Ilim ha’m ja’miyet № 4-1 (2025) · с. 20-22
ЎЗ-ЎЗИНИ ТАШКИЛ ЭТУВЧИ КОХОНЕН ХАРИТАЛАРИНИНГ НАЗАРИЙ АСОСЛАРИ ВА АМАЛИЁТДАГИ ҚЎЛЛАНИЛИШИ
Қудайбергенов, А.А., Бекмуродова, Р.С.
Аннотация
This article presents the theoretical foundations and the learning algorithm of self-organizing Kohonen maps through mathematical models. Kohonen maps are considered an effective neural network model for unsupervised clustering and visualization of data. The paper provides a detailed description of the method's architecture, the process of finding the Best Matching Unit (BMU), the neighborhood model, and the adaptive weight updating process. Additionally, visualization techniques – the Unified Distance Matrix (U-Matrix) and component projections – are discussed. Practical applications of the method in medicine, geospatial data, and agriculture are analyzed, highlighting its importance as a tool for understanding data structures and supporting decision-making.
Кохонен хариталаринейрон тармоқкластерлашназоратсиз ўрганишвизуализациямаълумот таҳлилиўлчам пасайтиришкарта Кохоненанейронная сетькластеризация
Источник метаданных: OAI-PMH архив журнала · Sindex не хранит полный текст, а даёт ссылку на источник.