Takagi–Sugeno–Kang Fuzzy Neural Network for Nonlinear Chaotic Systems and Its Utilization in Secure Medical Image Encryption DOI Creative Commons
Duc-Hung Pham, Mai The Vu

Mathematics, Год журнала: 2025, Номер 13(6), С. 923 - 923

Опубликована: Март 11, 2025

This study introduces a novel control framework based on the Takagi–Sugeno–Kang wavelet fuzzy neural network, integrating brain imitated network and cerebellar network. The proposed controller demonstrates high robustness, making it an excellent candidate for handling intricate nonlinear dynamics, effectively mapping input–output relationships efficiently learning from data. To enhance its performance, controller’s parameters are fine-tuned using Lyapunov stability theory. Compared to existing approaches, model exhibits superior capabilities achieves outstanding performance metrics. Furthermore, applies this synchronization technique secure transmission of medical images. By encrypting image into chaotic trajectory before transmission, system ensures data security. On receiving end, original is successfully reconstructed synchronization. Experimental results confirm effectiveness reliability model, as well encryption decryption process. Specifically, average_RMSE cerebral (TFWBCC) method 2.004 times smaller than articulation (CMAC) method, 1.923 RCMAC 1.8829 TSKCMAC 1.8153 emotional (BELC) method.

Язык: Английский

Takagi–Sugeno–Kang Fuzzy Neural Network for Nonlinear Chaotic Systems and Its Utilization in Secure Medical Image Encryption DOI Creative Commons
Duc-Hung Pham, Mai The Vu

Mathematics, Год журнала: 2025, Номер 13(6), С. 923 - 923

Опубликована: Март 11, 2025

This study introduces a novel control framework based on the Takagi–Sugeno–Kang wavelet fuzzy neural network, integrating brain imitated network and cerebellar network. The proposed controller demonstrates high robustness, making it an excellent candidate for handling intricate nonlinear dynamics, effectively mapping input–output relationships efficiently learning from data. To enhance its performance, controller’s parameters are fine-tuned using Lyapunov stability theory. Compared to existing approaches, model exhibits superior capabilities achieves outstanding performance metrics. Furthermore, applies this synchronization technique secure transmission of medical images. By encrypting image into chaotic trajectory before transmission, system ensures data security. On receiving end, original is successfully reconstructed synchronization. Experimental results confirm effectiveness reliability model, as well encryption decryption process. Specifically, average_RMSE cerebral (TFWBCC) method 2.004 times smaller than articulation (CMAC) method, 1.923 RCMAC 1.8829 TSKCMAC 1.8153 emotional (BELC) method.

Язык: Английский

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