In this paper, we proposed an improved discrete Hopfield neural network (DHNN) for Max-Cut problems. By introducing a nonlinear self-feedback term to the motion equation of the DHNN, the DHNN can escape from local minima and therefore get better solutions.
DHNN is a minimalistic and Numpy based implementation of the Discrete Hopfield Network. DHNN can learn (memorize) patterns and remember (recover) the patterns when the network feeds those with noises.
In this study, we investigate and implement a discrete Hopfield neural network (DHNN) using SFQ circuits. A DHNN is a binary neural network with less information than a standard full precision neural network; it also providedhnns a higher processing speed.
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