A HYBRID ALGORITHM BASED ON LS-SVM AND BACTERIAL FORAGING APPROACH FOR BURNING-THROUGH-POINTS(BTP)PREDICTION IN SINTERING PROCESS

Transcription

1 0 h March 03. Vol. 49 o JAI & LLS. All rghs reserved. ISS: E-ISS: A HYBRID ALGORIHM BASED O LS-SVM AD BACERIAL FORAGIG APPROACH FOR BURIG-HROUGH-POIS(BPPREDICIO I SIERIG PROCESS SOG QIAG, WAG AI-MI Mechancal engneerng deparmen of Anyang nsue of echnology,songqang0@6.com School of Compuer Scence and echnology, Anyang normal Unversy, wam508@6.com ABSRAC In order o solve he analyss problem more effcenly and quckly, we presened a hybrd mehod based on LS-SVM and Baceral Foragng Opmzaon (BFO n hs sudy.he socal foragng behavor of Eschercha col bacera has been used o solve opmzaon problems. hs paper proposes a hybrd approach nvolvng Bacera Foragng Opmzaon Algorhm (BFO and LS-SVM algorhms for predcon problems. I s found ha Bacera Foragng Algorhm (BFO s capable of mprovng he speed of convergence as well as he precson n he desred resul. Smulaon resuls clearly llusrae ha he proposed approach s very effcen and could easly be exended for oher global opmzaon problems.i can conclude ha BFO s effecve and rapd for he cluser analyss problem. Keywords:Bacera Foragng Algorhm (BFO,LS-SVM,Opmzaon,BP.. IRODUCIO he snerng process s an mporan sep n sove smelng.i no only can agglomerae power maerals,bu also has prereamen funcon of burnng mehod for raw maerals,so ha he arge of hgh producvy, hgh qualy, low cos and longevy can be acheved n sove smelng.he sudy of snerng process has araced neres,no only from ron and seel ndusry, bu also from nonferrous ndusry. he Burnng-hrough-Pon (BP s an mporan parameer n snerng process. In he ron and seel enerprses, snered ore s he man raw maeral for blas furnace. In he snerng process, sably and qualy of sner s a decson facor o producon effcency of blas furnace. he snerng process s a preprocess for blas-furnace maerals. he qualy of sner s very mporan for smooh operaon and hgh producvy of he blas furnace snce mproves he permeably and reducbly of he burden maeral. Burnng-hrough-Pon for judgng he qualy of sner mporan ndcaor s a measure of good and bad snered mnerals mporan parameers[,]. he BP s a very mporan parameer n he snerng process. he sner qualy can be mproved, and he energy consumpon can be reduced, f one can accuraely predc he BP value. ha means he accurae predcon of BP can brng sgnfcan economc benefs and s of mporan praccal sgnfcance. radonal mehods usng feaures such as exhaus gas emperaure, negave pressure and exhaus gas composon o predc he BP[3]. Research on BP has araced wde aenon for many years. Several major mehods and echnques have been proposed and developed. hese mehods can roughly be caegorzed no wo ypes: One s classcal mehods, such as me seres models, regresson models, and ARX models [3]. he oher s arfcal nellgence mehods, such as exper sysem, neural nework, fuzzy logc.among hese mehods, he neural nework ( has receved 3

2 0 h March 03. Vol. 49 o JAI & LLS. All rghs reserved. ISS: E-ISS: much aenon n las few decades. he advanage of les n he fac ha hey do no requre any complex mahemacal formulaons or quanave correlaon beween npus and oupus. However, he faced wh wo shorcomngs, vz., converges oo slowly and he opmal weghs/bases are dffcul o se.alhough genec algorhm can solve above flaws n some measure, bu he resuls are no sasfacory. In hs sudy, a novel baceral foragng opmzaon (BFO was proposed. he baceral foragng opmzaon (BFO s a new global-search echnque and has acheved wdespread success n solvng praccal opmzaon problem n dfferen felds... SIERIG PROCESS AD HE AALYSIS OF BP.. Snerng process A snerng process s he nal one n an ron and seel makng plan (Augusn [995]. In hs process,ores, whch are mxed wh fne parcles of lmesone and coke breeze, are agglomeraed by combuson hea of coke breeze o become he man maeral of he blas furnace burden. he raw sner maerals are granulaed wh mosure n a mxer, and are charged on he palle of a snerng machne. Under he gnon hood ho gases and ar are drawn down hrough he bed by draugh fans. Provded he emperaure reached he op layer of he bed s hgh enough and condons are suable for combuson, he coke wll sar o burn and he snerng of he ore begns. Afer he sner maeral leaves he gnon hood, he combuson connues by drawng only cold ar hrough he bed. A relavely hn combuson zone hen progresses down hrough he bed. he srand speed has o be conrolled a a value whch enables he bed o be compleely snered by he me he ore reaches he end of he srand. Afer beng dscharged and cooled, he snered ores are crushed no a proper sze and fed o he blas[4,5,6,7]. Snerng s a mehod ha makes powdered maerals (such as fne ore or preparaon concenrae no block mass under condons nvolvng ncomplee fuson by heang o hgh emperaure. Is producon s sner whch s rregular and porous. he followng pars are usually ncluded n snerng process: accepance and sorage of ron-conanng raw maerals, fuel and flux; crushng and screenng of raw maerals, fuel and flux; bachng, mx-granulaon, feedng, gnon and snerng of mx maeral; crushng, screenng, coolng and sze-sablzaon of sner[8,9,0]. he flowchar s shown n Fg.. Fg. Snerng Process.. he Analyss of BP he BP s characerzed by he maxmum of he exhaus gas emperaure, whch s measured a sx locaons owards he end of he srand. I s prmarly conrolled by varable srand speed. he emperaure s measured a a ceran pon. he mehod employed here s no o explcly conrol he emperaure maxmum, bu o yeld he expeced BP by keepng he exhaus emperaure dsrbuon on a pre-defned curve.he BP can no be esed on-lne, and he judgmen based on he observaon daa by operaors s usually naccurae (Fan [999]. Alhough many successful aemps have been made o model he snerng process (Wang [00], s very dffcul o oban some mporan parameers n hese models, whch ndcae he physcal properes of he sner maeral. herefore, Sof-sensng mehod s adoped o solve hs problem. he model wll be bul up based on he BP relaed varables n real-me. he value of BP can be calculaed accordng o he emperaure curve of wase gas n wnd-boxes. I s generally beleved ha he emperaure of wase gas n wnd-boxes s hghes when he sner-mx bed s jus burned hrough. hermocouples are pu along fve wnd-boxes a dscharge end. Quadrac curve s fed accordng o hree pons ncludng he hghes emperaure. he general expresson of quadrac curve s: = ax + bx + c,(=,,.., ( where s emperaure of wase gas n wnd-boxes, x s number of wnd-boxes, A,B,C are coeffcens. Wh he hree know values, ((x,,(x,,(x3,3, and he hghes emperaure, usng he Eq., he followng expresson can be obaned: b X max = a ( = ax + bx + c max max max Burnng hrough Pon (BP s he poson when sner process s fnshed and s expressed by correspondng wnd-box poson when sner bed s burned hrough. Accurae conrol of Burnng hrough Pon (BP can no only make he 3

3 0 h March 03. Vol. 49 o JAI & LLS. All rghs reserved. ISS: E-ISS: snerng process sable bu also use he snerng area effecvely. he BP affecs he snerng oupu and qualy. I s generally beleved ha BP should be a he second wnd-box counng from backward. If BP s ahead of hs pon, effecve grae area of snerng machne wll no be fully ulzed. On he conrary, f BP lags behnd he pon, sner bed canno be burned hrough, whch wll resul n he ncrease of re-sner and decrease of sner rae. BP may be affeced by many facors. Row maeral parameers, operaon parameers and process condon parameers may have a grea nfluence on BP drec or ndrec. Moreover, BP canno be measured drec. Up ll now, here are no nsrumens ha can be used o measure BP onlne. In he paper, he onlne predcon of BP based on combned BFO-SVMs analyzed. Mahemacal mehod wll be used o solve he problem of onlne measuremen of BP [3]. In he praccal snerng process, BP s affeced by a number of facors, such as he gnon emperaure, he maeral hckness, mxure of waer, rolley speed, lme dosage, fuel consumpon, exhaus gas emperaure, he alkalny. 3. HE BACERIA FORAGIG OPIMIZAIO ALGORIHM he survval of speces n any naural evoluonary process depends upon her fness crera, whch reles upon her food searchng and mole behavor. he law of evoluon suppors hose speces who have beer food searchng ably and eher elmnaes or reshapes hose wh poor search ably. he genes of hose speces who are sronger ges propagaed n he evoluon chan snce hey posses ably o reproduce even beer speces n fuure generaons. So a clear undersandng and modelng of foragng behavor n any of he evoluonary speces, leads o s applcaon n any non-lnear sysem opmzaon algorhm. he foragng sraegy of Eschercha col bacera presen n human nesne can be explaned by four processes, namely chemoaxs, swarmng, reproducon, elmnaon dspersal[8,9]. Durng foragng of he real bacera, locomoon s acheved by a se of ensle flagella. Flagella help an E.col bacerum o umble or swm, whch are wo basc operaons performed by a bacerum a he me of foragng. When hey roae he flagella n he clockwse drecon, each flagellum pulls on he cell. ha resuls n he movng of flagella ndependenly and fnally he bacerum umbles wh lesser number of umblng whereas n a harmful place umbles frequenly o fnd a nuren graden. Movng he flagella n he counerclockwse drecon helps he bacerum o swm a a very fas rae. In he above-menoned algorhm he bacera undergoes chemoaxs, where hey lke o move owards a nuren graden and avod noxous envronmen. Generally he bacera move for a longer dsance n a frendly envronmen. Fgure depcs how clockwse and couner clockwse movemen of a bacerum ake place n a nuren soluon. Fg.: Swm And umble Of A Bacerum When hey ge food n suffcen, hey are ncreased n lengh and n presence of suable emperaure hey break n he mddle o from an exac replca of self. hs phenomenon nspred Passno o nroduce an even of reproducon n BFOA. Due o he occurrence of sudden envronmenal changes or aack, he chemoacc progress may be desroyed and a group of bacera may move o some oher places or some oher may be nroduced n he swarm of concern. hs consues he even of elmnaon-dspersal n he real baceral populaon, where all he bacera n a regon are klled or a group s dspersed no a new par of he envronmen[,,3,4] Chemoaxs In he orgnal BFO, a un walk wh random drecon represens a umble and a un. represens walk wh he same drecon n he las θ ( j, k, l sep ndcaes a run. Suppose he bacerum a jh chemoacc, kh reproducve, and lh elmnaon-dspersal sep. C( s he chemoacc sep sze durng each run or umble.hen n each compuaonal chemoacc sep, he movemen of he h bacerum can be represened as: θ ( j +, k, l = θ ( j, k, l + C( ( ( ( (3 33

4 0 h March 03. Vol. 49 o JAI & LLS. All rghs reserved. ISS: E-ISS: Where s he drecon vecor of he jh chemoacc sep. When he baceral movemen s run, s he same wh he las chemoacc sep; oherwse, s a random vecor whose elemens le n[-,]. Wh he acvy of run or umble aken a each sep of he chemoaxs process, a sep fness, denoed as J(,j,k,l wll be evaluaed[5,6,7,8]. Swarmng: I s always desred ha he bacerum ha has searched he opmum pah of food should ry o arac oher bacera so ha hey reach he desred place more rapdly. Swarmng makes he bacera congregae no groups and hence move as concenrc paerns of groups wh hgh baceral densy. Mahemacally, swarmng can be represened by J CC ( θ = = S p hrepellan exp ωrepellan( θ m θ m S J CC ( θ = = m (4 J CC ( ϑ, P( j, k, l S d exp ω arac arac = m Where s he cos funcon value o be added o he acual cos funcon o be mnmzed o presen a me varyng cos funcon. S s he oal number of bacera. p s he number of parameers o be opmzed ha are d h presen n each bacerum. arac repellan ω arac ω repellan are dfferen coffcens ha are o be chosen judcously Reproducon he healh saus of each bacerum s calculaed as he sum of he sep fness durng s lfe, ha s gradually or suddenly o elmnae he accdens of beng rapped no he local opma. In BFO, he dsperson even happens afer a ceran number of reproducon o be klled processes. hen some bacera are chosen, accordng o a prese probably Ped and moved o anoher poson whn he envronmen[9,0,]. he flow char of he erave algorhm s shown n he followng Fg.3. p ( θ m θ m +, where s he maxmum sep n a chemoaxs process. All bacera, are sored n reverse order accordng o healh saus. In he reproducon sep, only he frs half of populaon survves and a survvng bacerum spls no wo dencal ones, whch are hen placed n he same locaons. hus, he populaon of bacera keeps consan Elmnaon and Dspersal he chemoaxs provdes a bass for local search, and he reproducon process speeds up he convergence whch has been smulaed by he classcal BFO. Whle o a large exen, only chemoaxs and reproducon are no enough for global opma searchng. Snce bacera may ge suck around he nal posons or local opma, s possble for he dversy of BFO o change eher Fg 3:. Flowchar Of Baceral Foragng Algorhm 4. PREDICIO MODEL BASED O BAYESIA FRAMEWORK AD LS-SVM 4. LS-SVM Algorhm he LS-SVM,evolved from he SVM,changes he nequaly consran of a SVM no all equaly consran and forces he sum of squared error(sse loss funcon o become an experence loss funcon of he ranng se.hen he problem has become one of solvng lnear programmng problems.hs 34

5 0 h March 03. Vol. 49 o JAI & LLS. All rghs reserved. ISS: E-ISS: call be specfcally descrbed as follows: x x, y Gven a ranng se { } n R, y samples, =,wh n R, x R s npu vecor of he frs y R s he desred oupu value of he frs corresponds o samples, s he number of samples daa, he problem of lnear regresson s o fnd a lnear funcon y(x ha models he daa. In feaure space SVM models ake he form: (5 ϕ( : y( x = ω ϕ( x + b Where he nonlnear funcon mappng n nh R R maps he hgh-dmensonal space no he feaure space. Havng comprehensvely consdered he complexy of funcon and fng error,we can express he regresson problem as he consraned opmzaon problem accordng o he srucural rsk mnmzaon prncple: mn J ( w, e = w w + γ e = (6 subjec o he resrcve condons, y x = w x + b e,for =,, ( ϕ( + Where γ s margn parameer, and slack varable for x. e s he In order o solve he above opmzaon problems, by changng he consraned problem no an unconsraned problem and nroducng he Lagrange mulplers, we oban he objecve funcon: (,,, α = (, α{ ϕ( = L w b e J w e w x Where α (7 s Lagrange mulplers. Accordng o he opmal soluon of Karush-Kuhn-ucker(KK condons,ake he paral dervaves of (5wh respec o w,b and e,respecvely,and e hem be zero,we oban he opmal condons as follows: e Afer elmnaon of and w, he equaon can be expressed as a lnear funcon group: 0 b 0 = ϕ( x ϕ( x + D α y y = [ ] where y, L, y α = α [,, = [, L,],, α D = dag ], [ γ, L, γ ] Selec γ > 0, and guaranee marx 0 φ = ϕ( x ϕ( x + D b 0 = φ α y Fnally, he LS-SVM regresson model can be expressed as L = 0 w = α ϕ ( x w = L = 0 α = 0 b = L = 0 α = γ e e L = 0 w ϕ + b + e y = 0 α y( x = α exp{ x x / σ } + b = Where σ s a posve real consan. oe ha n he case of RBF kernel funcon, one has only wo addonal urnng parameers σ and γ, whch s less han sandard SVM. here exs wo parameers( γ and σ seleced by he user n advance. γ s vewed as a regularzaon parameer, whch conrols he rade-off beween complexy Of he machne and he number of non.separable pons.he kernel parameer denoes he wdh of Gauss funcon.he, 35

6 0 h March 03. Vol. 49 o JAI & LLS. All rghs reserved. ISS: E-ISS: dfference manly arbues o he dfferen effecs of parameers o LS-SVM regresson,namely γ only nfluences he ranng sep n he whole process.whleσ affecs boh he ranng sep and he predcng one. he role of he kernel funcon parameers σ affec he complexy of he dsrbuon of sample daa n hgh-dmensonal feaure space. he change n he parameers of he kernel funcon s acually mpled changng mappng funcon, hereby changng he number of dmensons of he sample space. he dmenson of he sample space deermnes lnear classfcaon surface VC dmenson can be consruced n hs space, and also deermnes he he lnear classfer surface o acheve he mnmum emprcal error. Example RBF kernel (kernel funcon parameer s he greaer, he wder he range of he oupu response of he samples, he smaller he opmal classfcaon surface srucure rsk, bu experence ncreased rsk; smaller he oupu response of he sample, he narrower he nerval, he opmal hyperplane experence rsk wll ge smaller, he srucural rsk wll ncrease, and lkely o cause over-fng, o reduce he performance of he SVM. choose he δ need o rade-off beween hese wo. he role of γ s o adjus he proporon of learnng machnes confdence range of experence and rsk daa subspace deermned o have he bes ably o promoe he learnng machne. he opmal γ are dfferen accordng o he dfferen daa subspace. Daa deermned subspace, small values of γ ndcaes ha he punshmen of he emprcal error s small, he degree of complexy of he learnng machne small experence greaer rsk value; vce versa. When γ exceeds a ceran value, he degree of complexy of SVM reached he maxmum value allowed by he sample space. Almos no experence n rsk and he ably o promoe change. he each daa subspace leas here s an opmal γ SVM promoe he ably o acheve he bes. o ye a unfed way o decde he bes values of he parameers of SVM, he choce s a ral and error mehod o ge sasfacory resuls hrough connuous expermens.o ye a unfed approach o deermne he he SVM parameers bes ake Value, he general selecon mehod s ral and error mehod, whch hrough connuous expermens o be sasfed wh he Resuls. Obvously, plays an mporan role o selec approprae parameers n he whole process of predcon usng LS-SVM.Movaed by pursung smaller error,we have dscussed he mehods of selecng parameers.here have exsed several effecve opmzaon approaches for LS-SVM, ncludng he mehod based on genec algorhm (GA and mullayer adapve parameers opmzaon( MAPO approach,ec.however, hey have some dsadvanages,such as cosng longer ranng me,easly desroyng he searched resuls for crossover operaor exsng n GA,and easly rappng no hose parameers wh local mnmzaon usng MAPO. hs LS-SVM regresson leads o solvng a se of lnear equaons, whch s for many applcaon n dfferen areas. Especally, he soluon by solvng a lnear sysem s nsead of quadrac programmng. I can decrease he model algorhm complexy and shoren compung me grealy. he LS-SVM algorhm sofware package s run n MALAB 7.0. sofware. 5.BFO OPIMIZE SVM PARAMEERS 5. predcon sep BFO-SVM predcon seps are as follows: a Analyss of he expermenal daa, seleced reaed predcon ndcaors have mporan nfluence on he relaonshp beween parameers, prereamen of he sample se s formed and he sample se; b choose he of SVM ypes and nuclear funcon, 36

7 0 h March 03. Vol. 49 o JAI & LLS. All rghs reserved. ISS: E-ISS: o deermne he operang parameers requred by he model o deermne he operang parameers of he BFO or PSO algorhm, he SVM regresson model; c call he opmal parameers of he PSO-SVM he algorhm search SVM regresson model; d he opmal parameers of re-ranng he SVM regresson machne regresson model was esablshed for solvng he regresson equaon; e promoe he profcency es wh he es sample se. 5. Expermens and Dscussons In order o evaluae he accuracy of he model, we use RMSE o evaluae,mean-square-error(rmseand concdence probably P are aken o evaluae he performance of he LS-SVM regresson.he frs creron(rmses calculaed RMSE = where = ( x x (8 x x and sand for he predced and desred values respecvely,and denoes he oal number for predcon. he second one,whch shows he concdence degree beween he predced and desred values,s evaluaed as p = where c (9 c represens he number of pons whose errors are less han ceran value. Fg4:Ieraons Resul o compare algorhms farly, we esed he opmzaon ably of BFO algorhm. Orgnal BFO, PSO and Genec Algorhm (GA were employed for comparson.i s clear from fgure. 4 ha BFO obaned he bes values.bfo s bes on he res one. PSO performed wors[0,,]. In order o search he global opmum more effcenly,planned furher work s o mprove he muaon operaon BFO and nvesgae how o choose he evoluons seps k and k o deal wh dfferen ssues.bfo can be appled o no only LS-SVM bu also oher modfed versons of SVM. Is developmen wll smulae he applcaon of SVM o more general felds[3,4,5]. able. Parameer Seleced For BFO Parameer Value umber of bacerum (s 0 umber of chemoac seps (nc 0 Swmmng lengh (ns 3 umber of reproducon seps (nre 4 umber of elmnaon and dspersal 5 evens (ned Deph of aracan (darac 0. Wdh of aracan ( darac 0.5 Hegh of repellen (hrepellan 0. Wdh of repellen ( repellan 0 Probably of elmnaon-dspersal 0.0 evens (ped 37

8 0 h March 03. Vol. 49 o JAI & LLS. All rghs reserved. ISS: E-ISS: Predcon wh BFO model and LS-SVM model λ Le be BFO predcon value, λ be he predcon value by LS-SVM, whle λc predcon value by opmal combned model. he η predcon errors are, η and ηc be respecvely. he correspondng weghed coeffcens are ω, ω ω and c,and ω + ω = η = ϖ η + ϖ η c (0 Var ω Var = ω ω ( ( ηc = Var( ωη + ωη = ( η + ωvar( η + ωω Cov( η, η Var( η + ( ω Var( η + ( ω Cov( η, η Var ω ( η c ω = Var = Var ( η + Var( η Cov( η, η Var( η Cov( η, η ( η + Var( η Cov( η, η Cov( η, η 0 because = Var η = γ, Var η = γ ( ( Le hen he weghed coeffcens of combned predcon are ω γ =, γ + γ gamma=300,σ = γ ω = γ + γ (, ω As o,n order o deermne he funconal 4 predcon values acual values Y mnmum value,le Var ω ( η c = 0 and me Fg5: he Predcon Dagram Of he BP Based On BFO And LS-SVM able he Comparson Of Forecasng Model Accuracy Of he BP Mehods Mean relave Mean absolue Mean square error error error LS-SVM BFO BFO-LSSV M Fg.4 shows he comparson of forecas load wh he acual. he resuls are found o be que accurae. From able can be seen ha hree models presen que sasfacory forecasng resuls.by comparng he Mean relave error, Mean absolue error and Mean square error of BFO-SVM s smaller han ha of BFO and LS-SVM. Moreover, BFO-SVM has hgher precse predcon han BFO and LS-SVM. 6. COCLUSIO hs paper presened an approach o 38

9 0 h March 03. Vol. 49 o JAI & LLS. All rghs reserved. ISS: E-ISS: opmze he parameers of LS-SVM regresson. he effecveness of he proposed was verfed and was also compared agans resuls produced by BFO. From he underaken expermens, s shown ha he approach of usng BFO n opmzng parameers of he Leas Squares Suppor Vecor Machnes (LS-SVM s benefcal. hs can be seen n he obaned resuls of Mean Squared Error and predcon accuracy. he hybrdzaon of BFO-LSSVM can be consdered as a promsng echnque ha s suable for predcng nonvolale daa.here are ways o mprove our proposed algorhms. Furher research effors should focus on he unng of he user-defned parameers for ABFO algorhms based on exensve evaluaon on many benchmark funcons and real-world problems. REFERECES [].FA Xao-hu,WAG Ha-dong. Mahemacal model and Arfcal Inellgence of snerng process[m]. Cenral Souh Unversy Press, ,Vol. 47. o [].WAG Ywen, GUI Wehua, WAG Yaln. Inegraed model for predcng burnng hrough pon of snerng process based on opmal combnaon algorhm[j]. he Chnese Journal of onferrous Meal, 00, (: [3].ZHAG Xao-long. forecasng mehod and applcaon of BP based on neural nework[d]. Cenral Souh Unversy,006. [4].CHEG Wushan. A buldng of he genec-neural nework for sner s burnng hrough pon[j].snerng and Pellezn, 004, 9(5:8. [5]XIAG Q-lang,WU Mn,XIAG Je el. predcon mehod of BP Based on mulple fuzzy lnear regresson [J]. Conrol Engneerng,Vol.4 o.6: [6].WU Xao-feng,FEI Mn-ru.Fuzzy conrol appled o burnng hrough pon based on suppor vecor machnes predcon model[j]. Journal of Zhejang Unversy ( Engneerng Scence,Vol.4 o.0,007(0:7-75. [7].LIU Yu-chang,GUI We-hua,ZHOU Je-mn.Sudy on nellgen conrol of BP based on sof-sensor echnology[j]. Snerng and Pellezng,003,7(:7-30. [8].ZHAG Xue-gong. On he sascal learnng heory and suppor vecor machnes [J]. Auomaon Journal, 000, 6 ( :-6. [9].LI Guo-zheng, WAG Meng,ZEG Hua-jun. Suppor vecor machne Inroducon [M]. Bejng: Elecroncs Indusry Publsh press, 004. [0]. M.H. L, J. Wang.he Research for Sof Measurng echnque of Snerng Burnng hrough Pon.Indusral Elecroncs and Applcaons, 006 S IEEE Conference on, 4-6 May 006:-4. [] YUA H C, XIOG F L, HUAI X Y. A mehod for esmang he number of hdden neurons n feed-forward neural neworks based on nformaon enropy [J]. Compuers and Elecroncs n Agrculure, 003, 40(-3: [] OYSAL Y. A comparave sudy of adapve load frequency conroller desgns n a power sysem wh dynamcneural nework models [J]. Energy Converson and Managemen, 005, 46(5-6: [3] MIRA A, KUDU D. Genec algorhms based robus frequency esmaon of snusodal sgnals wh saonary errors [J]. Engneerng Applcaons of Arfcal Inellgence, 00, 3(3:

10 0 h March 03. Vol. 49 o JAI & LLS. All rghs reserved. ISS: E-ISS: [4] PEDHARKAR P C. Genec algorhm based neural nework approaches for predcng churn n cellular wreless nework servces [J]. Exper Sysems wh Applcaons, 009, 36(3, Par : [5] MI X, ZOU Y, WEI W, e al. esng he generalzaon of arfcal neural neworks wh cross-valdaon and ndependen-valdaon n modellng rce llerng dynamcs [J]. Ecologcal Modellng, 005, 8(4: [6] C. Fernandes, V. Ramos, and A. C. Rosa, Varng he populaon sze of arfcal foragng swarms on me varyng landscapes, Lecure oes n Compuer Scence, Vol. 3696, pp. 3 36, 005. [7] H. C. Berg, D. A. Brown, Chemoaxs n Eschercha col analyzed by hree-dmensonal rackng, aure, Vol. 39, pp , 97. [8] S. Mshra, A hybrd Leas Square - Fuzzy Baceral Foragng Sraegy for harmonc esmaon, IEEE ransacon on Evoluonary Compuaon, Vol. 9, o., pp. 6 73, 005. [9] R. Walker, che Selecon and he evoluon of complex behavor n a changng envronmen a smulaon, Arfcal Lfe, Vol. 5, pp. 7 89, 999 [0] orega, G., Resrepo, J., Guzman, V., Gmenez, M., Aller and Smón Bolívar On lne parameer esmaon of elecrc sysems usng he baceral foragng algorhm. In Proceedngs of he 3h European Conference on Power Elecroncs and Applcaons, pp [] ang, W.J., Wu, Q.H., and Saunders, J.R Baceral foragng algorhm for dynamc envronmens. IEEE Cong. Evol. Comp., July 6-, pp , Canada. [] Acharya, D.P., Panda, G., Mshra, S., and Lakshm, Y.V.S Bacera Foragng Based Independen Componen Analyss. In Proceedngs on Inernaonal Conference on Compuaonal Inellgence and Mulmeda Applcaons, Dec 3-5. pp Svakas, aml adu. [3] Sakhve,l V.P., and Subramanan, S. 00. Deermnaon of Inducon Moor Double Cage Model Parameers Based on Baceral Foragng Algorhm. In. Revew Elecrcal Eng., vol. 5, no. 4, pp [4]PARA KALPAA, Dr. A.LAKSHMI DEVI.PLACEME AD SIZIG OF DISRIBUED GEERAORS I DISRIBUED EWORK BASED O LRIC AD LOAD GROWH COROL.Journal of heorecal and Appled Informaon echnology,pp Vol. 47. o [5]Mohamed EAOUIL, Mohamed LAZAAR, Youssef GHAOU.ARCHIECURE OPIMIZAIO MODEL FOR HE MULILAYER PERCEPRO AD CLUSERIG.Journal of heorecal and Appled Informaon echnology,pp 40