI-LAYERED HOLLOW CYLINDER

GREEN S FUNCTION FOR HEAT CONDUCTION PROBLEMS IN A MULTI-LAYERED HOLLOW CYLINDER Joual of ppled Mathematcs ad Computatoal Mechacs 4, 3(3), 5- GREE S FUCTIO FOR HET CODUCTIO PROBLEMS I MULTI-LYERED HOLLOW CYLIDER Stasław Kukla, Uszula Sedlecka Isttute of Mathematcs, Czestochowa Uvesty

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

Computational Fluid Dynamics. Numerical Methods for Parabolic Equations. Numerical Methods for One-Dimensional Heat Equations

Computational Fluid Dynamics. Numerical Methods for Parabolic Equations. Numerical Methods for One-Dimensional Heat Equations Compuaoal Flud Dyamcs p://www.d.edu/~gyggva/cfd-couse/ Compuaoal Flud Dyamcs p://www.d.edu/~gyggva/cfd-couse/ Compuaoal Flud Dyamcs Numecal Meods o Paabolc Equaos Lecue Mac 6 7 Géa Tyggvaso Compuaoal Flud

More information

For the plane motion of a rigid body, an additional equation is needed to specify the state of rotation of the body.

For the plane motion of a rigid body, an additional equation is needed to specify the state of rotation of the body. The kecs of rgd bodes reas he relaoshps bewee he exeral forces acg o a body ad he correspodg raslaoal ad roaoal moos of he body. he kecs of he parcle, we foud ha wo force equaos of moo were requred o defe

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

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

Density estimation III.

Density estimation III. Lecure 4 esy esmao III. Mlos Hauskrec mlos@cs..edu 539 Seo Square Oule Oule: esy esmao: Mamum lkelood ML Bayesa arameer esmaes MP Beroull dsrbuo. Bomal dsrbuo Mulomal dsrbuo Normal dsrbuo Eoeal famly Eoeal

More information

Journal of Econometrics. Quasi-maximum likelihood estimation of volatility with high frequency data

Journal of Econometrics. Quasi-maximum likelihood estimation of volatility with high frequency data Joual of Ecoomecs 59 5 5 Coes lss avalable a SceceDec Joual of Ecoomecs joual homeage: wwwelsevecom/locae/jecoom Quas-maxmum lkelhood esmao of volaly wh hgh fequecy daa Dacheg Xu Bedhem Cee fo Face, Pceo

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

Manifolds with Bakry-Emery Ricci Curvature Bounded Below

Manifolds with Bakry-Emery Ricci Curvature Bounded Below Advaces Pue Maemacs, 6, 6, 754-764 ://wwwscog/joual/am ISSN Ole: 6-384 ISSN P: 6-368 Maolds w Baky-Emey Rcc Cuvaue Bouded Below Issa Allassae Kaboye, Bazaaé Maama Faculé de Sceces e Tecques, Uvesé de Zde,

More information

Op Amp Noise in Dynamic Range Maximization of Integrated Active-RC Filters

Op Amp Noise in Dynamic Range Maximization of Integrated Active-RC Filters Op Amp Nose Dyamc Rage Maxmzao of Iegaed Acve-R Fles N MARAOS* AND M MLADENO** * Naoal echcal Uvesy of Ahes Dep of Eleccal ad ompue Egeeg 9 Ioo Polyechou S ogafou 577 Ahes eece ** Depame of heoecal Elecoechcs

More information

Determination of Antoine Equation Parameters. December 4, 2012 PreFEED Corporation Yoshio Kumagae. Introduction

Determination of Antoine Equation Parameters. December 4, 2012 PreFEED Corporation Yoshio Kumagae. Introduction refeed Soluos for R&D o Desg Deermao of oe Equao arameers Soluos for R&D o Desg December 4, 0 refeed orporao Yosho Kumagae refeed Iroduco hyscal propery daa s exremely mpora for performg process desg ad

More information

BINOMIAL THEOREM OBJECTIVE PROBLEMS in the expansion of ( 3 +kx ) are equal. Then k =

BINOMIAL THEOREM OBJECTIVE PROBLEMS in the expansion of ( 3 +kx ) are equal. Then k = wwwskshieduciocom BINOMIAL HEOREM OBJEIVE PROBLEMS he coefficies of, i e esio of k e equl he k /7 If e coefficie of, d ems i e i AP, e e vlue of is he coefficies i e,, 7 ems i e esio of e i AP he 7 7 em

More information

Quantum Mechanics II Lecture 11 Time-dependent perturbation theory. Time-dependent perturbation theory (degenerate or non-degenerate starting state)

Quantum Mechanics II Lecture 11 Time-dependent perturbation theory. Time-dependent perturbation theory (degenerate or non-degenerate starting state) Pro. O. B. Wrgh, Auum Quaum Mechacs II Lecure Tme-depede perurbao heory Tme-depede perurbao heory (degeerae or o-degeerae sarg sae) Cosder a sgle parcle whch, s uperurbed codo wh Hamloa H, ca exs a superposo

More information

ESTIMATION OF PARAMETERS AND VERIFICATION OF STATISTICAL HYPOTHESES FOR GAUSSIAN MODELS OF STOCK PRICE

ESTIMATION OF PARAMETERS AND VERIFICATION OF STATISTICAL HYPOTHESES FOR GAUSSIAN MODELS OF STOCK PRICE Lhuaa Joual of Sascs Leuvos sasos daba 06, vol 55, o, pp 9 0 06, 55,, 9 0 p wwwsascsjouall ESTIMATIO OF PARAMETERS AD VERIFICATIO OF STATISTICAL YPOTESES FOR GAUSSIA MODELS OF STOCK PRICE Dmyo Maushevych,

More information

Multi-Item Single-Vendor-Single-Buyer Problem with Consideration of Transportation Quantity Discount

Multi-Item Single-Vendor-Single-Buyer Problem with Consideration of Transportation Quantity Discount ul-iem Sgle-Vedo-Sgle-Buye Poblem wh Cosdeao of Taspoao Quay Dscou Ye- WANG, Roh BHATNAGAR, Sephe C. GRAVES 3 IST Pogamme, Sgapoe-IT Allace, 5 Nayag Aveue, Sgapoe 639798 Nayag Techologcal Uvesy, Nayag

More information

Chapter 3: Vectors and Two-Dimensional Motion

Chapter 3: Vectors and Two-Dimensional Motion Chape 3: Vecos and Two-Dmensonal Moon Vecos: magnude and decon Negae o a eco: eese s decon Mulplng o ddng a eco b a scala Vecos n he same decon (eaed lke numbes) Geneal Veco Addon: Tangle mehod o addon

More information

GLOBAL OPTIMIZATION FOR THE SYNTHESIS OF INTEGRATED WATER SYSTEMS IN CHEMICAL PROCESSES

GLOBAL OPTIMIZATION FOR THE SYNTHESIS OF INTEGRATED WATER SYSTEMS IN CHEMICAL PROCESSES GOBA OPTIMIZATIO O THE SYTHESIS O ITEGATED WATE SYSTEMS I HEMIA POESSES amuma Kauah ad Igaco E. Gossma* Deame o hemcal Egeeg aege Mello vesy Psbugh PA 523 Mach 2005 ABSTAT I hs ae we addess he oblem o

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

k of the incident wave) will be greater t is too small to satisfy the required kinematics boundary condition, (19)

k of the incident wave) will be greater t is too small to satisfy the required kinematics boundary condition, (19) TOTAL INTRNAL RFLTION Kmacs pops Sc h vcos a coplaa, l s cosd h cd pla cocds wh h X pla; hc 0. y y y osd h cas whch h lgh s cd fom h mdum of hgh dx of faco >. Fo cd agls ga ha h ccal agl s 1 ( /, h hooal

More information

Minimum Hyper-Wiener Index of Molecular Graph and Some Results on Szeged Related Index

Minimum Hyper-Wiener Index of Molecular Graph and Some Results on Szeged Related Index Joual of Multdscplay Egeeg Scece ad Techology (JMEST) ISSN: 359-0040 Vol Issue, Febuay - 05 Mmum Hype-Wee Idex of Molecula Gaph ad Some Results o eged Related Idex We Gao School of Ifomato Scece ad Techology,

More information

Stability Criterion for BAM Neural Networks of Neutral- Type with Interval Time-Varying Delays

Stability Criterion for BAM Neural Networks of Neutral- Type with Interval Time-Varying Delays Avalable ole a www.scecedrec.com Proceda Egeerg 5 (0) 86 80 Advaced Corol Egeergad Iformao Scece Sably Crero for BAM Neural Neworks of Neural- ype wh Ierval me-varyg Delays Guoqua Lu a* Smo X. Yag ab a

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

5-1. We apply Newton s second law (specifically, Eq. 5-2). F = ma = ma sin 20.0 = 1.0 kg 2.00 m/s sin 20.0 = 0.684N. ( ) ( )

5-1. We apply Newton s second law (specifically, Eq. 5-2). F = ma = ma sin 20.0 = 1.0 kg 2.00 m/s sin 20.0 = 0.684N. ( ) ( ) 5-1. We apply Newon s second law (specfcally, Eq. 5-). (a) We fnd he componen of he foce s ( ) ( ) F = ma = ma cos 0.0 = 1.00kg.00m/s cos 0.0 = 1.88N. (b) The y componen of he foce s ( ) ( ) F = ma = ma

More information

Fourth Order Runge-Kutta Method Based On Geometric Mean for Hybrid Fuzzy Initial Value Problems

Fourth Order Runge-Kutta Method Based On Geometric Mean for Hybrid Fuzzy Initial Value Problems IOSR Joural of Mahemacs (IOSR-JM) e-issn: 2278-5728, p-issn: 29-765X. Volume, Issue 2 Ver. II (Mar. - Apr. 27), PP 4-5 www.osrjourals.org Fourh Order Ruge-Kua Mehod Based O Geomerc Mea for Hybrd Fuzzy

More information

AML710 CAD LECTURE 12 CUBIC SPLINE CURVES. Cubic Splines Matrix formulation Normalised cubic splines Alternate end conditions Parabolic blending

AML710 CAD LECTURE 12 CUBIC SPLINE CURVES. Cubic Splines Matrix formulation Normalised cubic splines Alternate end conditions Parabolic blending CUIC SLINE CURVES Cubc Sples Marx formulao Normalsed cubc sples Alerae ed codos arabolc bledg AML7 CAD LECTURE CUIC SLINE The ame sple comes from he physcal srume sple drafsme use o produce curves A geeral

More information

A Modeling Method of SISO Discrete-Event Systems in Max Algebra

A Modeling Method of SISO Discrete-Event Systems in Max Algebra A Modelg Mehod of SISO Dscee-Eve Syses Max Algeba Jea-Lous Bood, Laue Hadou, P. Cho To ce hs veso: Jea-Lous Bood, Laue Hadou, P. Cho. A Modelg Mehod of SISO Dscee-Eve Syses Max Algeba. Euopea Cool Cofeece

More information

Density estimation. Density estimations. CS 2750 Machine Learning. Lecture 5. Milos Hauskrecht 5329 Sennott Square

Density estimation. Density estimations. CS 2750 Machine Learning. Lecture 5. Milos Hauskrecht 5329 Sennott Square Lecure 5 esy esmao Mlos Hauskrec mlos@cs..edu 539 Seo Square esy esmaos ocs: esy esmao: Mamum lkelood ML Bayesa arameer esmaes M Beroull dsrbuo. Bomal dsrbuo Mulomal dsrbuo Normal dsrbuo Eoeal famly Noaramerc

More information

XII. Addition of many identical spins

XII. Addition of many identical spins XII. Addto of may detcal sps XII.. ymmetc goup ymmetc goup s the goup of all possble pemutatos of obects. I total! elemets cludg detty opeato. Each pemutato s a poduct of a ceta fte umbe of pawse taspostos.

More information

A Survey on Model Reduction Methods to Reduce Degrees of Freedom of Linear Damped Vibrating Systems

A Survey on Model Reduction Methods to Reduce Degrees of Freedom of Linear Damped Vibrating Systems opdaa Aavakom 460767 Mah, pg 003 A uvey o Model Reduco Mehods o Reduce Degees o Feedom o Lea Damped Vbag ysems ABRAC hs epo descbes he deals o he model educo mehods o educe degees o eedom o he dyamc aalyss

More information

THE PUBLISHING HOUSE PROCEEDINGS OF THE ROMANIAN ACADEMY, Series A, OF THE ROMANIAN ACADEMY Volume 10, Number 2/2009, pp

THE PUBLISHING HOUSE PROCEEDINGS OF THE ROMANIAN ACADEMY, Series A, OF THE ROMANIAN ACADEMY Volume 10, Number 2/2009, pp THE PUBLIHING HOUE PROCEEDING OF THE ROMANIAN ACADEMY, eres A, OF THE ROMANIAN ACADEMY Volume 0, Number /009,. 000-000 ON ZALMAI EMIPARAMETRIC DUALITY MODEL FOR MULTIOBJECTIVE FRACTIONAL PROGRAMMING WITH

More information

PULSATILE BLOOD FLOW IN CONSTRICTED TAPERED ARTERY USING A VARIABLE-ORDER FRACTIONAL OLDROYD-B MODEL

PULSATILE BLOOD FLOW IN CONSTRICTED TAPERED ARTERY USING A VARIABLE-ORDER FRACTIONAL OLDROYD-B MODEL Bakh, H., e al.: Pulsale Blood Flo Cosced Tapeed Aey Usg... THERMAL SCIENCE: Yea 7, Vol., No. A, pp. 9-4 9 PULSATILE BLOOD FLOW IN CONSTRICTED TAPERED ARTERY USING A VARIABLE-ORDER FRACTIONAL OLDROYD-B

More information

The Properties of Probability of Normal Chain

The Properties of Probability of Normal Chain I. J. Coep. Mah. Sceces Vol. 8 23 o. 9 433-439 HIKARI Ld www.-hkar.co The Properes of Proaly of Noral Cha L Che School of Maheacs ad Sascs Zheghou Noral Uversy Zheghou Cy Hea Provce 4544 Cha cluu6697@sa.co

More information

Lagrangian & Hamiltonian Mechanics:

Lagrangian & Hamiltonian Mechanics: XII AGRANGIAN & HAMITONIAN DYNAMICS Iouco Hamlo aaoal Pcple Geealze Cooaes Geealze Foces agaga s Euao Geealze Momea Foces of Cosa, agage Mulples Hamloa Fucos, Cosevao aws Hamloa Dyamcs: Hamlo s Euaos agaga

More information

Degree of Approximation of Fourier Series

Degree of Approximation of Fourier Series Ieaioal Mahemaical Foum Vol. 9 4 o. 9 49-47 HIARI Ld www.m-hiai.com h://d.doi.og/.988/im.4.49 Degee o Aoimaio o Fouie Seies by N E Meas B. P. Padhy U.. Misa Maheda Misa 3 ad Saosh uma Naya 4 Deame o Mahemaics

More information

A Function Projective Synchronization Control for Complex Networks with Proportional Delays

A Function Projective Synchronization Control for Complex Networks with Proportional Delays Modelg, Smulao ad Opmzao echologes ad Applcaos MSOA 06 A Fuco Projecve Sychrozao Corol for Comple eworks wh Proporoal Delays Xulag Qu, Hoghua B,* ad Lca Chu Chegy Uversy College, Jme Uversy, Xame 60, Cha

More information

On EPr Bimatrices II. ON EP BIMATRICES A1 A Hence x. is said to be EP if it satisfies the condition ABx

On EPr Bimatrices II. ON EP BIMATRICES A1 A Hence x. is said to be EP if it satisfies the condition ABx Iteatoal Joual of Mathematcs ad Statstcs Iveto (IJMSI) E-ISSN: 3 4767 P-ISSN: 3-4759 www.jms.og Volume Issue 5 May. 4 PP-44-5 O EP matces.ramesh, N.baas ssocate Pofesso of Mathematcs, ovt. ts College(utoomous),Kumbakoam.

More information

A Generalized Recursive Coordinate Reduction Method for Multibody System Dynamics

A Generalized Recursive Coordinate Reduction Method for Multibody System Dynamics eaoal Joual fo Mulscale Compuaoal Egeeg, 1(2&3181 199 (23 A Geealzed Recusve Coodae Reduco Mehod fo Mulbody Sysem Dyamcs J. H. Cchley & K. S. Adeso Depame of Mechacal, Aeoaucal, ad Nuclea Egeeg, Resselae

More information

Mixed Integral Equation of Contact Problem in Position and Time

Mixed Integral Equation of Contact Problem in Position and Time Ieraoal Joural of Basc & Appled Sceces IJBAS-IJENS Vol: No: 3 ed Iegral Equao of Coac Problem Poso ad me. A. Abdou S. J. oaquel Deparme of ahemacs Faculy of Educao Aleadra Uversy Egyp Deparme of ahemacs

More information

APPLICATION OF A Z-TRANSFORMS METHOD FOR INVESTIGATION OF MARKOV G-NETWORKS

APPLICATION OF A Z-TRANSFORMS METHOD FOR INVESTIGATION OF MARKOV G-NETWORKS Joa of Aed Mahema ad Comaoa Meha 4 3( 6-73 APPLCATON OF A Z-TRANSFORMS METHOD FOR NVESTGATON OF MARKOV G-NETWORKS Mha Maay Vo Nameo e of Mahema Ceohowa Uey of Tehoogy Cęohowa Poad Fay of Mahema ad Come

More information

Density estimation III.

Density estimation III. Lecure 6 esy esmao III. Mlos Hausrec mlos@cs..eu 539 Seo Square Oule Oule: esy esmao: Bomal srbuo Mulomal srbuo ormal srbuo Eoeal famly aa: esy esmao {.. } a vecor of arbue values Objecve: ry o esmae e

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

Some Probability Inequalities for Quadratic Forms of Negatively Dependent Subgaussian Random Variables

Some Probability Inequalities for Quadratic Forms of Negatively Dependent Subgaussian Random Variables Joural of Sceces Islamc epublc of Ira 6(: 63-67 (005 Uvers of ehra ISSN 06-04 hp://scecesuacr Some Probabl Iequales for Quadrac Forms of Negavel Depede Subgaussa adom Varables M Am A ozorga ad H Zare 3

More information