Permitted and forbidden sets in discrete-time linear threshold recurrent neural networks
IEEE Transactions on Neural Networks
Multistability and new attraction basins of almost-periodic solutions of delayed neural networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Practical approach to programmable analog circuits with memristors
IEEE Transactions on Circuits and Systems Part I: Regular Papers
IEEE Transactions on Neural Networks
Communications of the ACM
Fractional-order memristor-based Chua's circuit
IEEE Transactions on Circuits and Systems II: Express Briefs
Synchronization control of a class of memristor-based recurrent neural networks
Information Sciences: an International Journal
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
GenSoFNN: a generic self-organizing fuzzy neural network
IEEE Transactions on Neural Networks
A CMOS feedforward neural-network chip with on-chip parallel learning for oscillation cancellation
IEEE Transactions on Neural Networks
Encoding strategy for maximum noise tolerance bidirectional associative memory
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
On Attracting Basins of Multiple Equilibria of a Class of Cellular Neural Networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Self-Controlled Writing and Erasing in a Memristor Crossbar Memory
IEEE Transactions on Nanotechnology
Resistive Programmable Through-Silicon Vias for Reconfigurable 3-D Fabrics
IEEE Transactions on Nanotechnology
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The paper introduces a general class of memristor-based recurrent neural networks with time-varying delays. Conditions on the nondivergence and global attractivity are established by using local inhibition, respectively. Moreover, exponential convergence of the networks is studied by using local invariant sets. The analysis in the paper employs results from the theory of differential equations with discontinuous right-hand sides as introduced by Filippov. The obtained results extend some previous works on conventional recurrent neural networks.