Flexible satisfiability in discrete Hopfield neural network.

Satisfiability (SAT) is made up of systematic logic. The main problems of systematic logic are the inflexibility of the logic structure. Therefore, a more flexible random logical structure was proposed. In this article, we discussed the behavior of Y-Type Random 2-Satisfiability namely YRAN2SAT. YRAN2SAT is a combination of systematic logic and non-systematic logic. We compared the behavior of YRAN2SAT with the other two models (RANkSAT) and first-order logic) using 2 performance metrics. The results showed that YRAN2SAT was superior to the other two models in terms of the learning error and global minimum solutions. YRAN2SAT can be used as the symbol instruction of DHNN by obtaining the optimal synaptic weights. [ABSTRACT FROM AUTHOR]