This paper is concerned with the exponential synchronization problem of coupled memristive neural networks. In contrast to general neural networks, memristive neural networks exhibit state-dependent switching behaviors due to the physical properties of memristors. Under a mild topology condition, it is proved that a small fraction of controlled sub- systems can efficiently synchronize the coupled systems. The pinned subsystems are identified via a search algorithm. Moreover, the information exchange network needs not to be undirected or strongly connected. Finally, two numerical simulations are performed to verify the usefulness and effectiveness of our results.
Based on the classical composite laminate theory,the bending problem of a finite composite plate weakened by multiple elliptical holes is studied by means of the complex variable method.The present work is intended to express the complex potentials in the form of Faber series aided by the use of the least squares boundary collocation techniques on the finite boundaries.As a result,concise and high accuracy solutions are presented for the stress distribution around the holes.Finally,numerical examples are presented to discuss the effects of some parameters on the stress concentration around the holes.
Recently, it has been demonstrated that memristors can be utilized as logic operations and memory elements. In this paper, we present a novel circuit design for complementary resistive switch（CRS）-based stateful logic operations. The proposed circuit can automatically write the destructive CRS cells back to the original states. In addition, the circuit can be used in massive passive crossbar arrays which can reduce sneak path current greatly. Moreover, the steps for CRS logic operations using our proposed circuit are reduced compared with previous circuit designs. We validate the effectiveness of our scheme through Hspice simulations on the logic circuits.
