Publication: Conditional Random 2 Satisfiability In Discrete Hopfield Neural Networks For Enhanced Logic Mining
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Date
2025-06
Authors
Roslan, Nurshazneem
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Abstract
A deeper understanding of additional mechanisms in discrete hopfield neural network is essential for developing intelligent model for broader applications. This thesis introduces conditional random 2 satisfiability, a new logical rule guaranteeing the inclusion of at least one negative logic operator for each second-order clause which implements the non-monotonic smish activation function to enhance the updating process within the network. Furthermore, multi-objective function optimization using a hybrid binary whale optimization algorithm is employed during the retrieval phase to generate diversified neuron states with maximum global solutions and minimized similarity indices. Finally, a new approach for identifying the best logic based on various performance metrics in the logic mining model called conditional random 2 satisfiability reverse analysis is proposed. This approach is significant when dealing with imbalanced datasets leading to an enhanced search space for finding optimal induced logic.
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Keywords
Conditional Random Satisfiability Discrete Hopfield Neural