"Ta’limda raqamli va innovatsion texnologiyalarning o‘rni va istiqbollari" mavzusida ilmiy-amaliy konferensiya to‘plami. 2023-yil 14-dekabr.

Menejment va zamonaviy texnologiyalar universiteti 2023-yil 14-dekabr ZAMONAVIY ARMIYADA RAQAMLI ALOQA TEXNOLOGIYALARINING QO‘LLANISH -78-5&300%6-73*8;%6)()*-2)(2)8;36/-2+8)<2303+-=%0%6-2-2+,%6&-= 73,%(%+-438)27-%0-1/32-=%80%6- 7LIVFIO2EV^YPPEIZ 91*8QEKMWXVERXM %RRSXEXWM]E9WLFYQEUSPEHE7SJX[EVI(IJMRIH2IX[SVOMRK 7(2 XI\RSPSKM]EPEVMRMRK LEVFM] LEVEOEXPEVHEKM XEUHMQ IXEHMKER MQOSRM]EXPEVMRM EXVSJPMGLE ]SVMXMPHM WLYRMRKHIO dunyodagi kuchli armiyalardan biri hisoblanadigan Amerika Qo‘shma Shtatlari EVQM]EWMHE 7(2 XI\RSPSKM]EPEVMRM XEHFMU IXMWLHE IVMWLMPKER ]YXYUPEV ZE YPEV XEUHMQ IXKER TSXIRXWMEP imtiyozlar sanab o‘tildi. Kalit so‘zlar 7SJX[EVI(IJMRIH 2IX[SVOMRK 7(2 7SJX[EVIHIJMRIH [MHIEVIE RIX[SVOMRK 7(;%2 .SMRX -RJSVQEXMSR )RZMVSRQIRX .-) %HZERGIH &EXXPI 1EREKIQIRX 7]WXIQ %&17 ZMWYEP STXMQEPPEWLXMVMWL QEREKIQIRX XEVQSU VIWYVWM \EZJWM^PMO EPKSVMXMQM ERSQEPM]EOMFIVXELHMH &YXYRNELSRVEUEQPMEPSUEXI\RSPSKM]EPEVRMRKVMZSNPERMWLMREXMNEWMHEHYR]SRMRKHI]EVPM istalgan nuqtasidan bog‘lanishlarni amalga oshirishning turli vositalari va texnologiyalari yaratildi. Turli formatdagi ma’lumotlarning cheksiz oqimi va ularni qayta ishlash borasida katta yutuqlarga erishlidi. Shu bilan bir qatorda alohida yo‘nalishlar, xususan, harbiy sohada ma’lumotlarni sifatli va xavfisiz uzatish, qabul qilish va qayta ishlash bo‘yicha umumiy va alohida talablar yuzaga kela boshladi. Ma’lumotlarning ma’zmuni va ahamiyatida kelib chiqib YPEVRM \EZJWM^PMKM ZE WMJEXMKE RMWFEXER XYVPM Y^EXMWL UEFYP UMPMWL ZE UE]XE MWLPEWL QYLMXPEVM ]EVEXMWL XI\RSPSKM]EPEVM YWXMHE UEXSV ]YXYUPEVKE IVMWLMPHM 1E^OYV ]YXYUPEVHER FMVM FY 7SJX[EVI(IJMRIH 2IX[SVOMRK 7(2 XI\RSPSKM]EWM LMWSFPERMF FYKYRKM OYRHE FMV UEXSV ]IXEOGLM XMNSVEX OSQTERM]EPEVM VMZSNPERKER HEZPEX EVQM]EPEV YWLFY XI\RSPSKM]ERM EQEPM]SXKE tadbiq etish bo‘yicha ishlar olib bormoqda. 7SJX[EVI(IJMRIH 2IX[SVOMRK 7(2 XI\RSPSKM]EWM VEUEQPM XIPIOSQQYRMOEXWM]E tarmoqlarini barpo etish, boshqarish va ularga xizmat ko‘rsatish yo‘nilishida o‘ziga xos inqilob qildi. SDN tarmoqni boshqarish muhiti, ma’lumotlarni uzatish, qabul qilish, saqlash va qayta ishlash muhitlarini o‘zaro ajratgan holda, tarmoq qurilmalarini markazlashtirilgan FSWLUEVYZM ZE YPEVRM HEWXYVPEWL MQOSRMRM FIVEHM 7(2 XYVPM WSLEPEVHE NYQPEHER telekommunikatsiya, ma’lumotlar markazlari va bulutli texnologiyalarda keng qo‘llaniladi. SDN texnologiyalaridan foydalanish bo‘yicha bir qator rivojlangan davlatlar keng ko‘lamli dasturlar qabul qilishgan bo‘lib, mazkur yo‘nalishda Amerika Qo‘shma Shtatlari 1YHSJEEHITEVXEQIRXM 8LI97(ITEVXQIRXSJ(IJIRWI WI^MPEVRM]YXYUPEVKEIVMWLKER 1YHSJEE HITEVXEQIRXM LEVFM] XEVQSUPEVHE 7(2 XI\RSPSKM]EPEVMHER JS]HEPERMWLRM o‘rganib chiqdi va harbiy sohada SDN texnologiyalaridan foydalanishni rivojlantirish uchun bir nechta dasturlarni qabul qildi. Asosiy dasturlardan biri Qo‘shma axborot muhiti (Joint Information Environment) tashabbusi bo‘lib, u butun Mudofaa departamenti bo‘yicha yagona axborot muhitini yaratishga qaratilgan. JIE dasturi Mudofaa departamenti bo‘ylab yagona, MRXIKVEXWM]EPEWLKERXEVQSUEV\MXIOXYVEWMRM]EVEXMWLYGLYR7(2XI\RSPSKM]EPEVMRMNSVM]UMPMWLRM o‘z ichiga oladi. Bu dastur rivojlangan tarmoq imkoniyatlarini taqdim etish, tarmoq \EZJWM^PMKMRM SWLMVMWL ZE XEVQSU FSWLUEVYZMRM ]E\WLMPEWL MQOSRMRM FIVEHM 7(2 texnologiyalarini joriy etish orqali Mudofaa departamenti o‘z xodimlariga, ularning joylashgan joyidan qat’iy nazar, yanada samarali tarmoq xizmatlarini taqdim etish imkonini yaratib berdi. 81

Menejment va zamonaviy texnologiyalar universiteti 2023-yil 14-dekabr rasm. AQSh Mudofaa departamenti “Joint Information Environment” HEWXYVMRMRKYQYQM]XSTSPSKM]EWM Shuningdek, Amerika Qo‘shma Shtatlari Harbiyhavo kuchlarining “Advanced Battle Management System” dasturida SD;%2 XI\RSPSKM]EWMRM XEHFMU IXHM %57L LEZS OYGLPEVM XEVQSU YPERMWLMRM ]E\WLMPEWL ZE XEVQSU QYVEOOEFPMKMRM OEQE]XMVMWL YGLYR 7(;%2 XI\RSPSKM]EWMRMNSVM]UMPMWLYWXMHEMWLPEQSUHE7(;%2HEWXYVM,EVFM]LEZSOYGLPEVMRMOIVEOPM ma’lumotlar bilan tezkorlik bilan ta’minlanishi va sohada yanada moslashuvchan va keng imkoniyatli tarmoq infratuzilmasi bilan ta’minlaydi. rasm. AQSh Harbiy havo kuchlari “Advanced Battle Management System” HEWXYVMRMRKYQYQM]XSTSPSKM]EWM Shu o‘rinda NATO tomonidan ta’sis etilgan SDN texnologiyalar sinov maydoni ta’kidlab o‘tish lozim. NATO harbiy amaliyotlarda SDN texnologiyalaridan foydalanish imkoniyatlarini o‘rganish uchun SDN sinov maydonchasini yaratgan bo‘lib, sinov maydoni 7(2 XI\RSPSKM]EPEVM WEQEVEHSVPMKMRM FELSPEWL ZE LEVFM] LEVEOEXPEV YGLYR IRK ]E\WLMPEVMHER foydalanish bo‘yicha tajribalarni amalga oshirishfa yordam beradi. 7(2 XI\RSPSKM]EPEVMRM LEVFM] WSLEHE NSVM] IXMWLHE OEXXE TSXIRWMEP ]YXYUPEVKE IVMWL mumkin. Jumladan, harbiy harakatlar va o‘quv mashg‘ulotlari davomidan mazkur texnologiyalarni qo‘llanish orqali bir qator operativ natijalarga erishish mumkin. SDN texnologiyasi harbiy harakatlarda tarmoq nazoratini kuchaytirish bo‘yicha keng MQOSRM]EXPEVKE IKE HI]E UE]H IXMPQSUHE 9RMRK ]SVHEQMHE XEVQSU UYVMPQEPEVMHEKM HMREQMO 82

Menejment va zamonaviy texnologiyalar universiteti 2023-yil 14-dekabr WS^PEQEPEV ZE XEVQSU VIWYVWPEVMRM HSMQM] STXMQEPPEWLXMVMWL QYQOMR FY IWE LEVFM] haraktlardagi o‘zgaruvchan operatsion talablarga tezda moslashish imkonini beradi. Bundan tashqari, oldindan kiritilgan turli algoritmlar asosida trafikni aqlli yo‘naltirish orqali tarmoq ish JESPM]EXMRM STXMQEPPEWLXMVMWL MQOSRMRM FIVEHM 7LYRMRKHIO QE^OYV XI\RSPSKM]E XEVQSURM ZMVXYEPM^EXWM]EUMPMWLKEMQOSRFIVEHMFYIWEXEVQSUZMVXYEPM^EXWM]EWM]SVHEQMHEFMXXENMWQSRM] MRJVEXY^MPQEHE FMV RIGLXE ZMVXYEP XEVQSUPEVRM ]EVEXMWL REXMNEHE QYLMQ MPSZEPEV ZE ma’lumotlarni xavfsiz izolyatsiyasini hosil qilish imkonini beradi. Virtual tarmoqlar dinamik VEZMWLHE ]EVEXMPMWLM ZE FSWLUEVMPMWLM REXMNEWMHE LEVFM] LEVEOEXPEVHE QSWPEWLYZGLERPMO MQOSRM]EXPEVMRM ]EREHE ]E\WLMPEREHM <EZJWM^PMO RYUXEM RE^EVMHER LEVFM] LEVEOEXPEVHE XYVPM bo‘g‘inlarga bo‘lingan tarmoq xavfsizligini ta’minlay olish qobilayti hozirgacha mavjud XI\RSPSKM]EPEVKE RMWFEXER OYGLPMPMKM FMPER ENVEPMF XYVEHM EPFEXXE FYRMRK YGLYR OIVEOPM algoritmlar va dasturlash bo‘yicha mutaxassilardan yetarli bilim va qobilyatga ega bo‘lish XEPEF IXMPEHM 7(2 XI\RSPSKM]EWM ]SVHEQMHE SPHMRHER ]EVEXMPKER \EZJWM^PMO WM]SWEXM FYXYR tarmoq bo‘ylab dinamik ravishda qo‘llanilishi mumkin, bu xavfsizlik tahdidlariga tezkor javob berishni zamoniv yodashuvlardan biri sifatida e’tibordan chetda qoldirib bo‘lmaydi. 7LY FMPER FMV UEXSVHE QE^OYV XI\RSPSKM]E LEVFM] LEVEOEXPEVHE XI^OSV XEVQSU UYVMWL MQOSRMRMFIVMWLMQYQOMR9WLFYXI\RSPSKM]E]SVHEQMHEXEVQSUVIWYVWPEVMRMHMREQMOVEZMWLHE ta’qsimlash, joylashtirish va qurish mumkin, bu esa o‘zgaruvchan operatsion talablarga NEZSFER XEVQSU VIWYVWPEVMRM XI^OSV UYVMWL ZE MWLKE XYWLMVMWL MQOSRMRM FIVEHM &YRHER XEWLUEVM 7(2 XI\RSPSKM]EWM EZXSQEXPEWLXMVMPKER XEVQSU WS^PEQEPEVMRM EQEPKE SWLMVE SPEHM buning natijasida tarmoqni qo‘lda sozlash uchun zarur bo‘lgan vaqt va resurslar qisqartirilishi mumkin. Harbiy harakatlarda tarmoqdagi jarayonlar haqida kerakli ma’lumotlarni doimiy olish va kuzatib turish imkoniyati ham mavjud bo‘lib, SDN texnologiyasi yordamida tarmoq XVEJMKMRM VIEP ZEUX VINMQMHE XELPMP UMPMWL QYQOMR FY IWE XYVPM ERSQEPM]EPEV ZE \EZJWM^PMO XELHMHPEVMRM XI^HE ERMUPEWL MQOSRMRM FIVEHM &YRHER XEWLUEVM QE^OYV XI\RSPSKM]E XEVQSU XVEJMKM ZE XSTSPSKM]EWMRMRK ZM^YEP XEWZMVMRM LSWMP UMPMWL XEVQSU UEXPEQPEVMRM ]EVEXMWLKE MQOSR FIVEHM So‘ngi so‘z o‘rnidan shuni aytish lozimOM 7(2 XI\RSPSKM]EWM NEHEP VMZSNPERQSUHE ZE LEVFM] WSLEHE JS]HEPERMWL YGLYR OEXXE MQOSRM]EXPEVKE IKE 8EVQSU VIWYVWPEVMRM QEVOE^PEWLXMVMPKERXEV^HEFSWLUEVMWLZEHMREQMORE^SVEXUMPMWLUSFMPM]EXMLEVFM]LEVEOEXPEVHE ko‘proq moslashuvchanlik, chaqqonlik va xavfsizlikni ta’minlaydi. SDN texnologiyasi VMZSNPERMWLHE HEZSQ IXEV IOER LEVFM] EQEPM]SXPEVHE ]EREHE MRRSZEXWMSR ]ERKMPMOPEV ZYNYHKE OIPMWLM ERMU QE^OYV ]ERKMPMPEVRMRK FEVGLEWM 7(2 XI\RSPSKM]EPEVMKE EWSWPERMWL ]SOM YRMRK aynan kerakli imkoniyatlarini o‘zida mujjasam etishi, mazkur texnologiyani tadbiq etish va GLYUYVM^PERMWLPEVSPMFFSVMWL^EVYVEXMRM]Y^KEOIPXMVEHM %HEFM]SXPEV “Software Defined Networking (SDN) for Defense Applications” Nathan K. Johnson, /EP]ER4'LERHVEWIOEVER-)))'SQQYRMGEXMSRW1EKE^MRI “SoftwareDefined Networking and the Future of Military Communications” Scott 2I[QER7MKREP1EKE^MRI “The Benefits of SoftwareDefined Networking for Military Networks” Tim Cox. '-762)8 “The Evolution of SoftwareDefined Networking for Defense Networks” Andrew 0YRH&VEH;SSHJSVH1MPMXEV])QFIHHIH7]WXIQW “Software(IJMRIH 2IX[SVOMRK %R 3ZIVZMI[ ERH -QTPIQIRXEXMSR JSV 1MPMXEV] Networks” Shubhi Asthana, Pramod Kumar. -RXIVREXMSREP .SYVREP SJ 'SQTYXIV 7GMIRGI ERH 1SFMPI'SQTYXMRK 83

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Menejment va zamonaviy texnologiyalar universiteti 2023-yil 14-dekabr ✓ SF]IOXKE QSʻPNEPPERKERPMKM XE]]SV FYXXSR IHMXXI\X XI\XZMI[ QETZMI[ OEFM OSʻTPEFSF]IOXPEVRMMPSZEKEUM]MRGLMPMOWM^OSʻGLMVMFNS]PEWLXMVMWLSF]IOXPEVRMY]IVHERFY]IVKE WYVMWLZELSOE^S ✓ XE]]SV GEQIVE [MJM WSYRH QIHME TPE]IV OEFM OSʻTPEF OSQTSRIRXEPEVHER JS]HEPERMWL ✓ ERHVSMHZEYRHER]YUSVMZIVWM]EPEVYGLYR ✓ grafik va audio fayllarni qo‘shish; ✓ TVS]IOXQERFEOSHMRMOSʻVMWLMQOSRM]EXM .EZEZE<QP ✓ HEWXYV OSH ]S^MF SʻXMVQEWHER XE]]SV HEWXYV USPMTPEVMHER JS]HEPERMF YPEVRM HEWXYVKEOSʻGLMVMFSʻXOE^MWLOEFMOSʻTPEFMQOSRM]EXPEVRMFIVEHM 7OIXGL[EVI MPSZEWMRMRK WQEVXJSRKE SʻVREXMF MWLKE XYWLMVE]PMO =ERKM MPSZE ]EVEXMWL YGLYR VEWQHEKM FIPKMPERKER FIPKMWMRM FSWEQM^ 7LYRHE 2I[ 4VSNIGX QYPSUSX S]REWM SGLMPEHM 9RHE )RXIV ETTPMGEXMSR REQI UMWQMKE ]ERKM MPSZEQM^RMRK RSQMRM OMVMXMF '6)%8) %44FERHMRMXERPEWEO]ERKMMPSZE]EVEXMWLMQM^YGLYRVEWQHEKMHIOQYPSUSXQYLMXMSGLMPEHM /SʻVMRMF XYVKERMHIO SʻRK XSQSRHE YWXYR WLEOPMHE 0E]SYXW ZE ;MHKIXW OEFM FERHPEV FSV 0MRIEV ZE 7GVSPP ;MHKIXW UMWQMHEKM SF]IOXPEVRM Sʻ^ MGLMKE SPMWLM QYQOMR -PSZEHE M\XM]SVM] SF]IOXRMNS]PEWLXMVMWLYGLYRYRMXERPEFMWLGLMWSLERMRKOIVEOPMUMWQMKEWYVMFSPMFOIPMWLFMPER USʻ]MPEHM =YUSVM UMWQMHE :-); OSʻVMRMWL HER XEWLUEVM ):)28 LSHMWEPEV OSH ]S^MWL NS]PEWLXMVMWL UMWQM ZE '31432)28 XE]]SV OSQTSRIRXPEVRM USʻWLMWL FERHPEVMHER LEQ JS]HEPERMWLQYQOMR3\MVMHEMPSZERMETOJSVQEXHEWQEVXJSRHESʻVREXMFMWLPEXMFOSʻVMWLYGLYR TEWHEKM6YRXYKQEWMFSWMPEHM VEWQ-PSZE]EVEXMWLS]REWMrasm. Umumiy ko‘rinish /IPMRKIRHMSʻ^FIOGLEKETMVKERKETPEVMQM^RMQEXROSʻVMRMWLMHESʻXOE^MFFIVEHMKERMPSZE ]EVEXE]PMO VEWQPEV 85

Menejment va zamonaviy texnologiyalar universiteti 2023-yil 14-dekabr VEWQ(EWXYVMWLKEXYWLKERHEKMLSPEX rasm. Sinovdan keyingi ko‘rinish. &YRMRK YGLYR 7OIXGL[EVI MPSZEWMRM MWLKE XYWLMVMF ]ERKM MPSZEQM^ RSQMRM 8SZYWLRM QEXRKEHIFRSQPE]QM^7OIXGL[EVIRMRK]ERKMMPSZE]EVEXYZGLMQYPSUSXQYLMXMSGLMPKERHER WSʻng, VIEW qismidan Linear(V) obyektini ishchi maydon yaʼni kichik telefon rasmini ustiga SPMFOIPMFUSʻ]EQM^7LYRHEWLYSF]IOXYWXMHEFENEVMWLQYQOMRFSʻPKERFEVGLEMWLPEVTEWXHE namoyon bo‘ladi, u bandlardan background color qismidan ilova foni uchun maʼqul boʻPKER FMVSV VERKRM XERPE]QM^ <YHHM WLYRHE] MPSZEQM^KE &YXXSR ZE )HMX 8I\XPEVRM LEQ NS]PEWLXMVEQM^ &YXXSRRM RSQMRM %&' XI\X UMWQMHER FSWMRK ZE KETMVMRKKE )HMX 8I\XRMRK SVUESWXKMQEXRMRM#LMRXUMWQMHERKETMRKM^WLY]IVHEQEXROSʻVMRMWLMHEGLMUEHMKELEQHE YPEVRMRK QEXR SʻPGLEQMRM 8I\X WM^I UMWQMHER KE Sʻ^KEVXMVEQM^ 7LY FMPER MPSZEQM^RMRK OSʻVMRMWLM XE]]SV VEWQ )RHM ]YUSVMHEKM '31432)28 FERHMHER FIPKMWMKE FSWEQM^ /SQTSRIRXPEVHER 7TIIGL8S8I\XRM XERPEF YRKE FMVSV RSQ QEWEPER WXX OMVMXMF %HH USʻWLMWL FERHMRM XERPE]QM^ ,SWMP FSʻPKER OSQTSRIRXMQM^RM YWXMKE FSWMF YRHER SR7TIIGL6IWYPXUMWQMRMXERPE]QM^ VEWQ 7LYRHEOSHNS]PEWLXMVMWL]S^MWLYGLYRQYPSUSX S]REWM SGLMPEHM rasm). Pasddagi doira belgini bosib, chiqqan ro‘yxatdan View bandi tanlaniladi. Viewning ostki ro‘yxatidan TextView: setText qismni yuqoriga When stt onSpeechResult(result)ning ostiga etib joylashtiriladi (ya’ni “tishlatiladi”). VEWQ-PSZERMRKOSʻVMRMWLMRMXE]]SVPEWLrasm. Tayyor komponenta qo‘shish. .S]PEWLXMVMPKER 8I\X:MI[ YWXMKE FSWMF 86 7IPIGX [MHKIX QYPSUSX S]REWMHER

Menejment va zamonaviy texnologiyalar universiteti 2023-yil 14-dekabr )HMX8I\XIHMXXI\X FERHM ZE 7IPIGX XERPERMPEHM ,EQHE ]YUSVMHEKM VIWYPXRM TEWXKM to‘rtburchakka ham ko‘chiriladi. Shunda 7rasmdagidek ko‘rinish hosil bo‘ladi. Endi bitta SVUEKEUE]XMF]YUSVMHEKM:-);UMWQMHER&YXXSRXYKQEWMXERPERMPEHM4EWHHEGLMUUER)ZIRX FERHMHER SR'PMGOOE FSWMPEHM FY MWLRM ]YUSVMHEKM ):)28 UMWQMHER LEQ FENEVMWL QYQOMR 7LYRHE ]YUSVMHE E]XMPKERHIO OSH NS]PEWLXMVMWL ]S^MWL YGLYR QYPSUSX S]REWM SGLMPEHM rasm). Unda Component bo‘limidan SpeechToText: start listening bandini When button1 clickedga biriktirib, SpeechToText: ga yaratilgan stt komponentini (Select) qo‘shib qo‘yamiz. 7LY FMPER MPSZE XE]]SV &MV RIGLE UEHEQHE SVUEKE UE]XMWL XYKQEWMRM FSWMF FSWL IOVERKE o‘tamiz va Run bandini tanlab ilovamizni ishlatib ko‘rish uchun smartfonimizga o‘rnatib SPEQM^-PSZEQSFMPUYVMPQERMRKEWSWM]XMPMRMSʻ^MERMUPEFKETMVMPKERKETPEVRMSʻWLEXMPHE]S^MF FSVEHM7LYRMRKYGLYRILXM]SNFSʻPWEUYVMPQEXMPMRMSʻ^FIOGLEKESʻXOE^MFUSʻ]MWLOIVEO rasm. Nutq natijasini ko‘rsatish. VEWQ8MRKPEWLRMFSWLPEWL Xuddi shunday ilovamizga yana bir Button joylashtirib, u bosilganda tinglashni to‘xtatish buyrug‘i(stop listening)ni qo‘shib ilovani yanada mukamallashtirib boraverish mumkin. Bunday ilovalar orqali ma’ruzachining gaplarini avtomatik ekranda namoyish qilish orqali, (qulog‘i yaxshi eshitmaydigan) tinglovchilarga maʼruzani yaxshiroq yetkazish mumkin. Yoinki boshqa tillarda avtomatik(yozmasdan) tarjima qilish imkoni ham namoyon bo‘lmoqda. %HEFM]SXPEV LXXTWHSGWWOIXGL[EVIMSHSGWKIXXMRKWXEVXIHLXQP LXXTWHSGWWOIXGL[EVIMS 87

Menejment va zamonaviy texnologiyalar universiteti 2023-yil 14-dekabr 1-/6346387)77360-596-01%0%62-/314%/88)780%7,97900%6-:%13()0- 7LEL^SH1EQEXUYPSZ 91*8QEKMWXVERXM %RRSXEXWM]E/SQT]YXIVRMRKIRKEWSWM]UYVYPQEWMFYQMOVSTVSXWIWWSVLMWSFPEREHM9 uncha katta bo‘lmagan hajmda, kremniy yoki germaniy kristalida joylashtirilgan 10 milliondan ortiq tranzistorlar majmuasi bo‘lib barcha turdagi axborotni qayta ishlovchi qurilma LMWSFPEREHM 1MOVSTVSXWIWWSV FMV WIOYRH MGLMHE FMV RIGLE QMPPMSR EQEPPEVRM FENEVMWL MQOSRM]EXMKEIKE9WLFYQESPEHEQMOVSTVSXWIWWSVPEVMQOSRM]EXPEVMZE\YWYWYWM]EXPEVMLEUMHE so‘z yuritilgan. Kalit so‘zlar 1MOVSTVSXWIWWSV \SXMVE IPIQIRXPEVM VIKMWXV EVMJQIXMO QERXMUM] UYVMPQE FSWLUEVYZUYVMPQEWM <SXMVE–ma’lumotlarni tezkor xotiradan o‘qish va bajarishga hozirlash uchun ikkita 1 HEVENEPMZEHEVENEPMOIWL\SXMVEHERJS]HEPERMPEHM 6IKMWXVFENEVMPEHMKERFY]VYUPEVRMZEUXMRGLEPMO\SXMVEHEWEUPEWLXI^OSV\SXMVEEHVIWPEVM va ma’lumotlarni qayta ishlash uchun ishlatiladi. Arifmetik mantiqiy qurilma ko‘p vazifali summator va ikkita yordamchi registrdan tashkil topgan bo‘lib axborotlarni qayta ishlaydi. &SWLUEVYZUYVMPQEQMOVSTVSXWIWWSVMWLPEWLMZEWLMRESVUEPMFSWLUEUYVMPQEPEVFMPER aloqa o‘rnatish uchun singnal ishlab chiqaradi. Mikroprotsessorning arxitekturasini uning so‘z razryadi va mikroprotsessorning ichki axborot shinalarinianiqlaydi. Dastlabki mikprotsessorlar 4 razryadli arxitekturaga ega bo‘lib, PEMPda 8 razryadli mikroprotsessor qo‘llangan, hozirda esa arxitekturasi 16, 32, 64, 128 razryadli mikroprotsessorlar qo‘llanmoqda. 4 va 8 razryadli mikroprotsessorlarda komandalarni bajarish tartibi ketmaOIX bo‘lib, unda bir operatsiya tugagandan so‘ng undan keyingi operatsiyani bajarish boshlanadi. %]VMQ VE^V]EHPM EV\MXIOXYVEKE IKE QMOVSTVSXWIWWSVPEVHE OSQERHEPEVRM FENEVMWL TEVEPIP usulda amalga oshiriladi. U bir vaqtning o‘zida bir operatsiyani bajarish davomida keyingi STIVEXWM]ERMUE]XEMWLPEF\SXMVEHEWEUPEWL\YWYWM]EXMKEIKE <SXMVERMRK EHVIW QE]HSRM EHVIW VIKMWXVMRMRK VE^V]EHM ZE QMOVSTVSXWIWWSVRMRK EHVIW WLMREPEVM SVUEPM ERMUPEREHM VE^V]EHPM QMOVSTVSXWIWWSVPEVRMRK EHVIW VIKMWXVM XE VE^V]EHPM VIKMWXVVE^V]EHPMWLMRERMXEWLOMPIXMKERZE/FE]XEHVIWRM\SXMVEHEWEUPE]HMVE^V]EHPM QMOVSTVSXWIWWSVPEVHE IWE VE^V]EHPM EHVIW VIKMWXVM MWLPEXMPEHM ZE 1FE]X EHVIWRM \SXMVEHE WEUPE]HMVE^V]EHPMQMOVSTVSXWIWWSVPEVHEIWEZEVE^V]EHPMEHVIWVIKMWXVPEVM1FE]X HER+FE]XKEGLEEHVIWRM\SXMVEHEWEUPE]HM%\FSVEXRMUE]XEMWLPEWLZE\SXMVEFMPERE\FSVEX almashish uchun mikroprotsessorda axborot shinasi mavjud bo‘lib, uning razryadi ichki axborot shinasining razryadi bilan bir xil bo‘ladi. Tashqi qurilmalar bilan aloqani SWSRPEWLXMVMWLYGLYRXEWLUME\FSVSXWLMREWMOMGLMOVE^V]EHPMMGLOME\FSVSXWLMREWMZEE\FSVSX registrlari katta razryadli bo‘ladi. Masalan bir xil mikroprotsessorlar 16 razryadli arxitekturaga IKEPIOMRVE^V]EHPMMGLOME\FSVSXWLMREPEVMHERJS]HEPERMPEHM&YPEVQE\WYWQSHMJMOEXWM]EKE ega bo‘lgan 16 razryadli mikroprotsessorlarda bo‘lib ular bir xil hisoblash kuvvatiga ega. Mikroprotsessorning eng muhim parametrlaridan biri uning amal bajarish tezligi bo‘lib, u XEWLUM KIRIVEXSVHER OIPEHMKER WMR\VSR WMRKREP FMPER ERMUPEREHM ,EV FMV QMOVSTVSXWIWWSVHE o‘zining taktik chastota chegarasi mavjud bo‘lib u 0,4 MGs … 3,5 GGs gacha bo‘lishi mumkin. ),1XEOXMOGLEWXSXEWMRMLMWSFPEWLHEZEUXFMVPMKMRMOMVMXMWLXEPEFIXMPEHM ➢ QMPPMWIOYRH QW !W ➢ QMOVSWIOYRH QOW !W ➢ RERSWIOYRH RW !W 88

Menejment va zamonaviy texnologiyalar universiteti 2023-yil 14-dekabr ➢ TMOSWIOYRH TW !W Bu vaqt oralig‘ida elektron impulslarning bosib o‘tgan yo‘li quyidagicha: ➢ W!OQ!QQ ➢ QW!OQ!QQ ➢ QOW!Q!QQ ➢ RW!WQ!QQ ➢ TW!QQ!QQ &YKYRKMOYRHELMWSFPEWLQEWLMREPEVMRMRKXEOXMOGLEWXSXEWM1+WPEVHELMWSFPEREHM ➢ 1+W!W!QOW!Q!QQ ➢ 1+W!W!RW!WQ!QQ Bundan ko‘rinadiki, sxemalar nanosekund diapazonida ishlaydi. 1MOVSTVSXWIWWSV \EVEOXIVMWXMOEWM 1MOVSTVSXWIWWSVPEV \MP LEVEOXVMWXMOE FMPER farqlanadi: tipi (modeli) va taktli chastotali. Eng ko‘p tarqalgan INTEL firmasining I8088, ----7<-(<ZE6IRXMYQQMOVSTVSXWIWWSVPEVMHMVYPEVRMRKIRK birinchi avlodlarida yaratilgan dasturiy vositalarni oxirgi modellarda ham bemalol qo‘llash QYQOMR8EOXGLEWXSXEbu mikroprotsessorning bir sekund oralig‘ida bajaradigan elementar operatsiya sonini ko‘rsatadi, shuni esda tutish kerakki, turli modeldagi mikroprotsessorlar bir vazifani bajaradi, lekin takt oralig‘ida turlicha bo‘ladi. Model qancha yuqori bo‘lsa u bir operatsiyani bajarishi uchun shuncha kam takt kerak bo‘ladi. Shuning uchun I80386 mikroprotsessori I80286 dan ikki barobar tez ishlaydi, o‘sha takt chastota hisobidan. 1MOVSTVSXWIWWSVRMRK XMTPEVM ZE XY^MPMWLM ,EVUERHE] ),1RMRK EWSWM] QEUWEHM informatsiyani qayta ishlashdan iborat bo‘lib biz bilamizki turli tipdagi informatsiyalar sonlar ]SVHEQMHE MJSHEPEREHM ,MWSFPEWL QEWLMREPEVMHE WSRPEV FMPER MWLPEWL YGLYR QE\WYW UMWQ mikroprotsessor qurilmasi mavjud bo‘lib u mantiqiy unversal qurilma hisoblanadi va mantiqiy QEXIQEXMO SHHM] QYVEOOEF EQEPPEVRM FENEVMWL MQOSRM]EXMKE IKE &Y UYVMPQE MRXIKVEP sxemada qurilganligi tufayli uning hajmi uncha katta bo‘lmaydi. 1MOVSTVSXWIWWSVPM qurilmalarni testlashda bir nechta kompakt usullar mavjud. Ulardan ba’zilari: &YMPX-R 7IPJTest (BIST): Bu usulda, qurilma o‘zining o‘zida testlarni bajaradi. BIST ]SVHEQMHE UYVMPQERMRK EZXSQEXPEWLXMVMPKER XIWXPEV XM^MQMRM UYVMWL QYQOMR &YRHE UYVMPQE o‘zining integratsiya etilgan sinov funksiyalari orqali o‘zini tekshiradi. &SYRHEV] 7GER 8IWXMRK &Y YWYPHE UYVMPQERMRK LEV FMV XSQSRM EPSUEHSV WOERGLMTPEV bilan bog‘langan bo‘lishi kerak. Ushbu skan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qurilma boshqa protsentorlarga o‘xshash muhitda ishlaydi va uni testlash imkoniyatini FIVEHM 4S[IV3R 7IPJ8IWX 4378 &Y XIWX YWYPM UYVMPQERMRK FSWLPERMWLM FMPER FMVKE boshlanadi va processor, xotira, qurilmaning modullarini o‘zgartirishga imkon beradi. Har bir usul o‘zining afzalliklari va chegaralari bilan keladi. Testlash usulini tanlashda, qurilma turi, xususiyatlari, tekshiriladigan elementlar soni va kerakli sinovlarning turi e’tiborli bo‘lishi kerak. Bundan tashqari, kompakt testlash usullari o‘z ichiga muammolar va ta’sir ko‘rsatadigan faktorlarga ega bo‘lishi mumkin, shuning uchun har biri o‘zining afzalliklarini va chegaralarini ehtiyotkorlik bilan o‘rganish kerak. 89

Menejment va zamonaviy texnologiyalar universiteti 2023-yil 14-dekabr 1MOVSTVSXWIWWSVPMUYVMPQEPEVRMXIWXPEWLHE'SQTEGX8IWX1SHIP '81 ]SOM'SQTEGX 8IWX %VGLMXIGXYVI '8% HIF RSQPERKER QSHIPPEV QEZNYH &Y QSHIPPEV QMOVSTVSXWIWWSVPM qurilmalarni testlash uchun kerak bo‘lgan testlarni optimallashtirish va ularga eng yaxshi REXMNEPEVRMSPMWLYGLYR]EVEXMPKER CTM/CTA modellari quyidagi asosiy xususiyatlarga ega bo‘ladi: 8IWX OMVMWLPEVM '81 UYVMPQEHEKM QSHYPPEVRM MRXIVJI]WPEVRM ZE UYVMPQERMRK QYLMQ bo‘lgan komponentlarini tekshirish uchun test kirishlarini ERMUPE]HM &Y XIWX OMVMWLPEVM UYVMPQEHEKM QSHYPPEVRMRK JYROWM]EPEVMRM EPSUEPEVMRM ZE MRXIVJI]WPEVMRM XIOWLMVMWLKE ]SVHEQ FIVEHM 8IOWLMVYZNEVE]SRMSTXMQEPPEWLKERXIWXPEV'81'8%UYVMPQERMRKFEVGLEUMWQPEVMRM tekshirish uchun kerak bo‘lgan testlar va sinovlar jadvalini tuzishda yordam beradi. Bu model XIWXPEVSVUEPMUMWQPEVSVEWMHEKMEPSUERMERMUPEWLZE\EXSXSTMWLKEMQOSRFIVEHM 8IWX NEVE]SRPEVMRMRK EZXSQEXPEWLXMVMPMWLM '81'8% QSHIPPEVM EZXSQEXPEWLXMVMPKER testlar orqali qurilmaning avtomatik sinovini va tekshirishini ta’minlaydi. Bu, qurilmaning WMRSZZEXIWXNEVE]SRPEVMRM]IRKMPXI^ZEMWLSRGLPMUMPEHM 4.Xato ma’lumotlarining tahlili va ishlov berilishi: CTM/CTA, test jarayonlariHER olingan ma’lumotlarni tahlil qilish va xato aniqlangan holda ma’lumotlarni ishlov berishga ]SVHEQFIVEHM &Y QSHIPPEV QMOVSTVSXWIWWSVPM UYVMPQEPEVRM XIWXPEWLRM XI^VSU WEQEVEPMVSU ZE ishonchliroq qilish uchun mo‘ljallangan. Ularning asosiy maqsadi qurilmaning istiqbolligi va to‘g‘ri ishlashi haqida ma’lumot olish va qurilmaning istiqbolligini ta’minlashdir. %HEFM]SXPEV П. Добрин, В. Демчишин. "Особенности внедрения услуги переносимости номера абонента применительно к операторам сетей подвижной связи", Мобильные системы, №8, 2005 г. (MVIGXMZI)'SJXLI)YVSTIER4EVPMEQIRXERHSJXLI'SYRGMP3RYRMZIVWEP WIVZMGI ERH YWIVW VMKLXW VIPEXMRK XS IPIGXVSRMG GSQQYRMGEXMSR RIX[SVOW ERH WIVZMGIW 9RMZIVWEP7IVZMGI(MVIGXMZI *-'36% ' 1 6IKYPEXMSR 32 8)0)4,32) 291&)6 4368%&-0-8= LXXT[[[JMGSVEJMIRKPERXMHSGYQIRX*-'36%'1THJ В.Вайпан, С.Гладких. Правовые основы деятельности операторов виртуальных сетей подвижной связи. Право и экономика. 2006, №1 5. “Financial Accounting” ;EPXIV&1IMKW6SFIVX*1IMKW 90

Menejment va zamonaviy texnologiyalar universiteti 2023-yil 14-dekabr %<&3638/31192-/%87-=%8)<2303+-=%0%6-2-6-:3.0%28-6-7,(% NEYRON TARMOQLARNING O‘RNI 2EFM&SFS]IZ AKT va AHI kafedra katta o‘qituvchisi %RRSXEXWM]E 2I]VSR XEVQSU – FY FMSPSKMO RI]VSR tarmog‘iRMRK QSHIPMKE EWSWPERKER QEWLMRE o‘qish EPKSVMXQM 9WLFYRI]VSRPEV WMRETWPEV HIFRSQPERYZGLM QE\WYW XY^MPQEPEVKE bog‘langan. Sinapslar neyronlarga signallarni uzatish imkonini beradi. Neyron tarmoq ko‘p sonli modellashtirilgan neyronlardan hosil bo‘ladi. Ushbu texnologiya bugungi kunda jadal VMZSNPERQSUHE &YXI\RSPSKM]E YGLYZGLMWM^ XVERWTSVX ZSWMXEPEVMRM HEWXYVPEWLHE QEWLMREPEVRM o‘qitishda QEZNYH ma’lumotlar asosida QIHME ZE EYHMS ma’lumotPEVRM ]EVEXMWLHE LEQHE axborot texnologiyalarining boshqa ko‘plab sohalarida keng tarqaldi. Maqolada neyron tarmoqlarni qo‘llash doirasi, ularning tuzilishi, har bir “RI]VSR” RMRK QSHIPM YPEVRMRK FMVKEPMOHEKM JESPM]EXM va ushbu texnologiyaning asosiy algoritmlari ko‘rib chiqiladi. Sun’iy RI]VSR XEVQSUPEVRMRK MWLPEWLM ZE YRMRK FSWUMGLQEFSWUMGL XELPMPMKE SHHM] QMWSPPEV OIPXMVMPKER 7LYRMRKHIO FYKYRKM OYRHE JESPM]EX ]YVMXE]SXKER ZE NEQM]EX YGLYR NYHE JS]HEPM LEQHEko‘plab ilg‘or texnologiyalarni rivojlantirish uchun neyron tarmoqlar ham o‘rganilgan. Kalit so‘zlar RI]VSR XEVQSU QEWLMRElarni o‘qitish HEWXYVPEWL XEWZMVRM ERMUPEWL XEWZMVRMUE]XEMWLPEWLE\FSVSXXM^MQPEVM Neyron tarmoqlar ko‘plab mashinalarni o‘rganish va chuqur o‘rganish dasturlarida qo‘llaniladigan kuchli algoritmlar to‘plamidir. Ular tasvirni aniqlash, tabiiy tilni qayta ishlash, video tahlili va sun’iy intellekt bilan bog‘liq ko‘plab vazifalar uchun ishlatilishi mumkin. Sun’iy nI]VSR XEVQSU – FY MRWSR EWEF XM^MQMRMRK QSHIPMKE EWSWPERKER QEWLMREPEVRM o‘qitish EPKSVMXQM-RWSRQM]EWMQMPPMSRPEFRI]VSRPEVHERMFSVEX2I]VSRPEVWMKREPPEVRMIPIOXVZE OMQ]SZM] WMKREPPEV WLEOPMHE ]YFSVEHM LEQHE UE]XE MWLPE]HM 9WLFY RI]VSRPEV WMRETWPEV HIF nomlanuvchi maxsus tuzilmalarga bog‘langan. Sinapslar neyronlarga signallarni uzatish imkonini beradi. Neyron tarmoq ko‘p sonli neyronlarning o‘zaro modellashtirilgan tizimiHER hosil bo‘ladi. Sun’iy neyron tarmoq odatda XYVPM FSWUMGLPEVHE XEWLOMP IXMPEHM ZE FSWUMGLPEV JESPPEWLXMVMWLZE^MJEWMRMFENEVYZGLMo‘zarobog‘langan ko‘plab tugunlardan iborat. 2I]VSRXEVQSUUY]MHEKMYGLXEFSWUMGLni o‘z ichiga olishi mumkin[1]: /MVMWL FSWUMGLM /MVMWL FSWUMGLMRMRK QEUWEHM LEV FMV OMVYZGLM UM]QEXPEVRM OMVYZGLM ma’lumotlar sifatida qabul qilish 3HEXHE OMVMWL FSWUMGLMHEKM OMVMWL XYKYRPEVM WSRM OMVYZGLM UM]QEXPEVRMWSRMKEXIRK“/MVMWLbosqichi”FMV]SOMFMVRIGLXE“]EWLMVMRUEXPEQPEV”FMPEREPSUE UMPEHMKERXEVQSUHEKMREQYREPEVRMMJSHEPE]HM /MVMWLFSWUMGLMRMRKXYKYRPEVMTEWWMZbo‘lib, ular ma’lumotlarYWXMHEEQEPPEVFENEVQE]HM 9PEV OMVMWLHE FMXXE UM]QEX SPEHM ZE FMV RIGLXE GLMUMWLPEVMHE UM]QEXRM XEOVSVPE]HM /MVMWL FSWUMGLMHERLEVFMVUM]QEXXEOVSVPEREHMZEFEVGLE]EWLMVMRXYKYRPEVKE]YFSVMPEHM =EWLMVMR FSWUMGL=EWLMVMR FSWUMGLPEV YWLFY ma’lumotPEVRM XEVQSU MGLMHEKM OMVMWL qiymatlariga qo‘llaydi. Bunday holda kiruvchi yoylar boshqa yashirin tugunlardan yoki har bir XYKYRKE YPERKER OMVMWL XYKYRPEVMHER OIPEHM 9PEV GLMUMWL XYKYRPEVM ]SOM FSWLUE ]EWLMVMR XYKYRPEV YGLYR GLMUYZGLM ]S]PEVKE YPEREHM =EWLMVMR FSWUMGLHE LEUMUM] MWLPSZ FIVMWL ZE^RPM bog‘lanishlar tizimi orqali amalga oshiriladi. Bir yoki bir nechta yashirin FSWUMGLlar bo‘lishi mumkin. Yashirin tugunga kiritilgan qiymatlar og‘irliklarga ko‘paytirilib, dasturda saqlangan oldindan belgilangan raqamlar to‘plamini ifodalaydi. Keyin bitta raqam ishlab chiqarish uchun vaznli kirishlar qo‘shiladi. 91

Menejment va zamonaviy texnologiyalar universiteti 2023-yil 14-dekabr 'LMUMWL FSWUMGLM=EOYRHE ]EWLMVMR FSWUMGLPEV GLMUMWL FSWUMGLMKE YPEREHM 'LMUMWL FSWUMGLM ]EWLMVMR FSWUMGLPEVHER ]SOM OMVMWL FSWUMGLMHER YPERMWLPEVRM SPEHM NEZSF o‘zgaruvchisining mantig‘iKE QSW OIPEHMKER GLMUMWL UM]QEXMRM UE]XEVEHM 8EWRMJPEWL QYEQQSPEVMHE SHEXHE FMXXE GLMUMWL XYKYRPEVM QEZNYH 'LMUMWL FSWUMGLMHEKM JESP XYKYRPEV chiqish qiymatlarini ishlab chiqarish uchun ma’lumotlarni birlashtiradi va o‘zgartiradi. Arxitektura yoki topologiya deb ham ataladigan neyron tarmog‘ining tuzilishi FSWUMGLPEVWSRMIPIQIRXEVFMVPMOPEVZEZE^RRMWS^PEWLQI\ERM^QMHERMFSVEX?A )RK SHHM] XEVQSU XY^MPMWL FMVPMOPEV MOOM FSWUMGLKE XEUWMQPERKER XEVQSU XY^MPMWLM LMWSFPEREHM OMVMWL FSWUMGLM ZE GLMUMWL FSWUMGLM &YRHE LEV FMV FMVPMO OMVMWL FSWUMGLMHE FMXXE kirish va bitta chiqish mavjud bo‘lib, u kirishHEKMFMVPMOOEXIRKbo‘ladi Sun’iy neyronning ichki tuzilishini va uning kirish joylariga kelgan signalni o‘zgartirish jarayonini ko‘rib chiqaylik. Quyidagi rasmda sun’iy neyronning to‘liq modeli ko‘rsatilgan (1 VEWQ ?A Kirish x1 x2 x3 Og‘irlik w1 Aktivlashtiruvchi funksiya w2 w3 net ∑ f Chiqish Qo‘shuvchi xn wn rasm. Sun’iy neyronning ichki tuzilishi. An’anaviy kompyuterlar algoritmik yondashuvdan foydalanadi, ya’ni kompyuter muammoni hal qilish uchun ko‘rsatmalar to‘plamini bajaradi. Agar kompyuter bajarishi kerak bo‘lgan aniq qadamlarni bilmasa, u muammoni hal qila olmaydi. Neyron tarmog‘ining foydali ma’lumotlar manipulyatsiyasini ta’minlash qobiliyati og‘irliklarni to‘g‘ri tanlashda yotadi. Bu uni axborotni kompyuterda qayta ishlashdan ajratib turadi. Agar ulanish og‘irliklariga tasodifiy qiymatlar berilgan bo‘lsa, unda bunday neyron tarmoq hech qanday mazmunli ish qilmaydi. Ya’ni, ular hali ham qandaydir tarzda to‘g‘ri XERPERMWLMOIVEO&SWLUEGLEUMPMFE]XKERHERI]VSRXEVQSURMo‘qitishOIVEO?A 2I]VSR XEVQSURM o‘qishi – FY tarmoqdan o‘tgandan so‘ng kirish signali bizga kerak bo‘lgan chiqish signaliga aylantiriladigan og‘irlik koeffitsientlari to‘plamini qidirish. Neyron tarmoqlar va an’anaviy algoritmik kompyuterlar raqobat qilmaydi, balki bir birini to‘ldiradi. Arifmetik amallar kabi algoritmik yondashuv uchun ko‘proq mos bo‘lgan muammolar va neyron tarmoqlar uchun ko‘proq mos keladigan muammolar mavjud. Ko‘p WSRPM ZE^MJEPEV YPEVRM IRK ]YUSVM WEQEVEHSVPMO FMPER FENEVMWL YGLYR MOOMXE ]SRHEWLYZRMRK kombinatsiyasidan foydalanadigan tizimni talab qiladi (ko‘pincha oddiy kompyuter neyron tarmog‘ini boshqarish uchun ishlatiladi). Neyron tarmoqlarning muhim qo‘llanilishi ob’ektlarRM ERMUPEWLHMV Ob’ektRM XERMF SPMWL mos ravishda o‘qitilgan bevosita bog‘langan neyron tarmog‘i (2VEWQKE UEVERK ]SVHEQMHE EQEPKE SWLMVMPMWLM QYQOMR -WLPEWL NEVE]SRMHE XEVQSU GLMUMWLPEVRM OMVMWL ob’ektPEVM FMPER bog‘lashni o‘rganadi. Tarmoqdan foydalanilganda, u kirish namunasini aniqlaydi va tegishli GLMUMWL REQYREWMRM GLMUEVMWLKE LEVEOEX UMPEHM 2I]VSR XEVQSUPEVRMRK OYGLM LIGL UERHE] 92

Menejment va zamonaviy texnologiyalar universiteti 2023-yil 14-dekabr GLMUMWLKE Iga bo‘lmagan kirish sifatida namuna berilganda ]Y^EKE GLMUEHM &YRHE] LSPHE XEVQSU FIVMPKER ob’ektHER Ing kam farq qiladigan o‘qitilgan kirish namunasiga mos OIPEHMKERGLMUMWLRMMWLPEFGLMUEVEHM x11 x12 x13 TAN 1 F1 TAN 2 F2 TAN 3 F3 x21 x22 x23 x31 x32 x33 VEWQ. Bevosita bog‘langan neyron tarmoqWXVYOXYVEZM]XY^MPQEWM 1YVEOOEFVSU RI]VSR WMJEXMHE 1EO/EPPS\ ZE 4MXXW QSHIPMRM REQYRE WMJEXMHE SPMWL QYQOMR VEWQKEUEVERK 3PHMRKMQSHIPHERJEVUMWLYRHEOMOMVMWLPEVZE^RPERKERZELEVFMV kirishning qarorga ta’siri muayyan kirishning og‘irligiga bog‘liq[5]. Kirish og‘irligi –FYOMVMWLKE ko‘paytirilganda, vaznli kirishni hosil qiladigan raqam. Keyin bu og‘irlikdagi kirishlar birFMVMKE qo‘shiladi va agar ular oldindan belgilangan chegaradan oshib keXWE RI]VSR MWLKE XYWLEHM &SWLUELEVUERHE]LSPEXHERI]VSRMWLPEQE]HM x1 x2 w1 w2 Ўқитиш/қўллаш ∑ Кириш xn Чиқиш wn Ўқитиш VEWQ1EO/EPPS\ZE4MXXWRMRKNeyron tarmog‘i strukturaviy tuzilmasi. Matematik so‘zlar bilan aytganda, neyron faqatKMRE UY]MHEKM WLEVX FENEVMPWE MWLKE XYWLEHM \[\[\[\R[R"8 /MVMWL og‘irliklari va chegara qo‘shilishi bu neyronni juda moslashuvchan va kuchli qiladi. MakKallox va Pitts neyroni o‘z vaznini yoki chegarasini o‘zgartirib, muayyan vaziyatga QSWPEWLMWLUSFMPM]EXMKEIKE Neyron tarmoqlar ko‘p o‘n yillar oldin ixtiro qilingan, ammo keyinchalik ulardan JS]HEPERMWL YGLYR LMWSFPEWL UYZZEXM ]IXEVPM IQEW IHM ,S^MVKM ZEUXHE 728 MWLPEWLM YGLYR 93

Menejment va zamonaviy texnologiyalar universiteti 2023-yil 14-dekabr mos bo‘lgan juda kuchli video kartalar ishlab chiqilgan. >EQSREZM]RI]VSRXEVQSUPEVRMRK]YXYUPEVMRMUY]MHEKMPEVRMQMWSPOIPXMVMWLQYQOMR 8IRWSV*PS[ — Google tomonidan yaratilgan kuchli kutubxona bo‘lib, u modellarni ishlab chiqish, o‘qitish va joylashtirish uchun keng qamrovli platformani taqdim etadi. U chuqur o‘rganish algoritmlarini qo‘llabquvvatlash uchun mo‘ljallangan va tasvirni aniqlash, XEFMM] XMPRM UE]XE MWLPEWL ZE FSWLUE MPSZEPEV OEFM ZE^MJEPEV YGLYR MWLPEXMPMWLM QYQOMR TensorFlow ilg‘or modellarni ishlab chiqishda juda samarali, chunki u GPU va TPU tezlashuvidan foydalanadi va modelni tezroq va ishonchli o‘qitishni ta’minlaydi; /IVEW – bu ochiq manba Python kutubxonasi bo‘lib, u ishlab chiquvchilarga tezkor prototiplash va neyron tarmoqlarni rivojlantirish uchun qulay interfeys bilan ta’minlaydi. Bu ishlab chiquvchilarga mavjud neyron tarmoqlardagi kodni qayta ishlatish, keng ma’lumot QERFEPEVMHER JS]HEPERMWL ZE OEQVSU OSH UEXSVPM QSHIPPEVRM MWLPEF GLMUMWL SVUEPM ZEUXRM tejash imkonini beradi. Keras ko‘plab yirik mashina o‘rganish kutubxonalari va bulut xizmatlari tomonidan qo‘llabquvvatlanadi, bu esa ishlab chiquvchilarga o‘z modellarini tez va WEQEVEPMNS]PEWLXMVMWLMQOSRMRMFIVEHM 4]8SVGL – FY MWLPEF GLMUYZGLMPEVKE RI]VSR XEVQSUPEVRM SWSRPMO FMPER VMZSNPERXMVMWLKE yordam beradigan kuchli xususiyatlarni taqdim etadigan mashinani o‘rganish kutubxonasi. &Y 4]XLSRda yozilgan va Torch C++ kutubxonasidan foydalangan holda chuqur o‘rganish XM^MQM 4]8SVGL +49lar bilan foydalanish uchun mo‘ljallangan va ulardan to‘liq foydalanadi, bu esa ishlab chiquvchilarga o‘z modellarini tezda o‘qitish imkoniyatini beradi. Shuningdek, u taqsimlangan hisoblashni qo‘llabquvvatlaydi va chuqur o‘rganish modellarini tezroq o‘qitish YGLYRFMVRIGLXEXYKYRPEVHEMWLPE]HM 'EX&SSWX – FY =ERHI\ning kuchli ochiq manba kutubxonasi bo‘lib, u mashinani o‘rganish ilovalari uchun barqaror tezlik va aniqlikni ta’minlaydi. U qarorlar daraxtiga asoslangan algoritmlarni qo‘llabquvvatlash uchun mo‘ljallangan va toifali ma’lumotlarni SPHMRHERMWLPSZFIVQEWHERUE]XEMWLPEWLKEUSHMV'EX&SSWXLEQXEWRMJPEWLLEQVIKVIWWM]E ZE^MJEPEVMYGLYR]YUSVMWEQEVEPMZE]YUSVM'49ZE+49MWLPEWLMYGLYRSTXMQEPPEWLXMVMPKER Google DeepMind neyron tarmog‘i inson nutqiga taqlid qilishni o‘rganganini e’lon qildi. Ushbu texnologiya nutqni aniqlovchi va tarjimon bilan birgalikda turli tillardagi ma’ruzachilar osongina muloqot qilishlari mumkin bo‘lgan dastur yaratish imkoniyatiga ega. Neyron XEVQSURYXURMERMUPE]HMMRWSRHERJEVUUMPQE]HMKERSZS^ZEMRXSREXWM]EFMPERXEVNMQEUMPEHMZE IWLMXXMVEHM 2ZMHIE OSQTERM]EWM IWE \E]HSZGLMWM^ FSWLUEVMPYZGLM EZXSQSFMPPEV YGLYR MWLPEXMPMWLM mumkin bo‘lgan vizual hisoblash neyron tarmog‘ini ishlab chiqdi. Neyron tarmoq allaqachon svetoforlar, belgilar, boshqa mashinalar va odamlarni tanib, gavjum yo‘lda ham mukammal harakatlana oladi. Yo‘llarda qancha ko‘p mashina harakatlansa, neyron tarmoq shunchalik ko‘p o‘rganadi. Nvidia GauGAN neyron tarmog‘ida ishlaydigan Canvas dasturi oddiy yeskizlarni real PERHWLEJXPEVKEE]PERXMVEHM rasmga qarang). Obyektlar va ranglarning konturlarini ko‘rsatish kifoya (ko‘k suv sifatida qabul qilinadi, jigarrang tog‘lar, yashil o‘t kabi). GauGAN neyron tarmog‘i shablonlardan foydalanmaydi, lekin har safar yangi landshaftlarni yaratadi. 94

Menejment va zamonaviy texnologiyalar universiteti 2023-yil 14-dekabr VEWQ'ERZEWHEWXYVM]SVHEQMHEWYRM]MRXIPIOXEWSWMHEXE]]SVPERKERXEWZMV 9WLFY XEHUMUSX REXMNEPEVM RI]VSR XEVQSUPEVRMRK MWLPEWLMRM EWSWPEWL YPEVRMRK XY^MPMWLM va qo‘llanilish doirasi, shuningdek, mavjud SNT tahlillari va boshqa innovatsion XI\RSPSKM]EPEVRMVMZSNPERXMVMWLYGLYRELEQM]EXMRMFELSPEWLHMV <YPSWEUMPMFE]XMWLMQM^QYQOMROMYWLFYQEUSPEHEQYLSOEQEUMPMRKERPEVHERXEWLUEVM boshqa ko‘plab axborot neyron tarmoqlari mavjud. Ularning aksariyati juda foydali va XEOSQMPPEWLXMVMPQSUHE 2I]VSR XEVQSUPEV ]SVHEQMHE EUP FSZEV UMPQE]HMKER XI\RSPSKM]EPEVRM EQEPKE SWLMVMWL mumkin. Neyron tarmoqlarning asosiy afzalligi ularning o‘zo‘zini o‘rganishidir. Sizga kerak bo‘lgan narsa – tizimni o‘qitish uchun yetarli miqdordagi ma’lumot va vaqt. Sinov va xato orqali neyron tarmoq qanday ishlashni tushunadi. Ko‘pgina misollardan o‘rganish USFMPM]EXMHER JS]HEPERKER LSPHE 728 ZE^M]EXRMRK VMZSNPERMWL ILXMQSPPEVM OMVMWL ZE GLMUMWL ma’lumotlari o‘rtasidagi bog‘liqlik noma’lum bo‘lgan muammolarni hal qila oladi. Ya’ni, neyron tarmoqlar ishlab chiquvchining o‘zi ularni hal qilish algoritmini bilmagan muammolarni hal UMPMWLKEUSHMV Neyron tarmoqlar allaqachon mashinani tahlil qilish va o‘rganishning ko‘plab sohalarida keng tarqalgan. Bu texnologiya vaqt o‘tishi bilan rivojlanishda davom etadi. %HEFM]SXPEV 3VIPIR .IVSR 7GMOMXLearn va TensorFlow bilan amaliy mashinani o‘rganish: aqlli XM^MQPEVRMUYVMWLYGLYRXYWLYRGLEPEVEWFSFPEVZEXI\RMOEPEV] Santanu Pattanayak, Mutaxassislar uchun chuqur o‘rganish va TensorFlow. Python da sun’iy intellekt tizimlarini yaratishga matemati19yondashuv, 2019 y. Rashid Tarik, Neyron tarmog‘ini yaratish, 2022 y. Saymon Xaykin, Neyron tarmoqlari: To‘liq kurs, 2019 y. 2I]VSR XEVQSUPEVKE OMVMWL ?)PIOXVSR QERFEEA LXXTWLEWLHSVOGSQY^RI]VSR XEVQSU 95

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II-SHO‘BA BARQAROR RIVOJLANISHNI TA’MINLASHDA RAQAMLI IQTISODIYOT VA MOLIYAVIY TEXNOLOGIYALARNING O‘RNI