Stability (learning theory)

Adaptive neuro fuzzy inference system

Neuro-fuzzy

Mathematics

Machine learning

Artificial intelligence

Fuzzy control system

Control (management)

Fuzzy rule

Computer science

Control theory (sociology)

Artificial neural network

Fuzzy logic

IEEE Transactions on Fuzzy Systems

This article focuses on the design of a fuzzy adaptive event-triggered sampled-data control (AETSDC) scheme for stabilization of Takagi-Sugeno (T-S) fuzzy memristive neural networks (MNNs) with reaction-diffusion terms (RDTs). Different from the existing T-S fuzzy MNNs, the reaction and diffusion phenomena are considered, which make the presented model more applicable. A fuzzy AETSDC scheme is proposed for the first time, in which different AETSDC mechanisms will be applied for different fuzzy rules. For each fuzzy rule, the corresponding AETSDC mechanism can be promptly adaptively adjusted based on the current and last sampled signals. So the fuzzy AETSDC scheme can effectively save the limited communication resources for the considered system. By introducing a suitable Lyapunov- Krasovskii functional, new stability and stabilization criteria are established for T-S fuzzy MNNs with RDTs. Meanwhile, the desired fuzzy AETSDC gains are obtained. Finally, simulation results are given to verify the superiority of the fuzzy AETSDC scheme and the effectiveness of the theoretical results.

Fuzzy Adaptive Event-Triggered Sampled-Data Control for Stabilization of T–S Fuzzy Memristive Neural Networks With Reaction–Diffusion Terms