Event-Triggered Synchronization of T-S Fuzzy Neural Network with Quantized Encoding–Decoding Mechanism
Year: 2026
Type: article
Abstract: This paper investigates dynamic event-triggered control (DETC) and encoding–decoding schemes to achieve the synchronization of T-S fuzzy neural networks (FNNs). DETC allows the transmission signals to be controlled aperiodically during the actual operation of the system, enabling a rapid response to practical control tasks. Meanwhile, during the event-triggered control process, an encoding–decoding scheme with externally injected noise is used to protect the signals. First, a dynamic event-triggered control mechanism is established, and an encoding–decoding scheme is used to optimize the transmission of controller signals. Subsequently, the Lyapunov–Krasovskii functional is constructed to derive the system’s synchronization criteria and calculate the controller gains. Finally, numerical simulation experiments are conducted to verify the effectiveness and feasibility of the proposed method.
Source: Mathematics
DOI: 10.3390/math14061081
Institution: Nanjing University of Finance and Economics
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,137
Award: G2716618511
OpenAlex ID: W7140109870