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Crossbar Switch Problem Solver by Hysteresis Neural Networks


Wei Zhang, Zheng Tang


Vol. 10  No. 1  pp. 185-191


In this paper, we propose a continuous hysteresis neurons (CHN) Hopfield neural network architecture for efficiently solving crossbar switch problems. A Hopfield neural network architecture with continuous hysteresis and its collective computational properties are studied. It is proved theoretically and confirmed by simulating the randomly generated Hopfield neural network with CHN. The network architecture is applied to a crossbar switch problem and results of computer simulations are presented and used to illustrate the computation power of the network architecture. The simulation results show that the Hopfield neural network architecture with CHN is much better than the binary hysteresis Hopfield neural network architecture for crossbar switch problem in terms of both the computation time and the solution quality.


Network architecture, crossbar switch problem, continuous hysteresis, Hopfield neural network