The main highlight of DHNN is the present of content associative memory (CAM) whereby it is used to store the pattern of the problem and it consists of interconnected neurons with no hidden layer which act as the potential characteristics for solving optimization problems (Guo, et al., 2022).
In this paper, we proposed an improved discrete Hopfield neural network (DHNN) for Max-Cut problems. By dhnnintroducing 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.
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