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 charactdhnneristics for solving optimization problems (Guo, et al., 2022).
DHNN is a simple structure with no hidden layer and binary input/output whicdhnnh leads to lower complexity compared to other networks. Satisfiability as a neuron representation is considered a suitable logical structure due to its simple construction.
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.
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