Existing satisfiability (SAT) is composed of a systematic logical structure with definite literals in a set of clauses. The key problem of the existing SAT is the lack of interpretability of a logical structure that leads to low variability of the retrieved neuron states. Thus, a new non-systematic SAT with higher interpretability is needed to reduce the repetition of the patterns of the final neuron states. This paper presents Major 2 Satisfiability (MAJ2SAT) by emphasizing a ratio of 2 Satisfiability (2SAT) clauses present in non-systematic SAT. Hence, different compositions of MAJ2SAT are implemented in a Discrete Hopfield Neural Network (DHNN) by adopting an Exhaustive Search as a training algorithm. Various performance metrics are utilized to measure the compatibility and behaviour of MAJ2SAT in DHNN. Overall, the formulation of MAJ2SAT offers an alternative logical structure in the field of data mining that involves a more dynamic composition of literals and clauses.

现有可满足性 (SAT) 由系统的逻辑结构组成，在一组子句中有明确的文字。现有 SAT 的关键问题是缺乏逻辑结构的可解释性，导致检索到的神经元状态的可变性低。因此，需要一种具有更高可解释性的新非系统 SAT 来减少最终神经元状态模式的重复。本文通过强调非系统 SAT 中存在的 2 个可满足性 (2SAT) 子句的比率来介绍主要 2 可满足性 (MAJ2SAT)。因此，通过采用穷举搜索作为训练算法，在离散 Hopfield 神经网络 (DHNN) 中实现了 MAJ2SAT 的不同组合。各种性能指标被用来衡量 MAJ2SAT 在 DHNN 中的兼容性和行为。总体，