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.
DHNN™ exists to transform our surroundings and ourselves through design, empathy and a restless quest for quality.
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 anddhnn therefore get better solutions.
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