Bodyanskiy, Ye. V.Chala, O. S.2025-06-152025-06-152024Bodyanskiy 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.https://openarchive.nure.ua/handle/document/31627A 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.en-USvideo classificationneo-fuzzy componentsArticlehttps://doi.org/10.30837/0135-1710.2024.181.042