Dab tsi yog cov algorithms rau dynamically kho qhov pib?

Raws li kev siv cov complexity thiab cov xwm txheej, algorithms rau dynamically altering thresholds yuav muab faib ua peb pawg. Cov pawg no nthuav tawm txhua yam los ntawm kev kwv yees ntse mus rau kev siv hluav taws xob sib sib zog nqus, hloov mus rau ntau lub peev xwm txhim kho:

1. 3σ Statistical Dynamic Update Method (qhov nrov tshaj plaws, hnav, thiab yooj yim siv)

Hauv kev lag luam chaw, qhov no yog qhov yooj yim tshaj plaws siv algorithm. Nws yog qhov tsim nyog rau feem ntau ntawm cov chaw tsim khoom, muaj cov laj thawj ncaj ncees, thiab yuav tsum muaj lub zog ua haujlwm me me:

Cov ntsiab lus tseem ceeb: Qhov pib tshiab ntawm 3σ tau teeb tsa (99.7% ntawm kev hloov pauv ib txwm nyob rau hauv ib txwm faib yuav poob rau hauv qhov ntau; tshaj qhov ntau yam no suav tias yog txawv txav); txhua lub limtiam kev txheeb cais ntawm keeb kwm sib txawv ntawm ob lub sensors raug muab tso ua ke; tus qauv sib txawv σ ntawm qhov sib txawv yog xam;

Cov txiaj ntsig: hloov tau rau kev laus thiab hloov pauv kev ua haujlwm; tsis siv neeg hloov kho raws li cov ntaub ntawv ua haujlwm tiag tiag; tsis xav tau preset chaw;

Cov xwm txheej cuam tshuam: Feem ntau ntawm cov tshuaj txhaj tshuaj molding ntau lawm, uas yog thawj qhov kev xaiv rau cov tuam txhab me thiab nruab nrab -cov tuam txhab loj uas tau tsim thiab hloov kho lawv tus kheej.

2. Cov txheej txheem Matching Fuzzy Rule Algorithm (Adapts to Multiple Processing Switching)

Qhov kev pab cuam no siv fuzzy logic txuam rau dynamically hloov cov txheej txheem raws li cov kev cai predefined nyob rau hauv lub sij hawm thaum ntau lawm hloov ntau zaus:

Core Logic: Fuzzy inference cia li txiav txim siab txog qhov pib raws li cov khoom siv xws li cov khoom precision, raw khoom corrosivity, thiab siab ntsuas. Rau "siab iav fiber ntau + siab siab + precision," piv txwv li, nws qhia qhov tshwm sim nrog 15% qhov pib so.

Cov txiaj ntsig: Tsis xav tau cov qauv sib txawv rau txhua yam khoom; cia li hloov mus rau ntau yam txheej txheem ua ke.

Kev txhaj tshuaj molding chaw nrog ntau yam khoom, cov khoom me me, thiab kev hloov kho sai sai yog cov piv txwv ntawm cov xwm txheej siv tau.

3. Predictive Adjustment Algorithm for Machine Learning (High-End Intelligent Scenarios)

Qhov tseeb tshaj plaws kev kwv yees kev hloov pauv hloov pauv tau ua tiav los ntawm txoj hauv kev no, uas yog raws li kev cob qhia cov qauv siv cov ntaub ntawv tsis zoo yav dhau los.

Kev Ua Haujlwm Zoo Tshaj Plaws: Txhawm rau sib txawv ntawm "qhov tseeb kev laus sib txawv" thiab "kev ua haujlwm cuam tshuam kev sib txawv," siv cov qauv kev faib tawm (xws li logistic regression lossis lub teeb yuag CNN), hloov qhov pib rau qhov sib txawv tiag tiag. Raws li lwm txoj hauv kev, ua ntej - nthuav dav qhov pib los tso tseg qhov chaw laus thiab siv LSTM los kwv yees qhov sib txawv ntawm kev loj hlob.

Cov txiaj ntsig: Muab piv rau cov txheej txheem sib xyaw ua ke, nws tuaj yeem txheeb xyuas qhov cuam tshuam nyuaj thiab txo qis lub tswb tsis tseeb los ntawm ntau dua 30%.

Cons: Yuav kom cob qhia tus qauv, ib qho tshwj xeeb ntawm cov ntaub ntawv keeb kwm tsis zoo yuav tsum tau sib sau ua ke, yuav tsum tau ua haujlwm ntau ntxiv.

Loj -scale batch ntau lawm scenarios thiab ntse, siab -kawg txhaj molding manufacturing kab yog cov piv txwv ntawm cov xwm txheej.

Tam sim no, cov kev daws teeb meem zoo tshaj plaws ntawm lawv yog kev sib xyaw ua ke ntawm 3σ statistical dynamic hloov kho txoj kev + txheej txheem kev sib khi, uas ua tiav cov kev xav tau ntawm ntau dua 90% ntawm cov xwm txheej thiab tsis xav tau txoj kev txhim kho rau kev siv.