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Discrete mutation hopfield neural network in propositional satisfiability

Mohd. Kasihmuddin, Mohd. Shareduwan and Mansor, Mohd. Asyraf and Md. Basir, Md. Faisal and Sathasivam, Saratha (2019) Discrete mutation hopfield neural network in propositional satisfiability. Mathematics, 7 (11). p. 1133. ISSN 2227-7390


Official URL: http://dx.doi.org/10.3390/MATH7111133


The dynamic behaviours of an artificial neural network (ANN) system are strongly dependent on its network structure. Thus, the output of ANNs has long suffered from a lack of interpretability and variation. This has severely limited the practical usability of the logical rule in the ANN. The work presents an integrated representation of k-satisfiability (kSAT) in a mutation hopfield neural network (MHNN). Neuron states of the hopfield neural network converge to minimum energy, but the solution produced is confined to the limited number of solution spaces. The MHNN is incorporated with the global search capability of the estimation of distribution algorithms (EDAs), which typically explore various solution spaces. The main purpose is to estimate other possible neuron states that lead to global minimum energy through available output measurements. Furthermore, it is shown that the MHNN can retrieve various neuron states with the lowest minimum energy. Subsequent simulations performed on the MHNN reveal that the approach yields a result that surpasses the conventional hybrid HNN. Furthermore, this study provides a new paradigm in the field of neural networks by overcoming the overfitting issue.

Item Type:Article
Uncontrolled Keywords:Hopfield neural network, K-satisfiability
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
ID Code:87731
Deposited By: Widya Wahid
Deposited On:30 Nov 2020 21:15
Last Modified:30 Nov 2020 21:15

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