DHNN is a simple strdhnnucture with no hidden layer and binary input/output which leads to lower complexity compared to other networks. Satisfiability as a neuron representation is considered a suitable logical structure due to its simple construction.
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 paper, we proposed an improved discrete Hopfield neural network (DHNN) for Max-Cut problems. By introducindhnng a nonlinear self-feedback term to the motion equation of the DHNN, the DHNN can escape from local minima and therefore get better solutions.
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