Weighted random 3-satisfiability in discrete Hopfield neural network
Satisfiability (SAT) is remarkable in the field of computational mathematics because it can be utilized to represent the information of any categories of any datasets. Recent research about this paradigm has tended to model Discrete Hopfield Neural Network (DHNN) via SAT. Despite the widespread implementations of SAT in DHNN, there are limitations to the control of the distribution of negative and positive literals in the logical rule and this aspect has been rarely discussed. In this paper, a novel logic rule named weighted 3 satisfiability is proposed by implementi