A New Algorithm for Crossbar Switch Problem Using Hopfield Neural Network with Continuous Hysteresis Neurons

In this paper, we propose a continuous hysteresis neurons 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 continuous hysteresis neurons. 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 continuous hysteresis neurons is much better than the previous works including the original Hopfield neural network architecture, maximum neural network and Hopfield neural network with hysteresis binary neurons for crossbar switch problem in terms of both the computation time and the solution quality.