e O l M d l QUAN LY
SUf DUNG MANG NEURON N H A N TAO DlT BAO THdl GIAN THI CONG CAU CO V 6 N NGOAI NGAN SACH NHA Nl/dC
USING NEURAL NETWORKS TO PREDICT CONSTRUCTION TIME FOR EXTRA-BUDGETARY-STATE BRIDGE PROJECTS
VU DUY L I N H \ NGUYEN HOV PHUC^ LE HOAI LONG%
N G U Y I N V A N CHAU^ DANG NGOC CHAU"
TOM TAT
Nghidn cdru ndy da xdy di^g mfit md hinh mang neuron nhdn tao de du bao thdi gian thi cdng cOa cac di/ dn c^u cd ngu6n von diu VJ ngodi ngdn sdch nhd nUdc. Difa trgn dif lidu cOa 15 du an clu c6 ngu6n v6n diu tif ngodi ngdn sdch nhd nifdc tttu thdp di/dc, mOt md hinh mang neuron nhan tao da dudc phat tii^n tCf cac bien diu vdo Id cdc ydu t6 cd dnh hi/dng idn ddn thdi gian thi cdng cua du dn. Kdt qud cho thiy rdng md hinh mang neuron nhdn tao dUdc xay dung trong nghidn ciiti ndy cd the dUdc sljr dung d^ du bdo thdi gian thi cdng cOa cdc di/ dn cdu cd ngudn v6n dau tif ngodi ngdn sdch nhd nudc d giai doan chua cd thiet kd cd sd.
Tuf khda: Mang neuron, DU tiao, Thdi gian ^ i cdng, Du dn clu, Qudn ly Xay dung, V6n ngodi ngan sach ABSTRACT
This study developed an artificial neural network (ANN) model to predict the construction time of extra-budgetary-state bridge projects. Based on data from 15 extra-budgetary-state bridge projects, an ANN model for predicting the construc- tion time was developed using ttie input variables which are the factors significantly affecting the construction time. The resutts showed that tiie developed ANN model can be used to predict the construction time for extra-budgetary-state bridge projects at the early design stages.
Keywords: Artificial neural network, Predict, Construction time. Bridge projects. Construction management. Extra-budg- etary-state bridge projects.
1.Gl6lTH|gU
Viet Nam la mot trong nhung nutJc c6 t6c 66 tang trir&ng kinh t l cao (v^o nam 2014, dat 5,9% vk dung thir 2 th§ gidl). Kinh t l phdt trien c^ng nhanh thi site ep I3n ccf s6 ha t4ng giao th6ng c^ng idn. Trong khi ngudn v6n ngan sach nhd nirdc con han hep thi viec keu gpi cdc ngudn vdn ddu ti/ ngo^i ngan sach id rdt cdn thilt.
Hi§n nay, n^odi ngudn v l n ngdn sdch nhd ni/dc, cdn cd cdc ngudn vdn khdc nhir v l n trdi philu chfnh phu, v l n doanh nghidp tu nhdn, vdn ODA... Bo giao thdng vdn tdi da cd nhi^u k l hoach de thu hut cdc loai ngudn v l n ndy {da kiln til nam 2016-2020, Bd Giao thdng Van tdi se thu hilt khodng 235 ngdn ty ddng ddu tir vao cd sd ha tdng cho ITnh vuc giao thdng).
Mpt trong nhijmg khd khdn ma cdc nhd ddu ti/trong Unh vi/c giao thdng g^p phai la viec ki^m sodt thdi gian thi cdng cua dir dn. NSu kiem sodt t i t thdi gian vd tiln d$ cua dir dn thi cdc nhd ddu tu cd thi hi^u rd hdn v l du dn cua minh de cd k l hoach chuin bj t i t hon cho dl/ dn. Dac bi|t Id cdc dir dn Idn va phuc t^p nhir dir an cau. Di gdp phdn giilp nhung nhd ddu ta cd thdm cdng CM de kiem sodt thdi gian thuc hien da dn cua minh m^t cdch t i t hon, nghien cOu da xdy dyng mdt md hinh mang neuron nhdn tao de ado lUdng thdi gian thi cdng cua cdc di/ dn ciiu co ngudn vdn ddu ta ngoai ngdn sdch nhd nudc. Nghien cuu sO dung du^ lieu cua 15 da dn cdu cd ngudn vdn ODA va ngudn vdn ddu ta doanh nghiep da hodn thdnh tnrdc nam 2005 de xdy dimg md hinh. Nghien curu ndy da xdc djnh dirpc cac yeu td chinh dnh hudng d i n thdi gian thirc cua cdc da
dn cdu dudng bd vdi ket cdu thdng thudng va xdy dung duoc md hinh mang neuron nhan tao de da bdo thdi gian thuc hidn da dn. Sir dung mdt md hinh da bdo cd th^ giam cac idi sal sdt do chu quan gdy ra vd ta md hinh cdc chu ddu ta cdn cd thi dilu chinh cdc y i u td ddu vao cua da dn nho: phaong an kit cdu, phodng dn ban quan iy, phaong dn nhd thdu... d^ dat duoc cdc kit qud ddu ra nha mong muin.
2. MANG NERON NHAN TAO
Mang neuron nhan tao Id mdt trong nhung phaong phdp sCr dung cac thudt todn de md phdng qud trinh truyen tdi vd xur ly thdng tin cua cdc neuron trong bd ndo con ngadi. Phucmg phdp nay ra ddi rdt sdm ta culi t h i ky XIX ddu the ky XX. Vdo thdp nidn 50 cua t h i ky XX, mang neuron nhdn tao bdt ddu daoc uffig dung, nhung mang neuron nhdn tao mcrt thyc saphdt trien manh me d nhOng thdp nien 80 bdi sa hd trpf cua mdy tinh. Vdi nhi3ng au diem nha khd nang hpc ttr du"
lieu qud khir, khd ndng tong qudt hoa cao, cd the sCr dung khi thdng tin ddu vdo thieu va phi tuyin... mang neuron nhdn tao da daoc img dung trong nhilu Unh vuc nha y hpc, quan sa. kinh t l , cdng nghd thdng tin, xOiy hinh dnh, quan ly xdy dung...
Mdt cdu true mang neuron bao gdm mpt Idp ddu vao dai dien la cdc y i u td (biln) da daoc xdc djnh trong giai doan 1 cua nghien cOu, mdt hodc nhilu Idp an (hidden layer) vd mpt Idp ddu ra Id gid tn thdi gian thuc hien ciJa da dn. Hinh 1 t h i hien mdt cdu true mang neuron diln hinh gdm 3 Idp an.
Mang neuron nhdn tao da dUdc ung dung trong
NGUdi XAY DUNG SO THANG 3 & 4 - 2015
SOf DUNG MANG NEURON NHAN TAO DU BAO THCll GIAN...
Input Layer Hidden Layer Output Layer i(l IOD) j ( l torn) k m
Hlnh 1. ciu tnic mang neuron nhan tao diin hinh khd n h i l u nghidn cifu d cd trong va ngodi nudc.
Chang h?n nha, Ludn vd Giang [1] ting dyng mang neuron nhdn tao ho tro cdng tdc ehpn thdu thi cdng theo quy djnh ddu thdu tai Vi§t Nam vdi 5 b i l n ddu vdo. Khoa vd cdc tdc gid [2] sir dung matlab di xdy dyng md hlnh udc logmg chi phi xdy dang chung c a vdi 6 b i l n ddu vdo. Emsley vd cdc tdc gia [3] da phdt trien md hinh m^ng neuron ta gdn 300 da dn thu thdp dQ lieu v l chi phi cua cdc da dn xdy dong. Jin va Zhang [4] xdy dung md hinh mang neuron nhdn tao de phdn bd rui ro trong cdc d a d n PPP. Wiimot vd Mel [51 sO dung mang neuron nhdn tgo d l xdc djnh chi phi cho dudng cao tdc. Williams [6] da xdy dyng md hinh mang neuron nhdn tgo vd md hinh hdi quy d l da doan chi phf hodn thdnh cdc da dn dudng cao tdc ta cac dO lieu ddu thdu.
3. PHUONG PHAP NGHIEN CUU 3.1. Thu thap dilliiu
Nghidn ciru ndy dope thyc hidn bdng hai giai doan.
Giai doan mdt thyc hidn cudc khdo sdt bang bdng cdu hdi. Dua tren nhCmg ddc trung co bdn ciJa mdt d a dn cdu, mdt bdng cdu hdi da dope t h i l t k l s d bd vdi muc dich tim dope nhung ydu t l cd dnh hudng idn d i n thdi gian thac hien da dn. Cd 20 chuyen gia da duoc mdi de tham gia gdp y v l sa ddy du, rd rdng, d l hieu yd phu hdp ciJa bdng cdu hdi. NhOng chuyen gia ndy d i u cd tren 20 ndm kinh nghiem trong ITnh voc xdy dong cdng trinh giao thdng. Hp da tung dam nhdn n h i l u vj tn' quan trong trong cdc da dn khdc nhau vdi cdc vai trd nhachCi ddu ta, ban qudn ly d y d n , t a v d n gidm sat, vd nhd thdu thi cdng. Vdi sa gdp y cda cdc chuydn gia, mdt sd y i u t l dd bj loai bd vl khdng phCj hop vdi d i l u kien dViet Nam. Ddng thdi, daatrdn nhung tinh hudng da xdy ra trong qud trinh thuc hi?n da dn, nhdm chuyen gia dd xudt daa them mot sd y i u td mdl vao bdng cdu hdi. Sau qud trinh dieu chinh, mdt bdng khdo sdt dope gCfi ngapc lai cdc chuyen gia de k i l m tra thir. Tdt ca cdc chuyen gia ddu d i n g y y(A bdng cdu hdi dope thdnh lap vdi 18 y i u td vd duoc danh gid bdng thang do Likert ndm mite dp t a " 1 = Hodn todn khdng ddng y" d i n "5 = Hodn todn ddng y". Bang cdu hdi duoc gtJri tryc t i l p t(A nhung ngudi duoc xdc djnh trade Id cd n h i l u kinh nghidm trong Unh vyc dang khao sdt. Tdt ca cdc bdng khdo sdt d i u dupc gCri bang hlnh thOc tare t i l p . NhQng ngudi tham gia deu dUdc
gidi thfch v l bang cdu hdi vd mite quan trpng ciJa nghien ciru. Ho daoc hodng ddn ddnh gid theo kinh nghiem bdn thdn. K i t qud thu thap v l dope tong hpp, tfnh todn diem trung binh vd xdp hgng dya tren di^m trung binh dd. Thdng qua cudc khdo sdt, 12 y i u t6 chfnh dnh hodng d i n thdi gian thi cdng ciia d o d n cdu da dope xdc djnh (loai bd nhOng y i u td cd diem trung binh < 3). Giai do^n 2 dope t i l n hdnh diKi trdn k i t qua ciia giai doan thQ n h i t . DQ lieu dope eung cdp tQ nhilu ngudn khde nhau nha chii ddu ta, ban qudn ly dydn, ta vdn gidm sdt, vd nhd thdu thi cdng. Tdng c^ng 15 bd dQ lieu dope cung cdp ddy dii thdng tin vd thdi gian thi edng vd cdc dgc d i l m ciia d y an. NhQng thdng tin dope eung cdp ddu dode ldy tQ hd s d cua nhQng dy dn da hodn thdnh trudc nam 2005. Thdi gian thi cdng cua d y an dope tinh Id thdi gian thi cdng thuc t l (da trO di nhung ngdy nghi, nhung ngdy ngOng thi cdng vi nhung Ij^ do khde). Sau khi t^ng hdp dQ lieu, cd thdm 2 y i u td bj loai Id ngudn vdn vd d i l u kien dja chdt vi tdt cd cdc bd dQ lieu thu thdp dope d i u Id nhOng dy an cd v l n d i u t o ngodi ngdn sdch nhd node vd ciing cd d i l u kidn dja chdt phOc tgp.
Bdng 1 . Xep h^ng cic yeu to
H;ng 1 2 3 4 5 6 7 8 9 10 11 12
Cic yiu ti inh hutngMnlMt gtan Tii tring ttilceng Unh NhiMuthicOng 5,00 NguJn »6n* 4,50 aSukl^naiachSf 4,40 Qj^uhi^ncungcipccicnguygnvdtli^ . . ^
chinh ' "
Chiiu dii ciu 4,00 Gia diim thi cang 3,90 (liiuki$nthu}van 3,90 OOnviquinlSidl^in 3,90 RiirangSnkitcauthUdngbS 3,80 Diiu |4^ giao Oifing tai ndi tht/c high . j . d u ^
fidn gia Im^dlSn tich mat cJu 3,50 ChiiurlJnBciu 3,50
Xip hfng 1 2 3 4 5 6 6 6 9 10 11 11 Ghi chu: * ih ydu td bi loai do dif liBu thu thip tfutfc cd cdng 0c (Sh
3.2. Die diim dif lieu
Tong cdng cd 15 d o d n duoc thu thdp gdm 8 dydn ngudn vdn ODA vd 7 d y dn v i n ddu t o doanh nghi$p.
Ve chidu ddi cdu, cd 2 do dn (13,3%) cd c h i l u ddi ti^
100 d i n dadi 2a0m, 3 d a d n (20%) cd e h i l u ddi ta200 d i n dudi 400m, 9 d o dn (60%) cd c h i l u ddi cdu ta 400 d i n dudi 600m, vd 1 d a dn (6,7%) cd chidu ddi trdn 600m. v l b l rdng cdu, cd 11 da dn (73,4%) ed b l rpng khdng qud 12m, 2 d y dn (13,3%) cd b l rdng tir 12m d i n khdng qud 20m, vd 2 d a dn (13,3%) cd b l rdng tO 20 d i n 24m. D i l u ndy cho thdy r i n g k i t qud cua nghien cOu ndy cd le dope dp dung phiJ hpp hon cho nhQng d a d n cd c h i l u ddi cdu khdng qud 600m vd b l rdng cdu khdng qud 24m.
3.3. Md hinh
Dua trdn cdc y i u td dupc ehpn, nghien cQu tiln hdnh thu thdp vd xQ ly sd li^u. Loa ehpn cdu true mang thfch hpp de tidn hdnh hudn luydn. Mang dupc hudn
NGU0I XAY DUNG SO THANG 3 & 4 • 2015
SOf DUNG MANG NEURON NHAN TAO DU BAO THOll GIAN...
luy^n vdi nhilu cdu tnic khdc nhau de loa chon ra mdt cdu hlnh cho sai sd nhd nhdt. Cdc bode xdy dong md hlnh nha sau:
- Phdn mdm hd trp: SPSS 16.0
- Biln ddu vdo (input layer): T^ng cpng 10 biln dupc sir dyng lam biln ddu vdo cho md hlnh (Bang 1) - Sd Idp an (hidden layer): Sd Idp an dope khdo sdt ta 1 din 2 ldp
- Biln ddu ra (output layer): Thdi gian thi cdng ciJa dadn
- Sd neuron: Sd neuron duoc xdc dinh dya tren cdng thdc l-Jn+m din 2n + l , vdi n la sd biin ddu vdo vd m Id sd bidn ddu ra (Liu, 1998 [6]). Ong vdi tnjPng hpp 10 bidn ddu vdo sd neuron dope xdc djnh trong khoang tO 8 din 21 neuron. D l md hinh dat kit qud tit nhdt, nghidn cOu ndy dd khdo sdt ldn loot vdi trodng hpp sd neuron chay tO 8 din 21.
- Hdm truyin: Dya tren ddc di^m phi tuyin ciia bp sd li$u ndn cdc hdm truyin Idp input vd Idp output dope m$c djnh Id Hyberbolic Tangent vd Identity.
- Chi dp hpc gidm sdt (supervised) vdi thudt todn lan truydn ngupc (back-propagation). Mang neuron nhdn tao sir dung thudt todn lan truyin ngupc gim cd 2 bode. Trong bode 1, tin hi$u di td Idp ddu vdo, qua cdc Idp ^n vd tdi Idp ddu ra, trong qud trinh truydn tfn hieu md hlnh daa ra bp trpng s i ngdu nhien, kit qua dope so sdnh vdi kit qud thac t l d l xdc djnh sai sd.
N I U sai sd chua dat ydu cdu thi md hinh tilp tuc thyc hten budc 2. Trong bode 2, md hlnh se truydn cdc tin hidu ngapc trd Igi de dilu chinh bd trpng sd sau khi didu chinh bd trpng s i tin hidu tilp tue truydn tdi idp ddu ra vd so sdnh sai sd. Qua trinh lan truydn ngapc dode lap di lap lai eho tdi khi sai sd dat dope kit qua mong mu6n.
- Gidi thudt tli au hda: Trong md hinh, giai thudt tdi ou hda dupc mac cSnh Id gidi thudt "scaled conjugate gradienf. Cdc gidi thudt tdi ou hda nhdm Idm tang nhanh dd hdi tu trong qud tririh hudn luydn, ldm gidm thdi gian hudn luy^n. MOt sd thudt todn tdi ou hda dope dimg trong qud trinh hudn luydn nhin Steepest Descent, Newton, Conjugate Gradient...
Trong nghien cQu ndy, 12 bd s i lidu dupc sir dung dS huin luyen (chilm 80%) vd 3 bO sd lieu dope sQ dung de kiem tra (chilm 20%). Md hinh dope lya ehpn cd edc gid trj gdm sai sd phln tram (percentage error, PE), sai sd phln trdm tuydt ddi trung binh (mean absolute percentage error, MAPE) vd hd sd xdc djnh (coefficient of determination) R^ dat kit qua tdt nhdt trong 203 md hlnh dd dope thQ. Cdu true thich hpp nhdt vdi 2 Idp ^n, cdc hdm truyin Idp input vd Idp out- put dope m§c djnh Id Hyberbolic Tangent vd Identity, sd neuron ciia ldp I n 1 vd 2 ldn loot Id 4 neuron vd 11 neuron. Cdc gid tn PE vd MAPE vd R' dope tinh theo cdng thite sau:
pg^(Predicted-Actual)^,Pp,^^
Actual (1)
1 " IPredicted - Actual!
MAPE = - y J ; ; ^ X 100% (2)
„ 7 , Sum of squared errors
R = 1 (3) Total sum of squares
Trong dd, n la sd ldn dy bdo, predicted Id gid trj dy bdo, vd actual Id gia trj thuc t l .
Bang 2. Kit qua cua mo hinh ANN
PE Max 7,98%
M n -7,04%
Training MAPE 3,09% 0,9940
Test MAI=E 5,28% 0,9045
Bang 2 vd 3 trinh bay cdc kdt qua xdy dang md hinh. Gid tri MAPE cua bd hudn luyen vd bd kiem tra ldn lupt Id 3,09% vd 5,28%. Gia trj PE ndm trong khoang tO -7,04% din 7,98%. Gid tri R= ciia bd hudn luydn yd bO kiem tra ldn lupt Id 0,9940 vd 0,9045 eho thdy rang md hinh cd the gidi thfch dupc trdn 90%
phoong sai. Gia trj R^ cdng tiln gdn din 1 thi mun;; dd thfch hpp cua md hlnh vcri bd sd lieu cdng cao. Cdc gid tri PE cho thdy tdt cd cdc dQ li$u hudn luydn vd dQ lidu kilm tra diu cho kit qua do bdo rdt tit (sai sd dutfri 10%).
Bdng 3. Gii tri phin trim sai s6 PE sn
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Testing Training
PE -5.62214 - 3 1 8 5 6 4 -7.04827 -2.57602 -1.07925 -0.50238 2.838624 2.697834 - 1 4 4 6 1 4 -6.42383 -0.12132 7.981973 7.56805 -4.3838 0 5 5 4 1
Gill C M
Min
Max
4. Gldl HAN COA NGHIEN COiJ
Nghien ciru nay chi dilmg Igi d vide sQ dung 15 dy dn cdu vdi kit cdu thdng thodng cd ngudn vdn ddu to Id vdn ODA vd vdn ddu to doanh nghiep vd tdt ca cdc dQ lidu diu dope thu thdp tgi vung ddng bdng sdng Ciiu Long vd miln Ddng Nam B^. Hy vpng edc nghidn cOu sau se khdc phuc dij/oc cdc gidi han ndy
5. K^T LUAN VA KIEN NGHI Nghien cOu dat dope cdc kit qua nha sau:
- Nghien ciifu dd xdc dmh dupe 12 yiu t l cd dnh hodng ddng ke ddn thdi gian thue hidn cua dadn cdu thdng qua cudc khdo sdt y kiln chuyen gia.
- Daa trdn nhQng bd sd lidu thu thdp dupc, nghidn cQu da xdy dong dupc mdt md hlnh mgng neuron nhdn tao de do bao thdi gian thi cdng cua cdc da dn cdu d giai dogn chua ed thilt kd cd sd.
Vdi kit qua dgt dope, nghidn cQu cd mdt vdi d l xudt nho sau:
(Xem tidp trang 19)
NGUdi XAY DUNG s 6 THANG 3 & 4 • 2015
BO LUdNG SU HAI LONG TRONG CONG VIEC.
5. Ket tuan
Ket q u a khao sdt mQe muc do hai Idng vdi cdng v i ^ eiJa lao ddng nO ngdnh xdy dyng t h d n ^ qua ehi sd m d t a edng vide rut gpn (AJDI) vd chT sd t i n g the cdng vide rut gpn (AJIG) eho thdy lao dong nQ ngdnh xay d y n g khd hdi Idng vdi cdng vide eua hp.
Cdc k i t qud eua nghidn cQu ndy ed the giup cac ben lidn quan c i i a mpt d o d n xdy dyng ma dac biet la lanh dao cdc edng ty thi cdng hieu ro hon tdc ddng cua nhQng y i u td ndi trdn d i n mQe dp hai Idng trong edng vide cua nQ cdng nhdn xdy d y n g , tii do cd nhung chfnh sdch hpp ly de thu hOt cung n h o duy tri dpi ngu lao ddng nO trong ngdnh xdy dyng Vidt Nam (VN) v d gdp phdn thuc day binh ddng gidi trong ngdnh xdy dong V N . Q
m li^u tham Miio
[1.) Al-A]mi, R. (2QQA). The effect of personal charactenstics on job satisfaction: a study among male managers in the Kuwait oil indus-
try, IntemationaiJoumal of Commerce & Management. Voi. 11, pp.91- 101
[2,] Brayfield, A.H., & Rothe, H.F. (1951), An index ofiob satis- faction. Journal of Applied Psycholofly, 35,307 - 3 1 1 .
[3.] Dainty, A.R.J., Bagilhole, B.M., and Neale, R.H (2001). A4a/e and female perspectives on equality measures for the UK constmction sector. Women In Management Review, Vol. 16, Iss: 6, pp.297 - 304
[4.] Ironson, G. H., Smitii, R C , firannick, M. T , Gibson, W. M ,
& Paul, K. B., (1989). ConstibJbon of a Job in General Scale: A Comparison of Global, Composite, and Specific Measures, Journal of Applied Psychology, 74, (1989), 193 - 200
[5.] Kunin, T. (1955). The construction of a new type of attitude measure. Personnel Psychology, 8 , 6 5 - 7 7 .
[fi.] U K.H. & Dinh T.H. (2008). Jim hieu thdc trang ngi^i lao ddng xay ddng ngoai tinh dang lim vi$c tai Hi Ndi. Trang tin di§n tu' T^ng hoi xSy di/ng ViSt Nam.
[7.] LiAj TV & Ngfl MT. (2011), Cic nhan td inh hUdng ddn sd thda man ddi vdi cong vide cua cbng nhin vi ky sif xiy dUng. Tgp chi xay Dimg (80 XSy Di/ng). S6 ttiSng 05-2011, frang 60-62
[8.] Price, J.L. (1997), Handbook of Organizational Measurement, Intemational Journal of Manpower, 18.303- 558
19.] Russell, S. S., Spitzmuiler. C , Lin, L, R, Stanton, J . M..
Smith, R C , & Ironson, G. H. (2004). Shoder can also be better Vie abridged job in general scale. Educational and Psychological Measurement, 64(5), 878.
[10.] Smith, R C , Kendall, L M., & Hulin, C. L (1969).
Measurement of Satisfaction in Work and Retirement, Chicago, IL:
Rand McNally.
[11.] Stanton, J. M., Smar, E. R, Balzer, W. K., & Smith, R C.
(2002). Issues and strategies for reducing the length of self-report scales. Personnel Psychology, 55,167-194,
[12.] Tr^n TT. (2003).J"ao viec tim cho tao ddng nSnUdc ta hi$n nay. Tap chi C$ng s^n di§n tiJ, s6 37 - 2003.
SUf DUNG MANG NEURON
N H A N TAO Dl/ BAG...
(liSp tfieo trang 13)
- Nhung ngodi tham gia thue hi^n ede d y dn cdu nen chQ y d i n 12 y i u td cd dnh hodng dang ke ddn thdi gian thi cdng cda d y dn de k i l m sodt tdt hon thdi gian thyc hi^n vd t i l n dO cOa d o dn.
- Bdi vl m d hinh mgng neuron nhdn tao ed the ddnh gtd dope mQc d d dnh hodnq cua eae y i u td ddn thdi gian thyc hi^n cua d y dn edu ndn dya vdo md hinh, cdc nhd d i u t o cd t h i thay d l i ede phoong an n h o phuwng dn k i t edu, phuong dn nhd thdu, phoong dn ban qudn ly... sao eho b i l n ddu ra dat dope gid trj n h o mong m u i n . Tir dd, cdc chu ddu t a cung se cd cdch nhin t6ng qudt hon vd daa ra ede q u y l t djnh ddu t o ehfnh xdc hon.G
TAI U | U THAM KHAO
1. RH. Luan va RT Giang (2006), "Nghidn cdU dng dyng mang neuron nhin tao hd trp cdng tic chon thdu theop quy dinh ddu thdu Vidt Nam", Tgp chi Phit tn^n Khoa hge vd Cflng nghfl, tap 9, s6 7.
15-24.
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3. Emsley, M.W., Love, D.J., Duff. A.R., Harding, A., Hickson, A.
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