Research on Control Strategy of Intelligent Vehicle Autonomous Learning Based on Neural Network Algorithm

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* author

: s d r o w y e

K Comfor,t Neural Network, GeneitcAlgortihm,AutonomousLearning.

.t c a r t s b

A W etiht h development fo et h automoblieindusrty na d et h intelilgentautomoblie,peoples' d

n a m e

d rf eo t h comfo tr fo et rh c si a improved, os et h comfo tr fo et h smatr rc a needs ot eb reasonably . d e ll o rt n o

c nI sit h pape,r eth intelilgentconrtolmethodbased no neuralnetworkalgortihm si u osedt l

o rt n o

c et h in lfuence parameters fo damping force fo shock absorbe.r Memory archtiecture si d e h si l b a ts

e ot ts orememory,geneitcalgortihm si uitilzed ot opitmize da n judgewhichendows et rh c a h

ti

w et h ablitiy fo autonomouslearning. eT h theoreitcal tsudy shows tt eha t h judgmentablitiy da n y c a r u c c

a fo comfo tradju tsment nc ea b improvedeffecitvely yb et h opitmizaiton fo geneitcalgortihm d e n i b m o

c hw ti neuralnetwork.

n o it c u d o r t n I h ti

W et h development fo et h global automoblie indusrty, et h demand rf eo t h automoblie si , g n is a e r c n

i da en t h requriement rf o operaiton tsablitiy da n comfo tr fo et h vehiclesuspen ison si oa sl g

n is i a

r . eT h requ riemenst rf eo t h operaiton tsablitiy da n comfo tr fo et h vehiclesuspen ison ea or a sl e l b a c il p p

a ot et h smatr .rc a IEEE conrtolsy tsems sa sociaiton beileves tt ha intelilgent conrtolm u ts e

v a

h a ismulaitoncapactiy fo humanlearning da n adapitve .][ N1 euralnetwork da n geneitcalgortihm e

r

a widely u nsedi et h intelilgentconrtolsy tsem. Ameircan neuralnetwork scienit tsHechtNieslon s

e v i

g a de ifniiton ot neuralnetwork sa fo llowing: A neuralnetwork si a sy tsemcomposed fo many e

l p m

is proces isngelemen stoperaitng ni parallelwhosefunciton si determined yb networksrtucture, n o it c e n n o

c srtength ,s da en t h proces isng pefrormed ta compuitng eel menst ro node .s eT h neural k r o w t e

n sh a uniqueabiilites ni et h intelilgentconrtol fo et h parameter ,ssrtucture da n envrionment fo fl

e

s -adaptaiton, fls -e organizaiton, fls -e learning. Geneitc algortihm si a k find o nn -o determin siitc l a r u t a

n tsochasitcopitmizaiton too.l sA a k findo globalopitmizaitonmethod w htih h ig ef ifciency, l e ll a r a

p da n randomsearchabiilite ,s ti si a powefrul lt ooo t solvesomeproblem .s tI nc ea b mixed hw ti r

e h t

o technologies rf o intelilgent conrtol fo et h parameter ,s srtucture ro t eh envrionment fo et h l

a m it p

o conrto.l eT h combinaiton fo neural network da n geneitc algortihm nc ea b a method fo c if it n e i c

s searchoperaiton.According ot et h advantages fo neuralnetwork, et h globalscholarshave e

d a

m deepresearches no et h neuralnetwork modeilngtechnology fo shockabsorbe.rSincebaugh te .l

a ][ 2 tsudy et h neural network model fo et h nonilnear charactersiitcs fo et h mlitiary vehicle c il u a r d y

h shock absorber yb u isng nn -o parametirc modeilng. Chang da n Roschke ][ e3 u s neural k r o w t e

n ot ismulate et h response fo damper ,s et nh n -o paramet ircmodeilng si oa sl used.Dong np ta e .l

a ][ 4 bulid et h modeilng fo vibraitondamperbased no neuralnetwork, da en t h b uitlmodel si rtained d

n

a te tsed. eT h resulstshow tt eha t h vibraitondamper sh a a good ismulaiton da n predicitonablitiy fo g n i p m a

d force-veloctiy.W etiht h increase fo et h number fo learning, et h accuracy fo et h model nc a e

b improved. E ]ski [ 5 de isgns a robu tscon rtolsy tsem based no neuralnetwork rf eo t h conrtol fo e l c i h e

v suspen ison, tse ablsih 7 degree fo freedommodel fo automoblie da n compared et h suspen ison h

ti

w et Dh P I conrtolle.r eT h ismulaiton resulst show t ehat t h neural network algortihm sh a good e c n a m r o fr e

p fo ita - dn roa di tsurbance.G D tuo, e .la ][ 6 propose na indriectadapitveconrtolmethod r

o

(2)

e v it p a d

a conrtol fo et h neuralnetwork nc a enhance et h conrtoleffec.t N lA -Holou te .la ][ 7 propose a ts

u b o

r intelilgentnonilnearconrtoller rf o acitvesuspen ison sy tsemsbased no a comprehen isve da n c

it si l a e

r non ilnear mode,l da a n silding mode neural network inference fuzzy logic conrtoller si .

d e n g is e

d eT h ismulaitonresulstprove tt ha conrtollerexceedsexsiitngconvenitonalcon rtollers ni et h st

c e p s

a fo bodyacceleraiton,suspen isondelfeciton, da en t ri delfeciton.H W tu,i e .la ][ 8 presenst na e

v it p a d

a conrtol srtategy based no neural network, which aims ta et h it me vairant da n nonilnear x

e l p m o

c sy tsem sa semiacitve ria suspen ison. eT h resulst fo ismulaitonanalyssi da n bench tst e show t

a h

t et h vibraitonampltiude fo et h vehiclebody nc ea b reduced yb about30%.Chuanyin, T te .la 9[ ] e

s

u a conrtolsrtategybased no et h combinaiton fo geneitcalgortihm da n neuralnetwork ot de isgn a e

ll o rt n o

c r rf o vibraiton dampe .r eT h problem si t hat a tfer et h acceleraiton fo et h body da en t h n

o is n e p s u

s fo et h dynamic rtavel have been improved, et h dynamic load fo et h wheel sh a .

d e t a r o ir e t e d

n

I sit h pape,r na aritifcialintelilgencelearningmethodbased no t eh combinaiton fo neuralnetwork d

n

a geneitcalgortihm si proposed. eT h in lfuenceparameters fo dampingforce fo shockabsorber ea r d

e g n a h

c no et h bassi fo previous attempt ;s memory ad si at b yuitl b u isng neural network ,s a wf e s

r e t e m a r a

p memo ires ea rr ts do e ni et h process fo automob lietes.t eT h neuralnetwork da n geneitc m

h ti r o g l

a ea r combined which provides a method rf eo t h in tsantaneous judgment fo et h comfo tr .t

n e m ts u j d

a Whether heac in tsantaneousjudgment si reasonable ro tn si o judged. fI ti si reasonable, ti n

a

c eb depostied ni et h memory ad at which si rteated sa a wn e memory ot provide a bassi rf eo t h b

u

s sequentin tsantaneousjudgmen.t

s r e t e m a r a

P A ffecitng et Ch omfort fo et C rh a

e h

T comfo tr means tt a ha comfo trable irding envrionment da n convenientoperaiton condiitons h

c i h

w should eb provide yb et .rh c a Comfo trshouldinclude eir d comfor,t ria condiitoningproperites ,

e r u t a r e p m e

t( humidtiy,etc. ,)inteirornosie, irdingenvrionment(acitvespace, et h width fo d doora n ,l

e n n a h

c internalfaciilite ,s e ).t dc a en t h operaitngpefrormance fo et h d irve.r e

li b o m o t u

A suspen ison si na elasitc conneciton between et h vehicle body da en t h wheel .s eT h n

o it c n u

f fo automoblie suspen ison si ot e dase a n resrtain et h vibraiton da n attack caused yb et h n

e v e n

u droa su frac ewhich nc a ensure tt eha t h members da pn shi p edgoods ea nr i goodcondiiton. yB g

n it s u j d

a et h damping force fo et h damper no et h suspen ison, et h rfequency da n vibraiton n

o it a r e l e c c

a fo rc na c ea b adju tsed, nthe et h comfo tr nc ea b changed.Factorsselected yb sit h paper :

e d u l c n

i speed, droa roughnes ,s et h anglebetween droa su frace da n ho irzontald rieciton,sprungmas,s .

c t e

r e d n

U di fferent road condiiton ,s speed, road roughnes ,s et h angle between road su frace da n l

a t n o z ir o

h drieciton da n sprungm eassa br a el ot in lfuence et h adju tsment fo dampingforcemade yb k

c o h

s absorber os tt eha t h comfo tr nc ea b in lfuenced. Comfo tr si represented yb et h frequency fo y

d o

b vibraiton (Thefrequency fo et h bodys'upper da n lowermovemenstwhen et h body si u osed t k

l a

w osh u eldb de isgned ot eb et h naturalfrequency fo vehiclebodyvibraiton, sti frequency si about 0

6 ot 08 itmes rp e minute(1Hz ot 1.6Hz) .) nI sit h pape,rvehiclebodyvibraitonfrequency fo 1.3Hz si t

e

s sa opitmalcomfor.t

e h

T Establsihment fo a Memory Architecture

l a i c if it r

A neural network sh a a ce train adapitveab litiy da n autonomouslearning ablitiy. Neural k

r o w t e

n algortihm ismulates et h neurons which ts eore t h memory yb di tsirbuitng et h memory n

o it a m r o f n

i no et h network layer .s Informaiton proces isng si mainly based no et h cooperaitve g

n is s e c o r

p between di ts irbuted layer .s T he tsudy fo arit ifcial neural network mainly includes d

e si v r e p u

s learning da n unsupervsied learning: eT h formerclas isifes da n modesl ni a given range; d

n

a et h latter sh a a ce train ale rningablitiy, ti nc a dsicoverpotenitalrules fo ht e externalsy tsem da n n

r a e l .ti

e h

T inputlayer fo λ1 si assumed tt ti ha contains et h number fo 1σ fo neurons(parameters) da en t h

t u p t u

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e h

T N- ht hiddenlayer si expressed sa ρ(N) da hn eac neuron fo sit h hiddenlayer si expressed sa ρ(Na,) a�(1,2…n(N) .) eT h di ts irbuiton fo neurons fo neural network layers si shown ni F .ig. 1 eT h

d e r u s a e

m in lfuenceparameters fo dampingforce fo shockabsorber ea r impo tred oi nt networklayer ,s ti means tt eha t h in lfuenceparameters fo dampingforce fo shockabsorber ea r clas isifedaccording ot

e h

t properites fo parametersfrislty, et h parameterswhichhave et h sameproperites ea r di tsirbuted oi nt e

h

t samenetwork laye,rtheseparameterswhich no ew t h sameproperites nc ea b tsored as neuron .s e

h

T memoryarchtiecture nc ea b b nuitli et yh w a tsatedabove.

F gi u 1. ere T h di tsirbuiton fo neurons fo neuralnetworklayers.

h c a

E neuron sh a isngleoutputattirbute ni et h drieciton fo et h inputinformaiton. eO n neuron sh a a n

o it c e n n o

c weight ot a neuron fo n extnetworklaye.r rF a o neuron, sti input nc ea b expressed :sa

1 0 m

p i p

p ω µ

− =

∑

. (1)

st

I output nc ea b expressed :sa

1

0 )

(

f m_p _p ip

y=

∑

₌− ω µ −θ _. ₍₂₎

si et h connecitonweightbetween et p- hh t neuro fno et i- hh t networklayer da en t h targetneuron, si et h input fo et p- hh t neuron fo et i- hh t network layer fo et h target neuron, si et h internal

d l o h s e r h

t da f n functi si eon t h acitvaitonfunciton. e

h

T output nc ea b expressed sa following:

1 0 1 0

,1 ,1

m p i p p

fi y

θ µ ω

− = − =

 ₌ _≥

 

− = 

∑

＜ . ( 3)

e h

T proces isngprocedure si shown ni .F :ig 2

g

i

F u 2. ere T h proces isngprocedure fo input fo et h targetneuron. g

n i d r o c c

A ot et h input da n output modesl tsated above, et h method fo memory ts nore c ea b d

e n i a t b

o which oa sl means t ehat t h conneciton stiuaiton among neurons nc ea b expressed w tih .s

l e d o

m Assuming tt eha o fn o et h parameters fo et h uppernetwork layer ea r connected w tih some k

r o w t e

(4)

e h

t isgnasl ea r expressed sa - .1 Dfiferent types fo in lfuenceparameters fo dampingforce fo shock r

e b r o s b

a nc ea b rts do e ni differentnetworklayer .s

e h

T Appilca iton fo Geneitc Algortihm

e h

T geneitcalgortihm nc ea b appiled ni sit h pape.r eT h in lfuenceparameters fo dampingforce fo k

c o h

s absorber ea r produced when et h vehicle si running, which ea r di tsirbuted ni differentneural k

r o w t e

n layer .s sA rf o eachneuralnetworklaye,reach etyp fo parameters co rresponds ot a network ,r

e y a

l da n different ap rameters fo eo en typ co rrespond ot differentneuron .s eT h in lfuenceparameters f

o dampingforce fo shockabsorberneed ot eb compared hw eti t h o irginalparameters fo heac neural k

r o w t e

n layer eo yn b o ene.T h m ain ts fepso implementaiton ea sr a following: )

1

( eT h networklayer fo et h in lfuenceparameters fo dampingforce fo shockabsorbershould eb ,

d e n i m r e t e

d da n theseparameters ea r hypothe iszed sa χ( 1, χ2. χ.. n.) When determining et h network

r e y a

l rf o parameter ,s et h inputlayer da n outputlayershould eb confrimedfrislty.Assuming tt eha t h s

r e t e m a r a

p λ1 a λnd 2 co rrespond ot et h input layer da n output laye,r respecitvely. eT h in lfuence

s r e t e m a r a

p of dampingforce fo shockabsorber fo et h inputlayer nc ea b determineda tfer et χh 1, χ2. ..

χnbeingmapped ot λ1 o yneb o fI ne. there si a parameterbelongs ot eo fn o χ1, χ2. χ.. n which si et h same

e p y

t fo parameter sa λ1 ,then et h mapping process w eillb tsopped. Otherwsie, et h process w eill b

. d e u n it n o

c eT h outputlayer si processed yb et h sameway. )

2

( Atfer et h input layer da en t h output layer being determined, et h parameters χa, χb a re

d e z is e h t o p y

h sa et h parameterswhichhave et h same etyp hw λti 1,λ2,respecitvely (Hypothessi: a<b),

o

s et h network layer fo et tsh r e parameters χ( 1, χ2. χ.. a-1, χa+1. χ.. b-1, χb+1...χn) need ot eb confrimed.

g n is

U et h mappingmethodabove ni pts ,)e ( e1 t h networklayerdi tsirbuiton fo et h parameter fo χ nc a e

b ensured. fI χi sin otbelong ot ya n o irginalnetworklaye,r et h parameter fo χi w eillb processed sa a

w e

n hidden layer which w eill b recorded ni et h neural network memory atfer et h compleiton fo g

n i w o ll o

f tsep .s )

3

( eT h geneitcalgortihm si mainlyu nsedi et h op itmal selecitonatfermapping da n conrtas.t μ( i1,

μi2. μ.. im)a rehypothe iszed sa et h actualparameters fo et h networklayer fo χi, da ti n represenst et h

e t e r c n o

c numbe.rA tfer et h networklayer fo χi beingdetermined, χi needs ot eb mapped hw μti ( i1, μi2. ..

μim) eo yn b o ne.T hsiprocess nc ea b expressed sa et h seleciton fo geneitcalgortihmwhichmeans tt ha

χi sipariedw μtih ( i1, μi2. μ.. im) respecitvely. eT h conrta tsresutlatfer et h paiirng nc ea b regarded sa

r e v o s s o r

c operaiton. eT h ismlia irites fo χi dan μi1, μi2. μ.. im ea r needed ot eb compared. fI χi� f(1(μip,)

f2(μip) χ,) i da μn ip ea r regarded sa ismlia.r μip�(μi1, μi2. μ.. im) da p si en t h grade fo a paramete .r f1( μ)

d n

a f2( eμ)a er t h conrta tsfuncitonswhich ea dr u nse i et h con rta ts fo χi da μn i p. fI χi sin otbelong ot ya n

l a n i g ir

o network al y sie,rt ph ts lle w ei b skipped. )

4

( According ot et ph ts ,)e ( 3 atfer et h in lfuenceparameters fo dampingforce fo shockabsorber ,( χ1 ..

. 2

χ )χ n being co rresponded ot et h network layers ythe belong, these parameters ea r mapped w tih g

n it si x

e parameterswhich ea nr i et h o irginalnetwork layers eo yn b eo , n respecitvely. fI there si ta ts

a e

l eo n parameter fo ( χ1, χ )2... χ n belongs ot na o irginal network laye,r tb ti u d toesn o m eeett h :

n o it i d n o

c iχ�f(1(μip ,) f2(μip )), et wh n e aciton fo ( χ1, χ )2... χ n cannot eb judged yb exrtacitng r

a li m

is informaiton mfro et h o irginalmemory.Here, there si a er ul followed yb sit h paper: fi iχ� ,)

p i μ ( 3

f( f4(μip),) f(1(μip ,)f2(μip ))�f(3(μip ,)f4(μip )) da pn μ si ei t sh imliarparamete.r fI χi (f3(μip ,) )

p i μ ( 4

f , et h serach shoul ed b conitnued. fI iχ�(f5(μip ,)f6(μip ,)where (f3(μip ,)f4(μip))�(f5(μip ,) )

p i μ ( 6

f , pμ si ei t h ismliarparameter da p si n uncetrainquanttiy.T hesearchprocess llw ei b conitnued u lin it χ�( xf -1(μip ,)fx(μip) si found,atfertha,t sit ph ts ne c ea b tsopped.

) 5

( fI ya χn i in(χ1, χ2. χ.. n) lla satsify et h condiiton tt χha i� f(x-1(μip ,) fx(μip,) where i represenst et h

k r o w t e

n layer da p n represenst et h grade fo a paramete,r os μip da χn i a reregarded sa ismliar in et h

e m a

s network laye.r Simlia lry,there si na o irginalparameterwhich nc ea b found ni each network r

e y a

l rf χo i. According ot et h o irginalmemoryroute fo μ( 1p1, μ2p2. μ.. kpn,) et h aciton fo et h in lfuence

s r e t e m a r a

(5)

n o it c

a si et h same sa et h aciton fo μ( 1p1, μ2p2. μ.. kpn.) Where ,p 1 p2…pn ea er t h ismliarparameters fo

h c a

e networklayer da k si n represenitng et h totalnumber fo networklayer .s )

6

( A tfer et th aci onbeingcompleted, fi et h effectwhich si produced yb et h aciton si ni eil hn w eti t h ,

d r a d n a

ts et h memory fo et h in lfuenceparameters fo dampingforce fo shockabsorber fo χ( 1, χ2. χ.. n)

n a

c eb rteated sa effecitvememorywhich si ea obl t eb recorded ni et h neuralnetwork.T h siaciton fo χ

( 1, χ2. χ.. n) si et h same sa et h aciton fo μ( 1p1, μ2p2. μ.. kpn.) fI et h effectwhich si produced yb et h aciton

si tn no i eil hn w eti t h tsandard, sit h memory llw ei b recorded ot anotherneuralnetwork. If et h vehicle y

d o

b vibraiton frequencyfalsl ni 1.0Hz-1.6Hz, et h aciton si reasonable;otherwsie, et h aciton si tn o .

e l b a n o s a e r

e h

T mappingprocess si shown ni Fig.3.

g

i

F u 3. ere T h schemaitcdiagram fo et h mappingprocess.

h t w o r

G ni Memory D aa t

, d e e p

S droa roughnes ,s et h anglebetween droa su frace da n ho irzontaldrieciton da n sprungm ass e

r

a expressed sa ,v ,q r da .n m v( 1-v2 ,) q( 1-q2,) r(1-r2,) m( 1- )m n2 c a guarantee et h indicatorsexcept

tr o f m o

c ea r wtihin et h normalrange.Adjusitng et h parameterswhichmakes v�(v1, v2 ,) q�(q1, q2,)

r� r(1, r2,) m�(m1, m2,) nthe a group fo dampingforceadju tsment ad nat c ea b obtained. sA si tsated

, e v o b

a et h micro-computer ni et rh c a assesses et h effect fo execu iton fo acitonatfer et h acitonbeing .

e n o

d eT h vibraiton frequency fo et h vehicle body si obtained yb et h senso ,r da en t h 1.3Hz si d

e r a p m o

c hw eti t h frequencygained. fI ti si between z1 dH a n 1.6Hz, ti meest ro exceedsexpectaiton ,s e

h

t memory nc ea b recorded ni et h neural network sa wn e memory. tI means t ehat t h aciton is e

l b a n o s a e

r da ti nn c ea b saved sa wn e memory."Datagrowth"means et h growth fo memorydata, da n si

h

t memory llw ei b et h reference of r et h judgment fo lateraciton. n

I et h appilcaiton fo et h geneitcalgortihm, fi there ea er o rn o m oreactualparameters of χ( 1, χ2. χ.. n)

h c i h

w ea tr n o co rresponding ot ya n exsiitngnetworklaye,r et wh n e networklayer si necessary ot eb .t

li u

b Before a wn e memory si buli,t et h acitonshould eb executedeventhough eo rn o m oreactual s

r e t e m a r a

p ea tr n o co rresponding ot ya en xsiitngnetworklaye.r tA sit h itme, et h aciton nc ea b ts :se a e

h

t parameter fo χ which si tn o co rresponding ot ya n exsiitngnetwork layer si removed, da n other s

r e t e m a r a

p w eill b co rresponding ot et h o irginal parameters fo network layers according ot et h d

o h t e

m tsatedabove. eT h aciton fo χ( 1, χ2. χ.. n) si con is tsent hw eti t h aciton fo o irginalparameters fo

k r o w t e

n layer .sThere si a problem tt eha t h parameter fo χ which si tn o co rresponding ot ya n exsiitng k

r o w t e

n layer si tn no i et h o irginalmemory ad , oat s et h raitonaltiy fo et h aciton need ot eb judged r

e tf

a et h aciton sh a beenexecuted. fI et h aciton si ni eil hn w eti t h tsandardatfer ti sh a beenexecuted, ti nc ea b collected ni et h memorydata. fI et h aciton si tn no i eil hn w eti t h tsandardatfer ti sh a been

, d e t u c e x

(6)

e r e h

T si a conitnuousproblem fo aciton: A aciton nc ea b produced ni a momen,t os ti si ts te t ha e

r e h

t si yo enl o n in tsantaneousacitonproduced ni et h it fmeo ,λ da a n completeadju tsmentprocess d

e e

n ot eb combined hw ti manyin tsantaneousaciton .sAtfer et h wholeadju tsmentprocess sh a been e

n o

d reasonably, lla in tsantaneousacitons ea r analyzed da n processed, da en t h in tsantaneousacitons h

c i h

w ea r irraitonal llw ei b eilminated da n otherin tsantaneousacitons liw l eb tsored ni et h memory .

a t a d

y r a m m u S

n

I sit h pape,r et h neural network algortihm da n geneitcalgortihm ea r inrtoduced frislty. Then et h s

e h c r a e s e

r fo scholars no et h intelilgentsuspen ison ea r elaborated. nI order ot improve et h comfo tr fo e

h

t smatr c a wa ,r n e intelilgent conrtol scheme fo suspen ison si proposed. Autonomic learning n

o it c n u

f fo neuralnetwork algortihm si used.A tfer et h geneitcalgortihm si uitilzed ot opitmize, et h n

o it c

a fo vehicle nc ea b judged da nn the et h memory ad nat c ea b increased. eT h analyssishows tt ha d

o o

g comfo tr nc ea b provided yb u isngneuralnetworkalgortihm ot con rtol et h vibraiton fo et h shock r

e b r o s b

a a tferrepeated tsudy.

t n e m e g d e l w o n k c A

s k n a h

T ea er d ou t G iu ek eS n dh a n Xuan iL re f o as is tsance da n valuabledsicus ison.

s e c n e r e f e R

] 1

[ F .u, K (1971 .) Learning conrtol sy tsems da n intelilgent con rtol sy tsem :s nA interseciton fo l

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2

[ Sincebaugh, ,.P Green, ,.W & Rinku ,s .G (1996 .) A neuralnetworkbaseddiagnositc tst e sy tsem r

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f armoredvehicleshockabsorber .sExpertSystems hw ti Appilca itons, 11(2 ,) 72 -3 2 44. ]

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[ Chang, .C ,.C & Roschke, .P (1998 .)Neuralnetworkmodeilng fo a magnetorheologicaldampe.r l

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[ G .uo, D ,.L H .u, H ,.Y & ,iY .J .Q (2004 .) Neural network conrtol rf a o semi-acitve vehicle n

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[ lA -Holou, ,.N Lahdhri,i ,.T J .oo, D ,.S Weave ,r ,.J & lA -Abba ,s .F (2002 .)Silding modeneural k

r o w t e

n inference fuzzy logic conrtol rf o acitve suspen ison sy tsem .s Fuzzy System ,s IEEE s

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[ H ,.u,i W & Sihong, .Z (2006 .) Neuralnetworkadapitveconrtol rf o semi-acitve ria suspen ison. r

T ansacitons fo et h ChineseSociety rf o Agricu tluralMachinery, 37(1 ,) 8- .3 1 ]

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[ Chuanyin, ,.T H ,.ua, L W ,.e,i Z ShuWen, ,.Z & GuangYao, .Z (2009 .) Conrtol technology fo e

l c i h e

v acitve suspen ison based no geneitc algortihm da n neural network. Transacitons fo et h e

s e n i h