[Dissertation] An Exploration and Development of Current Artificial Neural Network Theory and Applications with Emphasis on Artificial Life

126 Pages·1997·0.28 MB·English

by  David J. Cavuto

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GN IL NROTEEOR KEHEFRNCBOEISLNGANE
A N E DN ANOITAROLPX D F OTNEMPOLEVE C TNERRU
A LAICIFITR N LARUE N KROWTE T HEORY AND A SNOITACILPP
HTIW E N OSISAHPM A LAICIFITR L EFI
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May 6, 1997
TRNE NEOHEE P ITCMRO FNNEOODO UECFCNEINAHTCATRSVADA
TRNE NEOHEE P ITCMRO FNNEOODO UECFCNEINAHTCATRSVADA
GN IL NROTEEOR KEHEFRNCBOEISLNGANE
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tropp ues hlt lra offfa tds nyatluc atfnemtrap e EdeErit nee hktna h otetk idllu o,IwrevoeroM
.srae yeh ttuohguorh te mnwoh seva hyeh t)noitarelo tdna (tnemegaruocn edna
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?ero mro fks aenoyn ana C.semi tdoog
ostkna hltaicepS bi eeLhT , suomaeS y,mesru o fcdo n,a niffuM .
,stner a y pomstkna hdt nnaoitaicerp ptasom tyuM otuv asCir odD neagroeG tn.aahcIW
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e hetrolp x oe styidu tssi h fteosopr uephT seitilibissop t elNaru elNaicifit rtAnerr u ycdberef fo
lacigolo ieb h.Tsessenka edw nsahtgner trsie hetnimret edd nsaeigolop odt nsaerutcur t)sNNA(
gnitsi xhet idwetsartn odc ndaerapm odc n,adeweiv e sreirutcur tNs NdAnih enboitaripsni
e rsatnemirep xleanoitida r.Tsledom demrofrep yroe hytfir e ovsterutcur tgsnitsi xeese hhtt iw
ero metagitsevn idna seitilibissop eh tgninimax ef odn eeh to tdetcudno cs iydut ssih T.
reorutcur t sasear edhenif e sdhici h”w,ef illaicifitr ae“tae r ocstN NgAni s fuyotilibissop
,smsinag rloacigolo i obdtetubirt tyal nyollacip ystcitsiretcara hscyalps ihdci hmwhtirogla
e bo tnwoh ss iygolopo tNN Aemo shguohtl A.sessecor pmodnarno n,gnitaeperno nyllausu
e bdenimrete der asmhtirogl aNN Agnitsix e,smetsy slacigoloi bf otah to tralimi sylhgih
ademre t,erutcurt sNN Awe nA.roivahe bf oepy tderise deh tetarene go ttneiciffusni
lacigoloi bhti wnommo cn iseitrepor pero msessapmocn ehcih w,dengise ds i,”norepmeT“
sadae s susicihpa regltr u ntdoes atbnemnoriv nleautr i.Avsrossecede rsp tdi inda hstnoruen
eh thti wdemrofre pstnemirepx E.norue nf oepy twe neh thti wtliu bte nlarue naro fdebtset
.noihs adfetsissa n nu nanira e olyttili bsa tmirifn o ocmte ensorepmeT
ii
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1. ABSTRACT ii
2. BACKGROUND 1
2.1 B IOLOGICAL N ER UFTOA N LARUE C SLLE 1
2.1.1 P LACISYH S NORUE NLACIGOLOI BF OERUTCURT 1
espan y,Ssetirdn e,Dno x,Ayd o1B.1.1.2 1
rettimsnart o2r.u1e.N1.2 3
p mmuuPissatoP/mui d3o.S1.1.2 4
esl udpezin o4I.1.1.2 6
noitasu agCnihtoN-ro-l l5A.1.1.2 8
2.1.2 M LACITAMEHTA R FNOOITATNESERPE N EVRE C SESSECO RLPLE 10
snoitcennocret nliacisy hep h otntoitalerr olcacitameht a1M.2.1.2 10
stup nfniooitanibm orcaen i2L.2.1.2 11
stupt uyoranib-ra erynoran ingbinitlus egrnidlohser h3T.2.1.2 12
2.2 A LAICIFITR N LARUE N RI E DHSNTTAE A PPLICATIONS 14
2.2.1 G LARENE T YROEH 14
2.2.1.1 Purpose 14
2.2.1.2 Structure 14
gnitad ptUhgi e3W.1.2.2 15
2.2.2 P SNORTPECRE - C NOITACIFISSAL 15
re yealLg n1i.S2.2.2 15
drawr o2Pf.L d2M-e.e2F.2 18
2.2.3 H DLEIFPO N TE - P NRETTA R NOITINGOCE 21
2.2.4 G SNOITAZILARENE 22
2.3 O DNE IRRUF A PLYSIA 24
2.3.1 G LARENE O SNOITAVRESB 24
2.3.2 S FYORAMMU R TNAVELE E STNEMIREPX 25
1.n2o.i3t.a2utibaH 25
noitazitis n2e.S2.3.2 26
2.3.3 R STE NLARUE NO TNOITALE RDN AECNAVELE 26
iii
SEHCA O.R3PPA 27
3.1 G LARENE M DSNDAOHTE T SDLEOSOU 27
NBNAAL T1A.M1.3 XOBLOOT 27
3.1.2 J AVA 29
egak cnaoPr u1e.N2.1.3 29
tnemnoriv neEsuoMeltr u2T.2.1.3 30
2.3P NOIT ANROORLTPPXEECRE 32
3.2.1 E SNOITAROLPX 32
stim ieLz ikSro w t t 1ete-Ss Ne1T.1.2.3 35
tteAnSi ot tje2 ssS2-ei.TD1.2.3 35
noig edResol c tn3 -eEtSs e3T.1.2.3 36
ttenB S itt o es4j4S es.-Ti1D.2.3 38
3.2.2 C SNOISULCNO 41
ecap sgf-noninoitit r1a.P2.2.3 41
fynotivitisn e2S.2.2.3 ht 1 - onrtey al ht noitacificepsred nruey al 41
scitsiru elhufesu-n osnemitem o nssitlus enroitul o’stselpmi sd‘n i ofytcnedn e3T.2.2.3 42
3.3 S REKAEP D NOITAITNEREFFI 43
3.3.1 G LARENE I AED 43
2.3.3A SEHCAORPP 43
3.3.3 C SNOISULCNO 45
3.4 T NOREPME 47
3.4.1 E F ONOITULOV M Y BDERIPSN ILEDO A AISYLP 48
3.4.2 D F ONOITPIRCSE M LEDO 48
3.4.3 D F ONOITPIRCSE T DEBTSE 49
4.4.3V ST ETSS ESTUOIRA —O WEIVREV 51
stnemtsuj deal ugrninra e:t L1etSs e1T.4.4.3 52
snoru efrnoebm u:t N2etSs e2T.4.4.3 55
sesn efssoepyT/rebm u:t N3etSs e3T.4.4.3 55
noiti seolpcat st:tbe 4sOS4e.T4.4.3 56
5.4.4.3 tseT teS :5CIDS 56
3.4.5 C SNOISULCNO 56
roivah elBlare v1O.5.4.3 56
segna hecl ugrninra e2L.5.4.3 57
s tt e sssoeettsnop s3e.R5.4.3 59
snoisulcn olCaren e4G.5.4.3 61
vi
4. CONCLUSIONS 63
5. FUTURE CONSIDERATIONS 67
SECID N.E6PPA 70
6.1 A PPENDIX :A BALTAM EDOC 71
6.1.1 P NOITAROLP XNEORTPECRE 71
6.1.1.1 HINTONEM.M 71
6.1.1.2 PLOTEM.M 71
6.1.1.3 SET1.M 71
6.1.1.4 SET6.M 73
6.1.1.5 TESTNET.M 74
6.1.2 S REKAEP D NOITAITNEREFFI 76
6.1.2.1 HAMDIST.M 76
2M..2A.T1A.D6DAER 76
6.1.3 T NOREPME 78
6.1.3.1 LCTEST.M 78
6.2 A PPENDIX :ABVAJ EDOC 79
1.2.6 NORUENP EGAKCA 79
avaj. n1o.r1u.e2N.6 79
avaj.nort p2e.c1r.e2P.6 81
ava j3..B1F.p2e.c6reP 82
6.2.1.4 Input.java 83
avaj.norep m5e.T1.2.6 84
6.2.2 T SMARGO RTPSE 89
avaj.telppAesuoMit l1u.M2.2.6 89
avaj.telppAp m2e.T2.2.6 103
7. BIBLIOGRAPHY 117
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F ERUGI 2–1. A LLEC EVREN LACIPYT [ G UYTON , 4]. 2
F ERUGI 2–2. A ESPANYS EHT NI TNESERP SERUTCURTS EHT FO NOITACIFINGAM [ G UYTON , 126]. 3
F ERUGI 2–3. D FO YTILIBAEMREP GNIYRAV DNA STNEIDARG NOITARTNECNOC OT EUD SNOI FO NOISUFFI
LAITNETOP ENARBMEM A NI STLUSER ENARBMEM [ G UYTON , 64]. 5
F ERUGI 2–4. S MUIDO -A ETAERC OT STNEIDARG RIEHT TSNIAGA SNOI SEVOM PMUP MUISSATOP
LAITNETOP ENARBMEM TSER LAMRON EHT FO NOITAZIRALOPED [ G UYTON , 64]. 6
F ERUGI 2–5. C FO NOISREVNO PTA TO PDA MUIDOS EERHT EGNAHCXE OT PMUP NOI FO NOITCA NI
SENO MUISSATOP OWT ROF SNOINA [ G UYTON , 68]. 6
F ERUGI 2–6. D REBIF EVREN A GNOLA GNITAGAPORP ENOZ DEZIRALOPED FO NOITCIPE . 7
F ERUGI 2–7. E ESPANYS EHT TA ILUMITS YROTIBIHNI DNA YROTATICX [ G UYTON , 131]. 8
F ERUGI 2–8. S EXCITATORY BOTH WITH PRESENTED SOMA OF ACTION UMMING (E) IANNHDI BITORY (I)
ILUMITS [ G UYTON , 134]. 9
F ERUGI 2–9. G LLA EHT GNIWOHS HPAR - OR - NOTHRIEANSTCGPHTP OOEIONF OTS NEE N TIAL [ G UYTON ,
79]. 10
F ERUGI 2–10. E TUPNI HCA X DETAUNETTA SI (DEIFILPMA RO ) WEIACG OBHNYTS TANT ,W WHICH
ESPANYS EHT TA DESOPMI NOITAUNETTA LACISYHP EHT OT SETALER . 11
F ERUGI 2–11. S STUPMI FO MUS DETHGIEW A SA DELEDOM EB NAC AMOS TA TCEFFE REIFILPMA GNIMMU .12
F ERUGI 2–12. C FO MARGAID KCOLB ETELPMO N LEDOM LARUE , RANILNON DNA SAIB HTIW
THRESHOLDFIUNNGC TION . 13
F ERUGI 2–13. S REYAL LARUEN ELGNI . E ERITNE NA STNESERPER ELCRIC HCA N LEDOM LARUE , HCAE
IMPUTSN WITH , TUPTUO ENO DNA . 16
F ERUGI 2–14. G EHT FO NOITCIPED LACIHPAR ROX MELBORP . T TSHWEEOT S X( DNA S O S ) YLRAENIL ERA
REYAL NORTPECREP ELGNIS A YB DENOITITRAP EB TONNAC EROFEREHT DNA ELBARAPESNI . 18
F ERUGI 2–15. T EERH - REYAL PNLM LARUE N KROWTE . 19
F ERUGI 2–16. F YLLUF A NI SNORUEN EVI - CONNECTED H KROWTEN DLEIFPO . A DEEF STUPTUO NORUEN LL
NEURONSOTHER ALL TO . 22
F ERUGI .71–2B OF VIEOWT TOM A AISYLP C ACINROFILA . 24
F ERUGI 3–1. C EHT ROF YHCRAREIH SSAL N EGAKCAP NORUE . A EHT MORF TIREHNI SESSALC LL N NORUE
SSALC TCARTSBA . 29
F ERUGI 3–2. T WODNIW TNEMNORIVNE LAUTRIV ESUOMELTRU . T ELTRU ( MIDDTLHEET RIINA NGLE )
WODNIW DNUORA SEVOM , DNIHEB LIART A GNIVAEL .W DNUORA POOL SEGDE WODNI . 30
F ERUGI 3–3. T HE ROX MELBORP ( EVOBA ) SOLUTION ITS AND ( WOLEB ) YLLACIHPARG DETARTSULLI . 33
F ERUGI 3–4. T RASITNTAIHTNEIFG SO TRI CS ROX SOLUTION ( INITIALRIAZNADTOIMO NWITH ). 34
F ERUGI .5–3R( YLHGUO ) NOITULOS NOIGER NOITCELES RALUCRIC . 37
iv
F ERUGI 3–6. T SOLUTIONREGION SELECTION CIRCULAR THE FOR STATISTICS RAINING . 38
F ERUGI 3–7. S WEITAT H “ HOLE ” EHT YB DEIFISSALC YLLUFSSECCUS 3- REYAL .PLM 39
F ERUGI 3–8. T NOIGER SUOIVERP EHT ROF ATAD GNINIAR . 40
F ERUGI .9–3B TUPNI ETARENEG OT GNISSECORPERP NOITAITNEREFFID REKAEPS FO MARGAID KCOL
THE FOR VECTORS NNA ROSSECORP . 44
F ERUGI 3–10. M CAOINNT ROLLWIINTNGHID ENO W T TELPPA DEBTSET NOREPME .W EZIS WODNI ( I.E .
XIRTAM NI SNORUEN FO REBMUN ) CONITSR OBLYL ED LMTH RETEMARAP . 50
F ERUGI 3–11. T TNEMNORIVNE LAUTRIV ESUOMELTRU . 51
F ERUGI .21–3W EIGHSTEO TN E . 52
F ERUGI .31–3B EMIT REVO ESUOMELTRUT FO NOITUCEXE CISA [ L - R , T - B ], NOITAZILAITINI EVOBA GNISU
WEIGHTS . 57
F ERUGI 3–14. T ELPMAXE SUOIVERP SA KROWTEN EMAS FO SEIRES EMI (SEULAV LAITINI LACITNED HTIW ),
XAM HTIW /MWIENI GCHOTN DITIAODND ED . 59
F ERUGI 3–15. C ONTRATTSPOWTIPO C TURWEIBSTO HT TTPOWIMOC TURES . 60
F ERUGI 3–16. I SNOITIDNOC LAITINI NO ECNADNEPED EVITISNES FO NOITARTSULL . 61
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dnuorgkc an2—BoitceS
2. Background
1.2 slleC larueN fo erutaN lacigoloiB
n sanawo ntkcurtsn olcacitameht a nmdanuo rsaretn escise hsti h ftloa eddo oeagcniS
artificial neural net s,astcurtsn oecse h fteorut aen hnto pdunap x oeltufe s eulbl i tw,i)NN A(
feorutcur tes hott nkio o el,whc u ss.Angis erdie hrt onfoitarips nliacigoloisy hep h stlalew
t utoni o opttnatrop m si ti.In agmnidulc n,iserutae rgcniv iyln a nmdinu o sfsall eecvr elnautca
f odlei feh tn itca fs adetpecc ayllarene ger asnoitanalpx egniwollo feh tf oyna mhguohtl ataht
to ns imetsy ssuovre neh tf ostra ptnenopmo ceh tf ohca ef onoitcnu fetamitl ueh t,ecneicsoruen
,denife dylesicerp to ner ayna mdna snoisulcno chcu semo s,revewo H.niatre cro fnwon knev e
larue nlaicifitr af otnempoleve deh totn ithgisn ignidivor pf oesopru peh tro fere hdetaepe rera
.retpa hrce t nadailnu oenf bascN NgAnidrag enroissucs iedr o.Mserutcurts
2.1.1 lacisyhP erutcurtS fo lacigoloibnoruen
larue nnamu hdnuor aretne clli wnoissucsi dgniwollo feh t,ytirailima fdn ayticilpmi sroF
eh tf otso mo tezilarene go dniere hdetneser psnoisulcno cdn astca feh t,revewo H.serutcurts
nasierutcur tlsaru eense h fteolpma xceifice pes n tokao orlet alll i ew,wtc a nf.Idlr olwamina
s anwon klians Aplysia gniwollo feh ttah teto nesael p,osl A. txet etelpmo caya wo nn isi
orteda eer hetzirailim a ofsttpmet tyaler e tm.Iroivah edb nearutcur tlsl elcaru e fnnooitpircsed
.sretpa hrcet a nlkir oew hetrips nhici hswesseco rdp nsaerutcur tcsifice pesht
2.1.1.1 Body, Axon, Dendrites, Synapse
.metsy ssuovre nlartne cru on itnemel egnissecor pnia meh te bo tthguoh ter aslle cevreN
.metsy ssuovre neritn eeh tn islle cevre nnoilli b00 1tuob aeva hyllarene gsnamuH
r o,lle cevre nehT noruen eh tn inoitiso placisyh pst iy bdenife dhca e,snoige rlarene gruo fsa h,
r,oyd olbl eec h.Tnoitcn usf t silal e swlalec amos eh thcih wn onoitadnuo fcisa beh tsedivor p,
snoitcn ugfnitroppus-ef iclis aeb hstedivo rops l ta.Iwo rng alcl eec h ftsotr arpehto
.c t,enoitcudorp e,rtnemhsinelp e,rtnemhsiru o -nl-l elcacigolo iyb n facoitsiretcarahc
:t imor fegrem ehcih wserutcurt snoitcennocretn if osepy tow tsa hydo blle cehT setirdned d na
eht noxa e h.Tsetirdn eydn asm ayhllacip ytt u,bno xea nyol nso ayhllaren engoru ehnc a.E
slang iysrr ascetirdn e.Dsnoru erneh t ooytd olbl eec hmto ryfa wlaang iesvr een hsteirr ancoxa
o tni sdraw e bna clle cevre ncisa beh t,hcu ss A.snorue nreht of osnox aeh tmor fydo blle ceh t
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