On global asymptotic stability of recurrent neural networks with time-varying delays
Applied Mathematics and Computation
Harmless delays for global exponential stability of Cohen-Grossberg neural networks
Mathematics and Computers in Simulation
Control synthesis of continuous-time T-S fuzzy systems with local nonlinear models
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Robust output regulation of T-S fuzzy systems with multiple time-varying state and input delays
IEEE Transactions on Fuzzy Systems
New results for robust stability of dynamical neural networks with discrete time delays
Expert Systems with Applications: An International Journal
T-S model based indirect adaptive fuzzy control using online parameter estimation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Global Synchronization in an Array of Delayed Neural Networks With Hybrid Coupling
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A neuromorphic VLSI device for implementing 2D selective attention systems
IEEE Transactions on Neural Networks
Temporal coding in a silicon network of integrate-and-fire neurons
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Fuzzy Modeling and Synchronization of Two Totally Different Chaotic Systems via Novel Fuzzy Model
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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This paper deals with the problem of global exponential synchronization of a class of memristor-based recurrent neural networks with time-varying delays based on the fuzzy theory and Lyapunov method. First, a memristor-based recurrent neural network is designed. Then, considering the state-dependent properties of the memristor, a new fuzzy model employing parallel distributed compensation (PDC) gives a new way to analyze the complicated memristor-based neural networks with only two subsystems. Comparisons between results in this paper and in the previous ones have been made. They show that the results in this paper improve and generalized the results derived in the previous literature. An example is also given to illustrate the effectiveness of the results.