Публікація: Enhanced multidimensional neo-fuzzy classification system and its learning for the video classification task
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Дата
2024
Автори
Назва журналу
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Видавництво
ХНУРЕ
Анотація
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.
Опис
Ключові слова
video classification, neo-fuzzy components
Бібліографічний опис
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.