A conventional systematic satisfiability logic suffers from a nonflexible logical structure that leads to a lack of interpretation. To resolve this problem, the advantage of introducing nonsystematic satisfiability logic is important to improve the flexibility of the logical structure. This paper proposes Random 3 Satisfiability (RAN3SAT) with three types of logical combinations ($k = 1, 3, k =2, 3$, and$k =1$, 2, 3) to report the behaviors of multiple logical structures. The different types ofRAN3SATenforced with Discrete Hopfield Neural Network (DHNN) are included with benchmark searching techniques, such as Exhaustive Search algorithm. Additionally, to strengthen and certify the behavior of the proposed model, we extensively conducted several performance evaluation metrics with a specific number of neurons. In particular, the experimental results revealed thatRAN3SATwas able to be implemented in DHNN, and each logical combination has its characteristics. Nonetheless,RAN3SATprovides more neuron variations in the whole solution space. The proposed model can also be applied in real-world applications such as the logic mining approach sinceRAN3SATconsists of various logic combinations that behave as input language to transform raw data into informative output.
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Karim et al. (2021) studied this question.