9. MRAC Design for Affine-in-Control MIMO Systems
|
|
- Opal Rice
- 6 years ago
- Views:
Transcription
1 Lectue 5 9. MRAC Desg fo Affe--Cotol MIMO Systes Readg ateal: []: Chapte 8, Secto 8.3 []: Chapte 8, Secto 8.5 I ths secto, we cosde MRAC desg fo a class of ult-put-ult-output (MIMO) olea systes whose plat dyacs s lealy paaetezed, the ucetates satsfy the so-called atchg codtos, ad f the full state s easuable, (.e., avalable o-le as the syste output). Moe specfcally, cosde the th ode MIMO syste the fo: ( ) = A+ BΛ u+ f (9.) whee R s the syste state, u R s the cotol put, B R s kow at, A R ad Λ R ae ukow atces. I addto, t s assued that Λ s dagoal, ts eleets λ ae o-egatve, ad the pa ( A, BΛ ) s cotollable. he ucetaty Λ s toduced to odel a cotol falue pheoeo. Moeove, the ukow possbly olea fucto : f R R epesets the socalled syste atched ucetaty. It s assued that the fucto ca be wtte as a lea cobato of kow bouded bass fuctos wth ukow costat coeffcets. I (9.), kow egesso vecto. f =Θ Φ (9.) Θ R s the ukow costat at, whle Φ R epesets the he cotol objectve of the MIMO tackg poble s to choose the put vecto u such that all sgals the closed-loop syste ae bouded ad the state follows the state R of a efeece odel specfed by the LI syste ef whee A R ef s Huwtz, ef ef ef ef = A + B t (9.3) B R t ef, ad R s a bouded efeece put vecto. ote that the efeece odel ad ts eteal put ( t ) ust be chose so that () t epesets a desed tajectoy that ( t ) has to follow. I othe wods, the cotol ef
2 put u eeds to be chose such that the tackg eo vecto asyptotcally teds to zeo. l t t = 0 (9.4) t If the atces A ad Λ wee kow, oe could apply the cotol law ad obta the closed-loop syste u = K + K Θ Φ (9.5) ( ) = A+ BΛ K + BΛK (9.6) Copag (9.6) wth the desed dyacs (9.3), t follows that the deal (ukow) at gas ust be chose to satsfy the so-called atchg codtos: A+ BΛ K = A BΛ K = Bef ef (9.7) Assug that the atchg codtos take place, t s easy to see that the closed-loop syste s the sae as that of the efeece odel, ad cosequetly, asyptotc (epoetal) tackg s acheved fo ay bouded efeece put sgal () t. Reak 9. Gve the atces A, B, Λ, Aef, Bef, o K, K ay est to satsfy the atchg codtos (9.7) dcatg that the cotol law (9.5) ay ot have eough stuctual fleblty to eet the cotol objectve. Ofte pactce, the stuctue of A s kow, ad the efeece odel atces Aef, B ef ae chose so that (9.7) has a soluto fo K, K. Assug that K, K (9.7) est, cosde the followg cotol law: u = K + K Θ Φ (9.8) whee,, K R K R Θ R ae the estates of the deal ukow atces K, K, Θ, espectvely. he estated atces wll be geeated o-le ad by a appopate adaptve law.
3 Substtutg (9.8) to (9.), the closed-loop syste dyacs ca be wtte. ( ) ( ) = A+ BΛ K + BΛ K Θ Θ Φ (9.9) Subtactg (9.3) fo (9.9), closed-loop dyacs of the desoal tackg eo et = t t ca be obtaed. vecto () () () ef ( ) ( ) e = A+ BΛ K + BΛ K Θ Θ Φ A B (9.0) Usg atchg codtos (9.7) futhe yelds: ef ef ef ( ( )) ( ) ( ef ef ef ) ( ) ( ) ( ef ) e = A + BΛ K K A + BΛ K K BΛ Θ Θ Φ = A e+ BΛ K K + K K Θ Θ Φ (9.) Let K = K K, K = K K, ad Θ=Θ Θ epeset the paaete estato eos. I tes of the latte, the tackg eo dyacs becoes: e = Aef e+ BΛ K + K Θ Φ (9.) Vecto ad at os Befoe poceedg ay futhe, ecall that gve a at o s defed by A= a R j, the Fobeus j A = t A A = a (9.3) F wth t the tace opeato. O the othe had, gve ay vecto p-o, the duced at o s defed by, j A p A p = sup (9.4) 0 p Collecto of Facts about vecto ad at os, (pove t). Fo vecto -o =, the duced at o s equal to the au = absolute colu su, that s: A = a aj. j = 3
4 Fo vecto -o =, the duced at o s equal to the au = sgula value of A, that s: A σ ( A) Fo vecto -o absolute ow su, that s: =. a = a, the duced at o s equal to the au A = a a. j= he duced at o satsfes: A A, ad fo ay two copatbly p p p desoed atces, A ad B, oe also has: AB A B. p p p he Fobeus o s ot a duced o of ay vecto o, but t s copatble wth the -o the sese that: A A. F Fo ay two copatbly desoed atces A ad B, the Fobeus e poduct s defed as: AB, = A B. F Accodg to the Schwatz equalty oe has: AB, = A B A B. j F F F F F Fo ay two co desoal vectos a ad b, the tace detty takes place: a b= ba. t Let Γ =Γ > 0, Γ =Γ > 0, Γ Θ =Γ Θ > 0. Gog back to aalyzg the tackg eo dyacs (9.), cosde the Lyapuov fucto caddate: (,,, Θ ) = + t( Γ + Γ + Θ ΓΘ Θ Λ) V e K K e Pe K K K K (9.5) whee P= P > 0 satsfes the algebac Lyapuov equato PA + A P= Q (9.6) ef ef fo soe Q= Q > 0. he the te devatve of V, evaluated alog the tajectoes of (9.), ca be calculated. 4
5 V e Pe e Pe t = + + K Γ K + K Γ K + Θ ΓΘ Θ Λ ( ( )) ef ep Aef e B ( K K ) = A e+ BΛ K + K Θ Φ Pe + + Λ + Θ Φ t + K Γ K + K Γ K + Θ ΓΘ Θ Λ ( ef ef ) = e A P+ PA e+ e PBΛ ( K + K Θ Φ ) t K K K K + Γ + Γ + Θ ΓΘ Θ Λ Usg (9.6), yelds: ( ) ( Θ ) V = e Qe+ e PBΛ K + t K Γ K Λ + epbλ K + t K Γ K Λ + epbλ Θ Φ + t Θ Γ ΘΛ (9.7) (9.8) Usg the tace detty, oe gets epbλ K = t K epbλ b b a a epbλ K = t K epbλ b b a a epbλ Θ Φ = t Θ Φ( epb ) Λ a b b a (9.9) Substtutg (9.9) to (9.8), futhe yelds V e Qe t K = + K e PB Γ + Λ t t K K e PB + Γ + Λ + Θ ΓΘ Θ Φ e PB Λ (9.0) Adaptve laws ae chose as follows: 5
6 K = Γ e PB K = Γ () t e PB Θ=Γ Θ Φ epb (9.) he the te-devatve of V becoes egatve se-defte. V = e Qe 0 (9.) heefoe the closed-loop eo dyacs s stable, that s the tackg eo et ad the paaete estato eos K ( t), K ( t), Θ ( t) ae bouded sgals te. heefoe, the paaete estates K ( t ), K ( t ), Θ ( t) ae also bouded. Sce ( t ) s bouded the ef () t ad ef () t ad, cosequetly the cotol put s bouded ad, hece et () ae bouded. Hece, the syste state () t s bouded u t (9.8) s bouded. he latte ples that ( t) s bouded. Futheoe, the d te devatve of V () t V = e Qe= e Qe (9.3) s bouded ad thus V () t s a ufoly cotuous fucto of te. he latte coupled V t s lowe bouded ad V ( t) 0 ples (Babalat s Lea) that wth the facts that lv () t = 0 t. hus l et () = 0 ad the MIMO tackg poble s solved. t Reak 9. (pove t) If soe of the dagoal eleets λ of the ukow dagoal at Λ ae egatve ad the sgs of all of the ae kow, the the adaptve laws K = Γ e PBsg Λ K () t e PBsg = Γ Λ Θ =Γ Θ Φ e PBsg Λ (9.4) solve the MIMO tackg poble, whee [ λ λ ] sg dag sg,, sg Λ=. 0. Atfcal eual etwoks fo Fucto Appoato Motvato. 6
7 A typcal cotol desg pocess stats wth odelg whch s bascally the pocess of costuctg a atheatcal descpto (such as a set of ODE-s) fo the physcal syste to be cotolled. ote that oe accuate odels ae ot always bette. hey ay eque uecessaly cople cotol desg ad aalyss ad oe deadg coputato. he key hee s to odel essetal effects the syste dyacs the opeatg age of teest. I addto, a good odel should also povde soe chaactezato of the odel ucetates the so-called ukow ukows, whch ca be used fo obust desg, adaptve desg, o eely sulato ad syste testg, (such as Mote Calo us). Model ucetates ae the dffeeces betwee the odel ad the eal physcal pocess. Ucetates paaetes ae called paaetc, whle the othes ae called o-paaetc ucetates. Eaple 0. Fo the odel of a cotolled ass = u the ucetaty s paaetc, whle the eglected oto dyacs, easueet ose, seso dyacs epeset the opaaetc ucetates. Eaple 0. Cosde the scala odel wth uceta dyacs = f + u, whee f kow. Suppose that = θϕ + ε = θ Φ + ε f = Paaetc Ucetaty o-paaetc s ot that s suppose that the ukow fucto f ( ) ca be appoated by a lea cobato of kow bass fuctos ϕ ad ukow costat paaetes θ. he appoato eo ε s the o-paaetc ucetaty, whle the ukow costat paaetes θ epeset the paaetc ucetaty the syste dyacs. I ode to Φ chaacteze the latte, oe eeds to be able to fd a good set of bass fuctos such that the appoato eo ε becoes sall o a copact doa. Polyoals, Foue sees epasos, sples ad feedfowad eual etwoks ca be used to appoate fuctos o copact doas. I what follows, we show how to adapt to paaetc ucetates, whle ata obustess the pesece of o-paaetc ucetates. Defto 0. Atfcal Feedfowad eual etwoks ae ult-put-ult-output systes coposed of ay te-coected olea pocessg eleets (euos) opeatg paallel. Fgues 0. ad 0. show sketches of two feedfowad -s. 7
8 4 etwok Iput 5 etwok Output Iput ode 3 6 Output Fst Hdde Laye Secod Hdde Laye Fgue 0.: Feedfowad eual etwok wth hdde layes ad 6 euos Fgue 0.: Feedfowad eual etwok wth hdde laye ad 5 euos As see fo the eaples, a atfcal feedfowad eual etwok cossts of euos ad the coectos. A block-daga of a euo s show below. Σ y Fgue 0.3: Atfcal euo Block-Daga euos, the basc pocessg eleets of -s, have two a copoets: a weghted sue a olea actvato fucto 8
9 he actvato fuctos of teest ae Radal Bass Fuctos o dge fuctos, (ofte called the sgods). Defto 0. A Radal Bass Fucto (RBF) s defed as a Gaussa: I (0.), R s the put, (, ) c ( c) W( c) c W ϕ = e = e (0.) c R s the cete, ad W W 0 ϕ, c = > s a postve-defte = ϕ to abbevate syetc at of weghts. Most ofte we wll wte ad deote a RBF whch s ceteed at the th cete. Reak 0. Othe deftos of a RBF ae avalable. Ofte the lteatue, a RBF s defed as φ = φ( c W ), whee deotes the usual Eucldea weghted o. I addto, t W s equed that φ () s tegable o R ad φ d 0. Bascally, ths type of RBF depeds oly o the weghted dstace = c betwee ts cuet put ad the W cete c. he Gaussa RBF (0.) s a eaple of ths type of actvato fucto. Othes clude: ultquadcs vese ultquadcs R ϕ = + c, c> 0 ϕ =, c> 0 ( + c ) Mcchell s heoe ϕ = ϕ be the Gaussa, the ultquadcs, o the vese ultquadcs fucto. Let Let { } whose (, ) R. he the = be a set of dstct pots tepolato at Φ, th ϕ = ϕ, s osgula. j eleet s j ( j ) Reak 0. hee s a lage class of RBF-s that s coveed by Mcchell s theoe. he theoe povdes theoetcal bass fo RBF based fucto appoato pobles. I othe wods, usg a RBF ϕ = ϕ ad a fte set of pots { } = R, t s always 9
10 such possble to appoate a lage class of fuctos f ( ) wth f = θϕ( ) that f = f, fo all { } =. Defto 0.3 A dge fucto / sgod s a olea fucto the fo: ( w b) σ = σ + (0.) whee w R σ s a olea fucto (ot ecessaly cotuous) defed R wth the followg popetes: deotes the vecto of weghts, b s a scala called the theshold, ad l σ s lσ s ( s) ( s) < < (0.3) he ost coo eaples of a dge fucto ae: a) the logstc sgod ad b) the hypebolc taget σ σ ( s) ( s) = (0.4) s + e s e = (0.5) s + e A feedfowad wth euos ts hdde laye s show Fgue 0.4. Hdde Laye of euos Iput heshold y Output Bas Fgue 0.4: Sgle-hdde-laye feedfowad wth euos 0
11 Foally, a feedfowad aps R to R, that s: y =, R, y R (0.6) Defto 0.4 A sgodal feedfowad s: = W V + + b σ ( θ) (0.7) whee W R s the at of the oute-laye weghts, σ = σ V + θ σ V + θ R () ( ) ( ) s the vecto of sgods, V R s the at of the e-laye syaptc weghts wth ts th colu deoted by V R, θ R s the vecto of thesholds, ad b R deotes the bas vecto. Defto 0.5 A feedfowad RBF s: ϕ ( C W ) ( θ ) ϕ = θ + b = b =Θ Φ ϕ ϕ ( C Θ W ) Φ (0.8) + Θ= b R s the vecto of weghts, ( ) whee ( θ ) eceptve feld, W = W > 0 s the o weghtg at, b ( ) + ϕ ϕ R C R s the cete of the th R s the bas, ad Φ = s the so-called egesso vecto, whose copoets ae the bass actvato fuctos ϕ ϕ( C ) fucto. = ad the uty W Reak 0.3 Ofte pactcal applcatos, the syetc postve-defte at W (0.8) s chose to be dagoal ad the fo:
12 W =, =,, σ whee σ epesets the wdth of the th Gaussa fucto, that s: ϕ = e C σ copoet of the egesso vecto becoes the th Φ (0.8). Most ofte, the copoets of the egesso wll be costucted usg a sotopc Gaussa fucto ϕ = e C d a whose stadad devato (.e., wdth) σ s fed accodg to the spead of the cetes C, s the ube of cetes, ad d a s the au dstace betwee the chose cetes. I ths case, the stadad devato σ of all the sotopc Gaussa RBF copoets s fed at σ = d a hs foula esues that the dvdual RBF-s ae ot too peaked o too flat. Both of these two etee codtos should be avoded. Feedfowad -s have bee show to be capable of appoatg geec classes of fuctos, cludg cotuous ad tegable oes, o a copact doa ad to wth ay toleace. hs popety of feedfowad -s s ofte efeed to as the Uvesal Appoato popety, whle the -s theselves ae ofte called the uvesal appoatos. wo elated theoes ae gve below. Uvesal Appoato heoe fo Sgodal -s, (G. Cybeko, 989) Ay cotuous fucto f : R R ca be ufoly appoated by a sglehdde-laye wth a bouded ootoe-ceasg cotuous actvato fucto ad o a copact doa X R, that s: Reak 0.4 ε > 0 WbV,,,, θ X R W σ ( V + θ) + b f ε (0.9)
13 he uvesal appoato theoe eteds to the class of L fuctos o a copact doa. I that case, t s assued that the actvato fucto s a bouded easuable sgod ad the appoato s udestood tes of the L o. Rate of Appoato heoe fo Sgodal -s, (. Bao, 993) Cosde a class of fuctos f ( ) o R fo whch thee s a Foue epesetato of the fo ω f = e f ( ω ) dω fo soe cople-valued fucto f ( ω ) fo whch ω f ( ω ) C = ω f ( ω) dω < f R R s tegable, ad defe he fo evey fucto f ( ) wth C f fte, ad evey, thee ests a sgodal of the fo (0.7), such that Reak 0.5 Fuctos wth ( ) ( C ) f L f f d C f fte ae cotuously dffeetable o R d. Moeove, the appoato eo s easued by the tegated squaed eo, ( L o), o the ball of adus. Uvesal Appoato heoe fo RBF -s, (Pak ad Sadbeg, 99) Let ϕ : R R be a tegable bouded cotuous fucto ad assue that R ϕ d 0 he fo ay cotuous fucto f ( ) ad ay ε > 0 thee s a RBF wth euos, a set of cetes { } C =, ad a coo wdth σ > 0 such that C f = θϕ =Θ Φ = σ ϕ 3
14 f ( f ) d ε O L = Copaso of sgodal ad RBF -s RBF ad sgodal -s ae uvesal appoatos RBF depeds the Eucldea dstaces betwee the put vecto ad the cetes C. Meawhle, sgodal -s deped o the e poduct of the put vecto wth the syaptc weght vectos V ad based by θ. Sgodal -s povde O ate of appoato whch does ot eplctly deped o the deso of. O the othe had, the ate of appoato fo the RBF -s s of ode O ad, cosequetly t deceases epoetally as the deso of the put vecto ceases. hs pheoeo s called the Cuse of Desoalty, (due to R. Bella). A RBF has local suppot whle a sgod does ot. he local suppot ples leag ad adaptato ablty of RBF -s. Sgodal -s adapt but do t lea. Wth specfc efeece to -s cotol, t s the ablty to epeset olea appgs, ad hece to odel olea systes, whch s the featue to be ost eadly eploted the sythess of olea cotolles. Readg ateal / Refeeces: ) F. Scasell, A.C. so, Uvesal appoato usg feedfowad eual etwoks: A suvey of soe estg ethods, ad soe ew esults, eual etwoks, vol., o., pp. 5-37, 998. ) K.J. Hut, D. Sbabao, R. Zbkowsk, P.J. Gawthop, eual etwoks fo Cotol Systes A Suvey, Autoatca, vol. 8, o. 6., pp. 083-, 99. 3) G. Cybeko, Appoato by supeposto of a sgodal fucto, Math. Cotol Sgals Systes, vol., pp , ) J. Pak, I.W. Sadbeg, Uvesal appoato usg adal-bass-fucto etwoks, eual Coputato, vol. 3, o., pp ) C. A. Mcchell, Itepolato of scatteed data: Dstace atces ad codtoally postve defte fuctos, Costuctve Appoato, vol., pp. -, MRAC fo MIMO Systes wth Ustuctued Ucetates 4
15 I secto 9, we cosdeed affe--cotol MIMO systes the fo ( ) = A+ BΛ u+ f (.) whee R s the syste state vecto, u R s the cotol put, B R s a kow at, A R ad Λ R, (a dagoal at wth postve ukow eleets), ae ukow atces. A MRAC tackg desg was caed out, assug that the atched possbly olea uceta fucto f : R R could be eactly epeseted by a RBF the fo f =Θ Φ wth costat ukow coeffcets Θ R ad a fed kow Φ R. egesso vecto I ths secto, we assue that the ukow fucto f ( ) ca be appoated usg a RBF wth kow bass fuctos, (such as Gaussas wth fed cetes). I effect, usg Uvesal Appoato popety of RBF-s, t s assued that the ukow ϕ ad appg f ( ) ca be appoated by a RBF wth fed euos deal ukow costat weghts at Θ R ε f =Θ Φ + (.) o a copact doa that s: X R, ad to wth a gve appoato toleace a 0 ε, a ε >, ε X (.3) he cotol objectve s to desg a adaptve state feedback cotolle whch guaatees boudedess of all vaables the coespodg closed-loop syste, whle tackg the state R of the desed (ope-loop asyptotcally stable) efeece odel ef ef ef ef ef = A + B t (.4) whch s tu dve by a bouded efeece sgal ( t) R. ote that the MRAC cotolle ust opeate the pesece of the syste stuctued ad ustuctued ucetates, whee the latte s epeseted by the fucto appoato eo vecto ε R, whch satsfes (.3). he MRAC soluto s chose the sae fo as Secto 9: 5
16 u = K + K Θ Φ (.5) whee,, K R K R Θ R ae the adaptve paaetes. I ode fo the soluto to ests the followg odel atchg assuptos ust hold: A+ BΛ K = A BΛ K = Bef ef (.6) I, (.6), K, K ae the deal feedback / feedfowad ga atces. ote that oly estece of the deal gas s assued, whle the kowledge s ot equed. Reak. f =Θ Φ epesets the RBF based o-le fucto appoato. I (.5), Moeove, the coespodg fucto appoato eo f the paaete estato eo Θ. lealy depeds o ( ) ε ε f = f f = Θ Θ Φ = Θ Φ Θ (.7) We ow poceed wth the developet of the adaptve laws. he tackg eo dyacs s obtaed by subtactg (.4) fo (.) ad usg the atchg codtos (.6). It yelds: ( ) ( ) ε ( ) ef ef ef ( ) ( ) ( ef ) ε e = A+ BΛ K + BΛ K Θ Θ Φ + A B ef ef ef ( A B K K ) A B ( K K ) B B ε = + Λ + Λ Λ Θ Θ Φ + Λ = A e+ BΛ K K + K K Θ Θ Φ + o, equvaletly ε (.8) e = Aef e+ BΛ K + K Θ Φ + (.9) Let P= P > 0 be the soluto of the Lyapuov equato. Cosde the Lyapuov fucto caddate PA + A P= Q, Q= Q > 0 (.0) ef ef 6
17 (,,, Θ ) = + t( Γ + Γ + Θ ΓΘ Θ Λ) V e K K e Pe K K K K (.) whee Γ =Γ > 0, Γ =Γ > 0, Γ =Γ > 0 ae the ates of adaptato, ad Θ Θ K = K K K = K K Θ = Θ Θ (.) ae the paaete estato eos. Also (.), t ( ) deotes the tace of a at. he te devatve of V alog the tajectoes of the eo dyacs (.9) s gve by: V e Pe e Pe t K = + + Γ K + K Γ K + Θ ΓΘ Θ Λ ( ( ε )) ef ep Aef e B ( K K ε ) = A e+ BΛ K + K Θ Φ + Pe + + Λ + Θ Φ + t K + Γ K + K Γ K + Θ ΓΘ Θ Λ ( ef ef ) e A P PA e ( ) = + + epbλ K + K Θ Φ + ε t K + Γ K + K Γ K + Θ ΓΘ Θ Λ (.3) Regoupg the tes ad usg (.0) yelds: V = e Qe+ e PBΛε ( Θ ) + epbλ K + t K Γ KΛ + epbλ K + t K Γ K Λ + epbλ Θ Φ + t Θ Γ ΘΛ (.4) Usg the tace detty, oe ca wte: 7
18 epbλ K = t K epbλ b b a a epbλ K = t K epbλ b b a a epbλ Θ Φ = t Θ Φ( epb ) Λ a b b a (.5) Substtutg (.5) to (.4) esults : V = e Qe+ e PBΛε t K + K e PB Γ + Λ t t K K e PB + Γ + Λ + Θ ΓΘ Θ Φ e PB Λ (.6) Reak. If adaptve laws ae chose as Secto 9, that s the the te devatve becoes: K = Γ e PB K = Γ () t e PB Θ=Γ Θ Φ epb ( λ εa ) V = e Qe+ e PBΛε λ Q e + PBΛ ε e = e Q e PBΛ Cosequetly, V < 0 outsde of the copact set the e doa: a E = e R : e PBΛ ε λ a Q Hece, oe ay attept to ague that the tackg eo vecto e s UUB. Ufotuately, sde E othg ca be sad about the adaptve gas K,, K Θ. he adaptve laws ae odfed as follows: 8
19 ( ) ( () ) ( K Φ e PB) K =Γ Poj K, e PB K =Γ Poj K, t e PB Θ=Γ Θ Poj, (.7) whee Poj (, ) deotes the Pojecto Opeato. he opeato aps two ( ) atces Ω= [ θ θ ] R ad Y = [ y y ] R to the ( ) Poj ( Ω, Y ), ad t s defed colu-wse, that s ( Y) ( ( θ y) ( θ y) ) at Poj Ω, = Poj, Poj, (.8) I patcula, the opeato copoets ae: ( θ j y j) Poj, ( θ ) f ( θ ) f ( θ j ) f j j yj y j f ( θ j), f f ( θ j) > 0 yj f ( θ j) > 0 = (.9) y j, f ot whee f ( θ j ): R R s a cove fucto. Gve θ a j the au allowable agtude of the vecto θ j, ad ε j > 0, the fucto defto s gve below. f ( θ j ) θ j θ = (.0) εθ j a j a j Key popetes ad geoetc tepetato of the Pojecto Opeato ae dscussed the et secto. Refeeces. J.J. Slote, W. L, Appled olea Cotol, Petce Hall, H.K. Khall, olea Systes, 3 d Edto, Petce Hall, ew Jesey, S. Hayk, eual etwoks: A Copehesve Foudato, d Edto, Petce Hall, ew Jesey, 99. 9
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 information14. MRAC for MIMO Systems with Unstructured Uncertainties We consider affine-in-control MIMO systems in the form, x Ax B u f x t
Lectue 8 14. MAC o MIMO Systes wth Ustuctued Ucetates We cosde ae--cotol MIMO systes the o, ABu t (14.1) whee s the syste state vecto, u s the cotol put, B s kow costat at, A ad (a dagoal at wth postve
More information13. Artificial Neural Networks for Function Approximation
Lecture 7 3. Artfcal eural etworks for Fucto Approxmato Motvato. A typcal cotrol desg process starts wth modelg, whch s bascally the process of costructg a mathematcal descrpto (such as a set of ODE-s)
More information= 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 informationχ be any function of X and Y then
We have show that whe we ae gve Y g(), the [ ] [ g() ] g() f () Y o all g ()() f d fo dscete case Ths ca be eteded to clude fuctos of ay ube of ado vaables. Fo eaple, suppose ad Y ae.v. wth jot desty fucto,
More information13. Parametric and Non-Parametric Uncertainties, Radial Basis Functions and Neural Network Approximations
Lecture 7 3. Parametrc ad No-Parametrc Ucertates, Radal Bass Fuctos ad Neural Network Approxmatos he parameter estmato algorthms descrbed prevous sectos were based o the assumpto that the system ucertates
More informationRelation (12.1) states that if two points belong to the convex subset Ω then all the points on the connecting line also belong to Ω.
Lectue 6. Poectio Opeato Deiitio A.: Subset Ω R is cove i [ y Ω R ] λ + λ [ y = z Ω], λ,. Relatio. states that i two poits belog to the cove subset Ω the all the poits o the coectig lie also belog to Ω.
More informationRECAPITULATION & 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 informationSome Different Perspectives on Linear Least Squares
Soe Dfferet Perspectves o Lear Least Squares A stadard proble statstcs s to easure a respose or depedet varable, y, at fed values of oe or ore depedet varables. Soetes there ests a deterstc odel y f (,,
More informationXII. 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 informationChapter 2: Descriptive Statistics
Chapte : Decptve Stattc Peequte: Chapte. Revew of Uvaate Stattc The cetal teecy of a oe o le yetc tbuto of a et of teval, o hghe, cale coe, ofte uaze by the athetc ea, whch efe a We ca ue the ea to ceate
More information7.0 Equality Contraints: Lagrange Multipliers
Systes Optzato 7.0 Equalty Cotrats: Lagrage Multplers Cosder the zato of a o-lear fucto subject to equalty costrats: g f() R ( ) 0 ( ) (7.) where the g ( ) are possbly also olear fuctos, ad < otherwse
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
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 informationProfessor 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 informationFIBONACCI-LIKE SEQUENCE ASSOCIATED WITH K-PELL, K-PELL-LUCAS AND MODIFIED K-PELL SEQUENCES
Joual of Appled Matheatcs ad Coputatoal Mechacs 7, 6(), 59-7 www.ac.pcz.pl p-issn 99-9965 DOI:.75/jac.7..3 e-issn 353-588 FIBONACCI-LIKE SEQUENCE ASSOCIATED WITH K-PELL, K-PELL-LUCAS AND MODIFIED K-PELL
More informationA Convergence Analysis of Discontinuous Collocation Method for IAEs of Index 1 Using the Concept Strongly Equivalent
Appled ad Coputatoal Matheatcs 27; 7(-): 2-7 http://www.scecepublshggoup.co//ac do:.648/.ac.s.287.2 ISSN: 2328-565 (Pt); ISSN: 2328-563 (Ole) A Covegece Aalyss of Dscotuous Collocato Method fo IAEs of
More informationThe Linear Probability Density Function of Continuous Random Variables in the Real Number Field and Its Existence Proof
MATEC Web of Cofeeces ICIEA 06 600 (06) DOI: 0.05/mateccof/0668600 The ea Pobablty Desty Fucto of Cotuous Radom Vaables the Real Numbe Feld ad Its Estece Poof Yya Che ad Ye Collee of Softwae, Taj Uvesty,
More informationA New Method for Solving Fuzzy Linear. Programming by Solving Linear Programming
ppled Matheatcal Sceces Vol 008 o 50 7-80 New Method for Solvg Fuzzy Lear Prograg by Solvg Lear Prograg S H Nasser a Departet of Matheatcs Faculty of Basc Sceces Mazadara Uversty Babolsar Ira b The Research
More informationSUBSEQUENCE CHARACTERIZAT ION OF UNIFORM STATISTICAL CONVERGENCE OF DOUBLE SEQUENCE
Reseach ad Coucatos atheatcs ad atheatcal ceces Vol 9 Issue 7 Pages 37-5 IN 39-6939 Publshed Ole o Novebe 9 7 7 Jyot cadec Pess htt//yotacadecessog UBEQUENCE CHRCTERIZT ION OF UNIFOR TTITIC CONVERGENCE
More informationRobust Adaptive Asymptotic Tracking of Nonlinear Systems With Additive Disturbance
54 IEEE TRANSACTIONS ON AUTOMATIC CONTROL, VOL. 5, NO. 3, MARCH 6 Robust Adaptve Asymptotc Tackg of Nolea Systems Wth Addtve Dstubace Z. Ca, M. S. de Queoz, D. M. Dawso Abstact Ths ote deals wth the tackg
More informationFairing 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 informationExponential Generating Functions - J. T. Butler
Epoetal Geeatg Fuctos - J. T. Butle Epoetal Geeatg Fuctos Geeatg fuctos fo pemutatos. Defto: a +a +a 2 2 + a + s the oday geeatg fucto fo the sequece of teges (a, a, a 2, a, ). Ep. Ge. Fuc.- J. T. Butle
More informationsuch that for 1 From the definition of the k-fibonacci numbers, the firsts of them are presented in Table 1. Table 1: First k-fibonacci numbers F 1
Scholas Joual of Egeeg ad Techology (SJET) Sch. J. Eg. Tech. 0; (C):669-67 Scholas Academc ad Scetfc Publshe (A Iteatoal Publshe fo Academc ad Scetfc Resouces) www.saspublshe.com ISSN -X (Ole) ISSN 7-9
More informationOn 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 informationA GENERAL CLASS OF ESTIMATORS UNDER MULTI PHASE SAMPLING
TATITIC IN TRANITION-ew sees Octobe 9 83 TATITIC IN TRANITION-ew sees Octobe 9 Vol. No. pp. 83 9 A GENERAL CLA OF ETIMATOR UNDER MULTI PHAE AMPLING M.. Ahed & Atsu.. Dovlo ABTRACT Ths pape deves the geeal
More informationVECTOR MECHANICS FOR ENGINEERS: Vector Mechanics for Engineers: Dynamics. In the current chapter, you will study the motion of systems of particles.
Seeth Edto CHPTER 4 VECTOR MECHNICS FOR ENINEERS: DYNMICS Fedad P. ee E. Russell Johsto, J. Systems of Patcles Lectue Notes: J. Walt Ole Texas Tech Uesty 003 The Mcaw-Hll Compaes, Ic. ll ghts eseed. Seeth
More informationVIII Dynamics of Systems of Particles
VIII Dyacs of Systes of Patcles Cete of ass: Cete of ass Lea oetu of a Syste Agula oetu of a syste Ketc & Potetal Eegy of a Syste oto of Two Iteactg Bodes: The Reduced ass Collsos: o Elastc Collsos R whee:
More informationObjectives. Learning Outcome. 7.1 Centre of Gravity (C.G.) 7. Statics. Determine the C.G of a lamina (Experimental method)
Ojectves 7 Statcs 7. Cete of Gavty 7. Equlum of patcles 7.3 Equlum of g oes y Lew Sau oh Leag Outcome (a) efe cete of gavty () state the coto whch the cete of mass s the cete of gavty (c) state the coto
More informationON THE CONVERGENCE THEOREMS OF THE McSHANE INTEGRAL FOR RIESZ-SPACES-VALUED FUNCTIONS DEFINED ON REAL LINE
O The Covegece Theoems... (Muslm Aso) ON THE CONVERGENCE THEOREMS OF THE McSHANE INTEGRAL FOR RIESZ-SPACES-VALUED FUNCTIONS DEFINED ON REAL LINE Muslm Aso, Yosephus D. Sumato, Nov Rustaa Dew 3 ) Mathematcs
More informationThe Mathematical Appendix
The Mathematcal Appedx Defto A: If ( Λ, Ω, where ( λ λ λ whch the probablty dstrbutos,,..., Defto A. uppose that ( Λ,,..., s a expermet type, the σ-algebra o λ λ λ are defed s deoted by ( (,,...,, σ Ω.
More informationKURODA S METHOD FOR CONSTRUCTING CONSISTENT INPUT-OUTPUT DATA SETS. Peter J. Wilcoxen. Impact Research Centre, University of Melbourne.
KURODA S METHOD FOR CONSTRUCTING CONSISTENT INPUT-OUTPUT DATA SETS by Peter J. Wlcoxe Ipact Research Cetre, Uversty of Melboure Aprl 1989 Ths paper descrbes a ethod that ca be used to resolve cossteces
More informationNon-axial symmetric loading on axial symmetric. Final Report of AFEM
No-axal symmetc loadg o axal symmetc body Fal Repot of AFEM Ths poject does hamoc aalyss of o-axal symmetc loadg o axal symmetc body. Shuagxg Da, Musket Kamtokat 5//009 No-axal symmetc loadg o axal symmetc
More informationIdentifying Linear Combinations of Ridge Functions
Advaces Appled Matheatcs 22, 103118 Ž 1999. Atcle ID aaa.1998.0623, avalable ole at http:www.dealbay.co o Idetfyg Lea Cobatos of Rdge Fuctos Mat D. Buha Matheatk, Lehstuhl 8, Uestat Dotud, 44221 Dotud,
More informationConsider two masses m 1 at x = x 1 and m 2 at x 2.
Chapte 09 Syste of Patcles Cete of ass: The cete of ass of a body o a syste of bodes s the pot that oes as f all of the ass ae cocetated thee ad all exteal foces ae appled thee. Note that HRW uses co but
More informationMinimizing spherical aberrations Exploiting the existence of conjugate points in spherical lenses
Mmzg sphecal abeatos Explotg the exstece of cojugate pots sphecal leses Let s ecall that whe usg asphecal leses, abeato fee magg occus oly fo a couple of, so called, cojugate pots ( ad the fgue below)
More informationMu Sequences/Series Solutions National Convention 2014
Mu Sequeces/Seres Solutos Natoal Coveto 04 C 6 E A 6C A 6 B B 7 A D 7 D C 7 A B 8 A B 8 A C 8 E 4 B 9 B 4 E 9 B 4 C 9 E C 0 A A 0 D B 0 C C Usg basc propertes of arthmetc sequeces, we fd a ad bm m We eed
More information2. Sample Space: The set of all possible outcomes of a random experiment is called the sample space. It is usually denoted by S or Ω.
Ut: Rado expeet saple space evets classcal defto of pobablty ad the theoes of total ad copoud pobablty based o ths defto axoatc appoach to the oto of pobablty potat theoes based o ths appoach codtoal pobablty
More information2/20/2013. Topics. Power Flow Part 1 Text: Power Transmission. Power Transmission. Power Transmission. Power Transmission
/0/0 Topcs Power Flow Part Text: 0-0. Power Trassso Revsted Power Flow Equatos Power Flow Proble Stateet ECEGR 45 Power Systes Power Trassso Power Trassso Recall that for a short trassso le, the power
More informationRobust Stabilization of Uncertain Nonlinear Systems via Fuzzy Modeling and Numerical Optimization Programming
Iteatoal Robust Joual Stablzato of Cotol, of Uceta Autoato, Nolea ad Systes, va vol Fuzzy 3, o Modelg, pp 5-35, ad Nuecal Jue 5 Optzato 5 Robust Stablzato of Uceta Nolea Systes va Fuzzy Modelg ad Nuecal
More informationUniversity of Pavia, Pavia, Italy. North Andover MA 01845, USA
Iteatoal Joual of Optmzato: heoy, Method ad Applcato 27-5565(Pt) 27-6839(Ole) wwwgph/otma 29 Global Ifomato Publhe (HK) Co, Ltd 29, Vol, No 2, 55-59 η -Peudoleaty ad Effcecy Gogo Gog, Noma G Rueda 2 *
More informationStability Analysis for Linear Time-Delay Systems. Described by Fractional Parameterized. Models Possessing Multiple Internal. Constant Discrete Delays
Appled Mathematcal Sceces, Vol. 3, 29, o. 23, 5-25 Stablty Aalyss fo Lea me-delay Systems Descbed by Factoal Paametezed Models Possessg Multple Iteal Costat Dscete Delays Mauel De la Se Isttuto de Ivestgacó
More informationRecent Advances in Computers, Communications, Applied Social Science and Mathematics
Recet Advaces Computes, Commucatos, Appled ocal cece ad athematcs Coutg Roots of Radom Polyomal Equatos mall Itevals EFRAI HERIG epatmet of Compute cece ad athematcs Ael Uvesty Cete of amaa cece Pa,Ael,4487
More informationA DATA DRIVEN PARAMETER ESTIMATION FOR THE THREE- PARAMETER WEIBULL POPULATION FROM CENSORED SAMPLES
Mathematcal ad Computatoal Applcatos, Vol. 3, No., pp. 9-36 008. Assocato fo Scetfc Reseach A DATA DRIVEN PARAMETER ESTIMATION FOR THE THREE- PARAMETER WEIBULL POPULATION FROM CENSORED SAMPLES Ahmed M.
More informationOn Eigenvalues of Nonlinear Operator Pencils with Many Parameters
Ope Scece Joual of Matheatc ad Applcato 5; 3(4): 96- Publhed ole Jue 5 (http://wwwopececeoleco/oual/oa) O Egevalue of Nolea Opeato Pecl wth May Paaete Rakhhada Dhabaadeh Guay Salaova Depatet of Fuctoal
More informationAn Unconstrained Q - G Programming Problem and its Application
Joual of Ifomato Egeeg ad Applcatos ISS 4-578 (pt) ISS 5-0506 (ole) Vol.5, o., 05 www.ste.og A Ucostaed Q - G Pogammg Poblem ad ts Applcato M. He Dosh D. Chag Tved.Assocate Pofesso, H L College of Commece,
More informationRademacher Complexity. Examples
Algorthmc Foudatos of Learg Lecture 3 Rademacher Complexty. Examples Lecturer: Patrck Rebesch Verso: October 16th 018 3.1 Itroducto I the last lecture we troduced the oto of Rademacher complexty ad showed
More informationchinaxiv: v1
Matheatcal pcple of essto etwos Zh-Zhog Ta * Zhe Ta. Depatet of physcs, Natog Uvesty, Natog, 69, ha. School of Ifoato Scece ad Techology, Natog Uvesty, Natog, 69, ha (9-3-) Abstact The ufed pocessg ad
More informationGREEN 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 informationThis may involve sweep, revolution, deformation, expansion and forming joints with other curves.
5--8 Shapes ae ceated by cves that a sface sch as ooftop of a ca o fselage of a acaft ca be ceated by the moto of cves space a specfed mae. Ths may volve sweep, evolto, defomato, expaso ad fomg jots wth
More informationCS 2750 Machine Learning. Lecture 7. Linear regression. CS 2750 Machine Learning. Linear regression. is a linear combination of input components x
CS 75 Mache Learg Lecture 7 Lear regresso Mlos Hauskrecht los@cs.ptt.edu 59 Seott Square CS 75 Mache Learg Lear regresso Fucto f : X Y s a lear cobato of put copoets f + + + K d d K k - paraeters eghts
More informationAssignment 5/MATH 247/Winter Due: Friday, February 19 in class (!) (answers will be posted right after class)
Assgmet 5/MATH 7/Wter 00 Due: Frday, February 9 class (!) (aswers wll be posted rght after class) As usual, there are peces of text, before the questos [], [], themselves. Recall: For the quadratc form
More informationLecture 10: Condensed matter systems
Lectue 0: Codesed matte systems Itoducg matte ts codesed state.! Ams: " Idstgushable patcles ad the quatum atue of matte: # Cosequeces # Revew of deal gas etopy # Femos ad Bosos " Quatum statstcs. # Occupato
More informationCoherent Potential Approximation
Coheret Potetal Approxato Noveber 29, 2009 Gree-fucto atrces the TB forals I the tght bdg TB pcture the atrx of a Haltoa H s the for H = { H j}, where H j = δ j ε + γ j. 2 Sgle ad double uderles deote
More informationPiecewise Quadratic Stability of Closed-loop Takagi-Sugeno Fuzzy Systems
7 Poceedgs of the Iteatoal Cofeece o Ifoato Autoato Decebe -8 Colobo S Laa Pecewse Quadatc Stablty of Closed-loop aag-sugeo Fuzzy Systes Pet Huše Mguel Beal Depatet of Cotol geeeg Faculty of lectcal geeg
More informationX ε ) = 0, or equivalently, lim
Revew for the prevous lecture Cocepts: order statstcs Theorems: Dstrbutos of order statstcs Examples: How to get the dstrbuto of order statstcs Chapter 5 Propertes of a Radom Sample Secto 55 Covergece
More informationA Variable Structure Model Reference Adaptive Control For MIMO Systems
Proceedgs of the Iteratoal ultcoferece of Egeers ad Coputer Scetsts 8 Vol II IECS 8 9- arch 8 Hog Kog A Varale Structure odel Referece Adaptve Cotrol For IO Systes Ardeshr Kara ohaad Astract A Varale Structure
More informationConnectionist Models. Artificial Neural Networks. When to Consider Neural Networks. Decision Surface of Perceptron. Perceptron
Atfcal Neual Netos Theshol uts Gaet escet Multlaye etos Bacpopagato He laye epesetatos Example: Face ecogto Avace topcs Coectost Moels Cose humas Neuo stchg tme ~. seco Numbe of euos ~ Coectos pe euo ~
More information( ) ( ) ( ( )) ( ) ( ) ( ) ( ) ( ) = ( ) ( ) + ( ) ( ) = ( ( )) ( ) + ( ( )) ( ) Review. Second Derivatives for f : y R. Let A be an m n matrix.
Revew + v, + y = v, + v, + y, + y, Cato! v, + y, + v, + y geeral Let A be a atr Let f,g : Ω R ( ) ( ) R y R Ω R h( ) f ( ) g ( ) ( ) ( ) ( ( )) ( ) dh = f dg + g df A, y y A Ay = = r= c= =, : Ω R he Proof
More informationA Conventional Approach for the Solution of the Fifth Order Boundary Value Problems Using Sixth Degree Spline Functions
Appled Matheatcs, 1, 4, 8-88 http://d.do.org/1.4/a.1.448 Publshed Ole Aprl 1 (http://www.scrp.org/joural/a) A Covetoal Approach for the Soluto of the Ffth Order Boudary Value Probles Usg Sth Degree Sple
More informationNONDIFFERENTIABLE MATHEMATICAL PROGRAMS. OPTIMALITY AND HIGHER-ORDER DUALITY RESULTS
HE PUBLISHING HOUSE PROCEEDINGS OF HE ROMANIAN ACADEMY, See A, OF HE ROMANIAN ACADEMY Volue 9, Nube 3/8,. NONDIFFERENIABLE MAHEMAICAL PROGRAMS. OPIMALIY AND HIGHER-ORDER DUALIY RESULS Vale PREDA Uvety
More information1 Lyapunov Stability Theory
Lyapuov Stablty heory I ths secto we cosder proofs of stablty of equlbra of autoomous systems. hs s stadard theory for olear systems, ad oe of the most mportat tools the aalyss of olear systems. It may
More informationPROJECTION PROBLEM FOR REGULAR POLYGONS
Joural of Mathematcal Sceces: Advaces ad Applcatos Volume, Number, 008, Pages 95-50 PROJECTION PROBLEM FOR REGULAR POLYGONS College of Scece Bejg Forestry Uversty Bejg 0008 P. R. Cha e-mal: sl@bjfu.edu.c
More informationφ (x,y,z) in the direction of a is given by
UNIT-II VECTOR CALCULUS Dectoal devatve The devatve o a pot ucto (scala o vecto) a patcula decto s called ts dectoal devatve alo the decto. The dectoal devatve o a scala pot ucto a ve decto s the ate o
More informationAn Expanded Method to Robustly Practically. Output Tracking Control. for Uncertain Nonlinear Systems
It Joual of Math Aalyss, Vol 8, 04, o 8, 865-879 HIKARI Ltd, wwwm-hacom http://ddoog/0988/jma044368 A Epaded Method to Robustly Pactcally Output Tacg Cotol fo Uceta Nolea Systems Keyla Almha, Naohsa Otsua,
More informationDebabrata Dey and Atanu Lahiri
RESEARCH ARTICLE QUALITY COMPETITION AND MARKET SEGMENTATION IN THE SECURITY SOFTWARE MARKET Debabrata Dey ad Atau Lahr Mchael G. Foster School of Busess, Uersty of Washgto, Seattle, Seattle, WA 9895 U.S.A.
More informationOptimality Criteria for a Class of Multi-Objective Nonlinear Integer Programs
Coucatos Appled Sceces ISSN -77 Volue, Nube,, 7-77 Optalty Ctea o a Class o Mult-Obectve Nolea Itege Pogas Shal Bhagava Depatet o Matheatcs, Babu Shvath Agawal College, Mathua (UP) Ida Abstact hs pape
More informationSolutions to problem set ); (, ) (
Solutos to proble set.. L = ( yp p ); L = ( p p ); y y L, L = yp p, p p = yp p, + p [, p ] y y y = yp + p = L y Here we use for eaple that yp, p = yp p p yp = yp, p = yp : factors that coute ca be treated
More information2.1.1 The Art of Estimation Examples of Estimators Properties of Estimators Deriving Estimators Interval Estimators
. ploatoy Statstcs. Itoducto to stmato.. The At of stmato.. amples of stmatos..3 Popetes of stmatos..4 Devg stmatos..5 Iteval stmatos . Itoducto to stmato Samplg - The samplg eecse ca be epeseted by a
More informationChapter 7 Varying Probability Sampling
Chapte 7 Vayg Pobablty Samplg The smple adom samplg scheme povdes a adom sample whee evey ut the populato has equal pobablty of selecto. Ude ceta ccumstaces, moe effcet estmatos ae obtaed by assgg uequal
More informationPolyphase Filters. Section 12.4 Porat
Polyphase Flters Secto.4 Porat .4 Polyphase Flters Polyphase s a way of dog saplg-rate coverso that leads to very effcet pleetatos. But ore tha that, t leads to very geeral vewpots that are useful buldg
More informationSome results and conjectures about recurrence relations for certain sequences of binomial sums.
Soe results ad coectures about recurrece relatos for certa sequeces of boal sus Joha Cgler Faultät für Matheat Uverstät We A-9 We Nordbergstraße 5 Joha Cgler@uveacat Abstract I a prevous paper [] I have
More informationFunctions of Random Variables
Fuctos of Radom Varables Chapter Fve Fuctos of Radom Varables 5. Itroducto A geeral egeerg aalyss model s show Fg. 5.. The model output (respose) cotas the performaces of a system or product, such as weght,
More informationPRACTICAL CONSIDERATIONS IN HUMAN-INDUCED VIBRATION
PRACTICAL CONSIDERATIONS IN HUMAN-INDUCED VIBRATION Bars Erkus, 4 March 007 Itroducto Ths docuet provdes a revew of fudaetal cocepts structural dyacs ad soe applcatos hua-duced vbrato aalyss ad tgato of
More informationJournal of Engineering Science and Technology Review 7 (2) (2014) Research Article
Jest Joual of Egeeg Scece ad echology Revew 7 () (4) 75 8 Reseach Atcle JOURNAL OF Egeeg Scece ad echology Revew www.jest.og Fuzzy Backsteppg Sldg Mode Cotol fo Msmatched Uceta System H. Q. Hou,*, Q. Mao,
More informationNumerical Analysis Formulae Booklet
Numercal Aalyss Formulae Booklet. Iteratve Scemes for Systems of Lear Algebrac Equatos:.... Taylor Seres... 3. Fte Dfferece Approxmatos... 3 4. Egevalues ad Egevectors of Matrces.... 3 5. Vector ad Matrx
More informationNon-degenerate Perturbation Theory
No-degeerate Perturbato Theory Proble : H E ca't solve exactly. But wth H H H' H" L H E Uperturbed egevalue proble. Ca solve exactly. E Therefore, kow ad. H ' H" called perturbatos Copyrght Mchael D. Fayer,
More informationMATH 247/Winter Notes on the adjoint and on normal operators.
MATH 47/Wter 00 Notes o the adjot ad o ormal operators I these otes, V s a fte dmesoal er product space over, wth gve er * product uv, T, S, T, are lear operators o V U, W are subspaces of V Whe we say
More informationChapter 5 Properties of a Random Sample
Lecture 6 o BST 63: Statstcal Theory I Ku Zhag, /0/008 Revew for the prevous lecture Cocepts: t-dstrbuto, F-dstrbuto Theorems: Dstrbutos of sample mea ad sample varace, relatoshp betwee sample mea ad sample
More informationMinimum 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 informationSUBCLASS OF HARMONIC UNIVALENT FUNCTIONS ASSOCIATED WITH SALAGEAN DERIVATIVE. Sayali S. Joshi
Faculty of Sceces ad Matheatcs, Uversty of Nš, Serba Avalable at: http://wwwpfacyu/float Float 3:3 (009), 303 309 DOI:098/FIL0903303J SUBCLASS OF ARMONIC UNIVALENT FUNCTIONS ASSOCIATED WIT SALAGEAN DERIVATIVE
More informationOn the limiting law of the length of the longest common and increasing subsequences in random words
O the ltg law of the legth of the logest coo ad ceasg subsequeces ado wods axv:505.0664v [ath.pr] 0 Sep 06 Jea-Chstophe Beto IRMAR, UMR 665, Uvesté de Rees, 63 Aveue du Gééal Leclec CS 7405, 3504, Rees,
More informationOverview. Review Superposition Solution. Review Superposition. Review x and y Swap. Review General Superposition
ylcal aplace Soltos ebay 6 9 aplace Eqato Soltos ylcal Geoety ay aetto Mechacal Egeeg 5B Sea Egeeg Aalyss ebay 6 9 Ovevew evew last class Speposto soltos tocto to aal cooates Atoal soltos of aplace s eqato
More informationHarmonic Curvatures in Lorentzian Space
BULLETIN of the Bull Malaya Math Sc Soc Secod See 7-79 MALAYSIAN MATEMATICAL SCIENCES SOCIETY amoc Cuvatue Loetza Space NEJAT EKMEKÇI ILMI ACISALIOĞLU AND KĀZIM İLARSLAN Aaa Uvety Faculty of Scece Depatmet
More informationRANDOM SYSTEMS WITH COMPLETE CONNECTIONS AND THE GAUSS PROBLEM FOR THE REGULAR CONTINUED FRACTIONS
RNDOM SYSTEMS WTH COMPETE CONNECTONS ND THE GUSS PROBEM FOR THE REGUR CONTNUED FRCTONS BSTRCT Da ascu o Coltescu Naval cademy Mcea cel Bata Costata lascuda@gmalcom coltescu@yahoocom Ths pape peset the
More informationC-1: Aerodynamics of Airfoils 1 C-2: Aerodynamics of Airfoils 2 C-3: Panel Methods C-4: Thin Airfoil Theory
ROAD MAP... AE301 Aerodyamcs I UNIT C: 2-D Arfols C-1: Aerodyamcs of Arfols 1 C-2: Aerodyamcs of Arfols 2 C-3: Pael Methods C-4: Th Arfol Theory AE301 Aerodyamcs I Ut C-3: Lst of Subects Problem Solutos?
More informationStrong Convergence of Weighted Averaged Approximants of Asymptotically Nonexpansive Mappings in Banach Spaces without Uniform Convexity
BULLETIN of the MALAYSIAN MATHEMATICAL SCIENCES SOCIETY Bull. Malays. Math. Sc. Soc. () 7 (004), 5 35 Strog Covergece of Weghted Averaged Appromats of Asymptotcally Noepasve Mappgs Baach Spaces wthout
More informationPoint Estimation: definition of estimators
Pot Estmato: defto of estmators Pot estmator: ay fucto W (X,..., X ) of a data sample. The exercse of pot estmato s to use partcular fuctos of the data order to estmate certa ukow populato parameters.
More informationPENALTY FUNCTIONS FOR THE MULTIOBJECTIVE OPTIMIZATION PROBLEM
Joual o Mathematcal Sceces: Advaces ad Applcatos Volume 6 Numbe 00 Pages 77-9 PENALTY FUNCTIONS FOR THE MULTIOBJECTIVE OPTIMIZATION PROBLEM DAU XUAN LUONG ad TRAN VAN AN Depatmet o Natual Sceces Quag Nh
More informationGeneralized Duality for a Nondifferentiable Control Problem
Aeca Joal of Appled Matheatcs ad Statstcs, 4, Vol., No. 4, 93- Avalable ole at http://pbs.scepb.co/aas//4/3 Scece ad Edcato Pblsh DO:.69/aas--4-3 Geealzed Dalty fo a Nodffeetable Cotol Poble. Hsa,*, Vkas
More informationFeature Selection: Part 2. 1 Greedy Algorithms (continued from the last lecture)
CSE 546: Mache Learg Lecture 6 Feature Selecto: Part 2 Istructor: Sham Kakade Greedy Algorthms (cotued from the last lecture) There are varety of greedy algorthms ad umerous amg covetos for these algorthms.
More informationInequalities for Dual Orlicz Mixed Quermassintegrals.
Advaces Pue Mathematcs 206 6 894-902 http://wwwscpog/joual/apm IN Ole: 260-0384 IN Pt: 260-0368 Iequaltes fo Dual Olcz Mxed Quemasstegals jua u chool of Mathematcs ad Computatoal cece Hua Uvesty of cece
More information18.413: Error Correcting Codes Lab March 2, Lecture 8
18.413: Error Correctg Codes Lab March 2, 2004 Lecturer: Dael A. Spelma Lecture 8 8.1 Vector Spaces A set C {0, 1} s a vector space f for x all C ad y C, x + y C, where we take addto to be compoet wse
More informationOrder Nonlinear Vector Differential Equations
It. Joural of Math. Aalyss Vol. 3 9 o. 3 39-56 Coverget Power Seres Solutos of Hgher Order Nolear Vector Dfferetal Equatos I. E. Kougas Departet of Telecoucato Systes ad Networs Techologcal Educatoal Isttute
More informationTrace of Positive Integer Power of Adjacency Matrix
Global Joual of Pue ad Appled Mathematcs. IN 097-78 Volume, Numbe 07), pp. 079-087 Reseach Ida Publcatos http://www.publcato.com Tace of Postve Itege Powe of Adacecy Matx Jagdsh Kuma Pahade * ad Mao Jha
More informationAn Enhanced Russell Measure of Super-Efficiency for Ranking Efficient Units in Data Envelopment Analysis
Aeca Joual of Appled Sceces 8 (): 92-96, 20 ISSN 546-9239 200 Scece Publcatos A Ehaced Russell Measue of Supe-Effcecy fo Rakg Effcet Uts Data Evelopet Aalyss,2 Al Ashaf,,3 Az B Jaafa,,4 La Soo Lee ad,4
More informationChapter Linear Regression
Chpte 6.3 Le Regesso Afte edg ths chpte, ou should be ble to. defe egesso,. use sevel mmzg of esdul cte to choose the ght cteo, 3. deve the costts of le egesso model bsed o lest sques method cteo,. use
More informationBest Linear Unbiased Estimators of the Three Parameter Gamma Distribution using doubly Type-II censoring
Best Lea Ubased Estmatos of the hee Paamete Gamma Dstbuto usg doubly ype-ii cesog Amal S. Hassa Salwa Abd El-Aty Abstact Recetly ode statstcs ad the momets have assumed cosdeable teest may applcatos volvg
More informationA Remark on the Uniform Convergence of Some Sequences of Functions
Advaces Pure Mathematcs 05 5 57-533 Publshed Ole July 05 ScRes. http://www.scrp.org/joural/apm http://dx.do.org/0.436/apm.05.59048 A Remark o the Uform Covergece of Some Sequeces of Fuctos Guy Degla Isttut
More informationLecture 3 Probability review (cont d)
STATS 00: Itroducto to Statstcal Iferece Autum 06 Lecture 3 Probablty revew (cot d) 3. Jot dstrbutos If radom varables X,..., X k are depedet, the ther dstrbuto may be specfed by specfyg the dvdual dstrbuto
More informationLecture 11: Introduction to nonlinear optics I.
Lectue : Itoducto to olea optcs I. Pet Kužel Fomulato of the olea optcs: olea polazato Classfcato of the olea pheomea Popagato of wea optc sgals stog quas-statc felds (descpto usg eomalzed lea paametes)!
More information