Enhanced multidimensional neo-fuzzy classification system and its learning for the video classification task

Bodyanskiy, Ye. V.; Chala, O. S.

Публікація:
Enhanced multidimensional neo-fuzzy classification system and its learning for the video classification task

dc.contributor.author	Bodyanskiy, Ye. V.
dc.contributor.author	Chala, O. S.
dc.date.accessioned	2025-06-15T19:16:52Z
dc.date.available	2025-06-15T19:16:52Z
dc.date.issued	2024
dc.description.abstract	A novel hybrid neo-fuzzy system for video classification, which includes multidimensional neo-fuzzy components with adjustable synaptic weights and kernel membership functions, is proposed. This system combines the strengths of extended neo-fuzzy neurons (ENFN) and neo-fuzzy units (NFU) with nonlinear activation functions. By integrating extended nonlinear synapses (ENS) and leveraging the neuro-fuzzy Takagi-Sugeno-Kang inference system, proposed architecture enhances the approximating capabilities of traditional models. This allows the system to effectively address the task of image recognition, including real-time video stream classification, while maintaining a high level of accuracy, as demonstrated by computational experiment.
dc.identifier.citation	Bodyanskiy Ye. V. Enhanced multidimensional neo-fuzzy classification system and its learning for the video classification task / Ye. V. Bodyanskiy, O. S. Chala // АСУ та прилади автоматики : всеукр. міжвід. наук.-техн. зб. – Харків : ХНУРЕ, 2024. – Вип. 181. – С. 42–50. – DOI: 10.30837/0135-1710.2024.181.042.
dc.identifier.doi	https://doi.org/10.30837/0135-1710.2024.181.042
dc.identifier.uri	https://openarchive.nure.ua/handle/document/31627
dc.language.iso	en_US
dc.publisher	ХНУРЕ
dc.subject	video classification
dc.subject	neo-fuzzy components
dc.title	Enhanced multidimensional neo-fuzzy classification system and its learning for the video classification task
dc.type	Article
dspace.entity.type	Publication