Publication: J-Type Random 2,3 Satisfiability Reverse Analysis Topological-Based Method In Discrete Hopfield Neural Network
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Date
2025-07
Authors
Jiang, Xiaofeng
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Abstract
The development of satisfiability logical representation in Discrete Hopfield NeuralNetworks has evolved into a more flexible structure, allowing for both systematic and non-systematic logic. However, the main issue with the exiting flexible satisfiability representation is the appearance of first order clauses which will degrade the quality of the logical rule as a neuron representation and leads to overfitting issue. Therefore, this
thesis proposes a hybrid higher order satisfiability logical rule named J-Type Random 2,3 Satisfiability. The proposed logical rule randomly generates structures based on either second order, third order, or a combination of both clauses. The behavior of the proposed logical rule will be evaluated using various performance metrics in terms of training error, retrieval error, energy analysis, and similarity analysis. Simulation
results indicate that the proposed model achieves the highest global minimum ratio with an average value of 0.4674. The newly proposed model will be incorporated into the logic mining model known as topology based J-type random 2,3 satisfiability reverse analysis. The unsupervised attribute selection method called topological data analysis is employed in selecting most significant attributes. Meanwhile, permutation operation
improves the ability of the logic mining model to retrieve the best induced logic that can extract patterns from the dataset. The proposed logic mining model demonstrates superior performance compared with existing state-of-the-art logic mining models with an average accuracy of 0.8375 in the selected various real-life datasets.
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Keywords
J-Type Random 2 , 3 Satisfiability Reverse Analysis Topological-Based Method , Discrete Hopfield Neural Network