Ukuhlaziywa kokubikezela ekuvimbeleni izingozi ohlelweni oluyinkimbinkimbi

Izibalo zokubikezela zingasekela ukuphathwa kwengozi ngokuhlonza lapho ukwehluleka kungase kwenzeke khona nokuthi yini engenziwa ukuze kuvinjwe.

Isikhathi sokufunda esilinganiselwe: 6 imizuzu

Umongo

Izinkampani zikhiqiza amanani akhula njalo edatha ahlotshaniswa nokusebenza kwebhizinisi, okuholela kuntshisekelo evuselelwe kuzibalo eziqagelayo, inkambu ehlaziya amasethi amakhulu edatha ukuze zibone amaphethini, zibikezele imiphumela kanye nokuqondisa izinqumo. Izinkampani nazo zibhekana nohlu oluyinkimbinkimbi nolukhula njalo lwezingozi zokusebenza ezidinga ukuhlonzwa ngokuqhubekayo futhi zincishiswe. Nakuba izinkampani eziningi seziqalile ukusebenzisa izibalo zokubikezela ukuze zibone amathuba okumaketha/okuthengisa, amasu afanayo awavamile kakhulu ekulawuleni ubungozi, okuhlanganisa nokuvikeleka.

Ama-algorithms okuhlukanisa, ikilasi elivamile lezibalo zokubikezela, angaba wusizo ikakhulukazi ezimbonini zokucwenga nezamakhemikhali e-petrochemical ngokubikezela isikhathi nendawo yezigameko zokuphepha ngokusekelwe kudatha yokuhlola ehlobene nokuphepha nokugcinwa, okuyizinkomba ezihamba phambili. Kunezinselelo ezimbili eziyinhloko ezihlotshaniswa nale ndlela: (1) ukuqinisekisa ukuthi izinkomba eziholayo ezilinganisiwe empeleni zibikezela ukuphahlazeka kanye (2) nokulinganisa izinkomba ezihamba phambili njalo ngokwanele ukuze zibe nevelu yokubikezela.

I-Metodologia

Ngokusetshenziswa kwedatha yokuhlola ebuyekezwa njalo, imodeli ingadalwa kusetshenziswa ukuhlehliswa kwezinto. Ngale ndlela ungakha imodeli, ngokwesibonelo, ukuze ubikezele amathuba okuhluleka kukajantshi wemayela ngalinye lethrekhi. Amathuba angase abuyekezwe njengoba idatha eyengeziwe iqoqwa.

Ngokungeziwe emathubeni abikezelwe okwehluleka kwezitimela, ngemodeli efanayo singakwazi ukuhlonza okuguquguqukayo okunokuqinisekiswa okukhulu kokuqagela (lezo ezinomthelela omkhulu ekwahlulekeni kwesitimela). Ngokusebenzisa imiphumela yemodeli, uzokwazi ukuhlonza kahle lapho kufanele ugxilise khona ukunakekela, ukuhlola kanye nezinsiza zokuthuthukisa imali kanye nokuthi yiziphi izici okufanele ubhekane nazo phakathi nale misebenzi.

Indlela efanayo ingasetshenziswa ezimbonini zokucwenga nezephethroli ukuze kulawuleke ubungozi ngokubikezela nokuvimbela izingozi, inqobo nje uma izinhlangano:

• Khomba izinkomba ezihamba phambili ezinobuqiniso bokubikezela;
• Bavame ukukala izinkomba ezihamba phambili (ukuhlolwa, ukugcinwa nokugcinwa kwedatha yemishini);
• Bakha imodeli yokubikezela uhlelo olusekelwe ezinkomba ezilinganiselwe;
• Buyekeza imodeli njengoba idatha iqoqwa;
• Sebenzisa okutholiwe ukuze ubeke eqhulwini ukugcinwa, ukuhlolwa kanye namaphrojekthi okuthuthukisa izimali kanye nokubuyekeza izinqubo/izinqubo zokusebenza;

Ukuhlaziya Okubikezelayo

Izibalo eziqagelayo yinkambu ebanzi ehlanganisa izici zemikhakha eyahlukene, okuhlanganisa ukufunda ngomshini,ubuhlakani bokufakelwa, izibalo kanye imininingo yedatha. Izibalo eziqagelayo zithola amaphethini namathrendi kumasethi amakhulu wedatha. Olunye uhlobo lwezibalo ezibikezelayo, ama-algorithms okuhlukanisa, lungaba usizo ikakhulukazi ezimbonini zokucwenga kanye ne-petrochemical.

Ama-algorithms okuhlukanisa angahlukaniswa njengokufunda komshini ogadiwe. Ngokufunda okugadiwe, umsebenzisi unedathasethi efaka izilinganiso zezinto eziguquguqukayo ezingase zixhunywe emiphumeleni eyaziwayo. Kumodeli okuxoxwe ngayo esigabeni sesifundo sendaba yalesi sihloko, izilinganiso zethrekhi ezihlukahlukene (isb. ukugoba, ukuwela) kuthathwe phakathi nenkathi yemayela ngalinye lethrekhi. Umphumela owaziwayo, kulesi simo, owokuthi ingabe kwenzeka ukwehluleka kwethrekhi kwimayela ngalinye lesitimela phakathi naleso sikhathi seminyaka emibili.

I-algorithm yokumodela

Kube sekukhethwa i-algorithm efanelekile yokumodela futhi isetshenziselwe ukuhlaziya idatha futhi kuhlonzwe ubudlelwano phakathi kwezilinganiso eziguquguqukayo nemiphumela ukuze kudalwe imithetho yokubikezela (imodeli). Uma isidaliwe, imodeli inikezwa idathasethi entsha equkethe izilinganiso zezinto eziguquguqukayo nezibikezelo ezingaziwa futhi izobe isibala amathuba omphumela ngokusekelwe emithethweni yemodeli. Lokhu kuqhathaniswa nezinhlobo zokufunda okungagadiwe, lapho ama-algorithms ethola amaphethini namathrendi kudathasethi ngaphandle kwanoma iyiphi inkomba ethile evela kumsebenzisi, ngaphandle kwe-algorithm esetshenzisiwe.

Ama-algorithms okuhlukanisa avamile ahlanganisa ukuhlehla komugqa, ukuhlehliswa kwezinto, isihlahla sesinqumo, inethiwekhi ye-neural, i-vector esekelayo/umshini wokubandlulula oguquguqukayo, i-naive Bayes classifier, nezinye eziningi. Ukuhlehla komugqa kunikeza isibonelo esilula sokuthi i-algorithm yokuhlukanisa isebenza kanjani. Ekuhlehleni komugqa, umugqa wokulingana ongcono kakhulu ubalwa ngokusekelwe kumaphuzu edatha akhona, okunikeza isibalo somugqa ay = mx + b. Ukufaka okuhlukile okwaziwayo (x) kunikeza isibikezelo sokuhluka okungaziwa (y).

Ubudlelwano obuningi phakathi kokuguquguqukayo emhlabeni wangempela abukho ngomugqa, kodwa buyinkimbinkimbi futhi abumbeki ngendlela engajwayelekile. Ngakho-ke, ukuhlehla komugqa ngokuvamile akusizi. Amanye ama-algorithms okuhlukanisa ayakwazi ukwenza imodeli yobudlelwano obuyinkimbinkimbi, obufana nobudlelwano be-curvilinear noma i-logarithmic. Isibonelo, i-algorithm yokuhlehla kwelogistic ingaba ubudlelwano obuyinkimbinkimbi, ingahlanganisa okuguquguqukayo okungezona izinombolo (isb., izigaba), futhi ngokuvamile ingakha amamodeli angokoqobo navumelekile ngokwezibalo. Okukhiphayo okujwayelekile kwemodeli yokuhlehla kwempahla amathuba aqagelwe omphumela/umcimbi owenzekayo. Amanye ama-algorithms okuhlukanisa ahlinzeka ngokukhipha okufanayo ekuhlehliseni izinto, kodwa okokufaka okudingekayo kuhlukile phakathi kwama-algorithms.

Ukulawulwa kobungozi

Ukumodela ubudlelwano obuyinkimbinkimbi buwusizo ikakhulukazi ekulawuleni ubungozi, lapho ubungozi ngokuvamile bubekwa eqhulwini ngokusekelwe emathubeni nakubunzima obungaba khona bomphumela othile. Ukwenza imodeli yezinto eziyingozi ezinikela kulowo mphumela kubangela isilinganiso esinembile nesivumelekile ngokwezibalo samathuba omphumela. Ngokuphambene, ukuhlolwa kwezingcuphe eziningi kulinganisa “amathuba” esikalini sezigaba (kanye ngeminyaka eyishumi, kanye ngonyaka, izikhathi eziningana ngonyaka), okunganembe kangako, okunengqondo kakhulu, futhi okwenza kungenzeki ukuhlukanisa phakathi kwezingozi ezikhona engozini. isigaba esibanzi esifanayo. Akhona amanye amasu okuhlola ngokulinganisa ubukhali obunamandla ekuhlolweni kwengozi, kodwa lokhu kungaphezu kobubanzi balesi sihloko.

Ukufundwa Okuhlobene

• Iyini i-Predictive Analytics, futhi kungani ibalulekile ebhizinisini
• Ukuhlukaniswa kwama-Machine wokufunda ama-algorithms: Ukuqothuka komugqa, ukuhlukanisa kanye nokuhlanganiswa
• isheshisa ekuvikelweni kwe-aerospace kanye nokwenza idijithali kokuphepha. I-Genoa Polo yamakhono kazwelonke ezimboni
• U-Spondee uyazalwa, isiqalisi sokuqala okuthi, ngenxa ye-algorithm, ibale futhi inikeze iziqinisekiso eziphelele zokuhlukaniswa kwabanikazi bezimoto ezisetshenzisiwe.
• Isibonelo sokufunda ngomshini ngePython: Ukuhlehla Kwemigqa Eminingi

BlogInnovazione.it