Special major 2 satisfiability logic in discrete Hopfield neural network.

The satisfiability problem (SAT) of propositional logic formulas is of great significance in computer science and artificial intelligence. It is the primary NP-complete problem that has been proved and is broadly introduced into intelligent systems. Many significant achievements have been made in the direction of integrating SAT problem and Hopfield network to solve optimal problems. However, it remains unexplored for multiple aspects, such as knowledge representation, knowledge reasoning and model construction, and there is still much space for research. Therefore, this study proposed the AM(A-Major) 2SAT and Hopfield neural network fusion model, in which each logical formula is required to have second-order clauses and third-order clauses. The number of second-order clauses is greater than that of third-order clauses, and at least one positive literal is set in each clause. We used two performance metrics to compare the behavior of this model with existing systematic and non-systematic logical structure. The results showed that this model was superior to the other five models. The main contribution of this research was to study the knowledge representation, knowledge reasoning and model construction of MAJ2SAT propositional logic in Hopfield neural network from a new perspective, and to further explore the fusion structure of propositional logic and Hopfield neural network. [ABSTRACT FROM AUTHOR]