| Publisher | Fukui University | ||
|---|---|---|---|
| Format | 1.0MB PDF | Date added | 01 Dec 2006 |
| Topics | Switching, Network Design | ||
| Downloads | 27 | ||
This paper proposes 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.
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