Journal
IEEE TRANSACTIONS ON FUZZY SYSTEMS
Volume 31, Issue 6, Pages 1953-1965Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TFUZZ.2022.3216777
Keywords
Synchronization; Delays; Memristors; Biological neural networks; Fuzzy logic; Adaptive control; Synapses; distributed delays; exponential synchronization; memristive neural networks (MNNs); Takagi-Sugeno (T-S) fuzzy systems
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This article addresses the exponential synchronization problem for a class of fuzzy inertial memsirtor-based neural networks with mixed time-varying delays. First, the inertial items are described as second-order systems and transformed into first-order systems by utilizing a appropriate variable substitution. Then, the fuzzy state-feedback control strategy and fuzzy adaptive control strategy are designed to ensure the exponential synchronization under the framework of Filippov solutions. The exponential synchronization algebraic conditions are obtained by choosing a proper Lyapunov-Krasovskii functional. Finally, two numerical simulations are provided to validate the effectiveness and benefit of the proposed results.
This article addresses the exponential synchronization problem for a class of fuzzy inertial memsirtor-based neural networks with mixed time-varying delays. First, the inertial items are described as second-order systems and transformed into first-order systems by utilizing a appropriate variable substitution. Then, the fuzzy state-feedback control strategy and fuzzy adaptive control strategy are designed to ensure the exponential synchronization under the framework of Filippov solutions. The exponential synchronization algebraic conditions are obtained by choosing a proper Lyapunov-Krasovskii functional. Finally, two numerical simulations are provided to validate the effectiveness and benefit of the proposed results.
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