ScholarGate
Msaidizi
MCDMNormalizationcrisp

Vector Normalization — Upimishaji wa safu yenye vipimo vya Euclidean (upimishaji wa L2)

NORM-VECTOR (Vector Normalization — Upimishaji wa safu yenye vipimo vya Euclidean (upimishaji wa L2)) ni mbinu ya upimishaji katika uamuzi wenye vigezo vingi (MCDM) iliyoanzishwa na Hwang, C. L., Yoon, K. mwaka 1981. Huleta matriks ya uamuzi wa mbadala zilizo na alama kwa vigezo vingi katika matokeo yaliyoandaliwa na yanayoweza kurudiwa.

Tumia kupitia DecisionMindHivi karibuniVideoHivi karibuniDownload slides

Soma mbinu kamili

Kwa wanachama pekee

Ingia kwa akaunti ya bure ili kusoma sehemu hii.

Ingia

Method map

The neighbourhood of related methods — select a node to explore.

Vyanzo

  1. Hwang, C. L., Yoon, K. (1981). Multiple Attribute Decision Making: Methods and Applications. Lecture Notes in Economics and Mathematical Systems, Vol. 186, Springer-Verlag DOI: 10.1007/978-3-642-48318-9

Jinsi ya kunukuu ukurasa huu

ScholarGate. (2026, June 2). Vector Normalization — Euclidean column-norm scaling (L2 normalisation). ScholarGate. https://scholargate.app/sw/decision-making/norm-vector

Which method?

Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.

Compare side by side
ScholarGateNORM-VECTOR (Vector Normalization — Euclidean column-norm scaling (L2 normalisation)). Imepatikana 2026-06-15 kutoka https://scholargate.app/sw/decision-making/norm-vector · Seti ya data: https://doi.org/10.5281/zenodo.20539026