2019
DOI: 10.3390/math7111133
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Discrete Mutation Hopfield Neural Network in Propositional Satisfiability

Abstract: 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 conf… Show more

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Cited by 47 publications
(48 citation statements)
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References 70 publications
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“…Hence, we can further agree that generally DHNN3-SATCSA is a better model in terms of and the capability of its mechanism to employ different sizes and natures of real-life data sets. We can further improve the retrieval property of DHNN-3SATCSA by implementing a mutation operator such as in [ 19 ].…”
Section: Resultsmentioning
confidence: 99%
See 2 more Smart Citations
“…Hence, we can further agree that generally DHNN3-SATCSA is a better model in terms of and the capability of its mechanism to employ different sizes and natures of real-life data sets. We can further improve the retrieval property of DHNN-3SATCSA by implementing a mutation operator such as in [ 19 ].…”
Section: Resultsmentioning
confidence: 99%
“…The simulated data set will be initiated by generating randomized clauses and literals for each . A similar approach has been implemented in several studies such as [ 19 , 60 ] in generating the initial neuron states. It is worth mentioning that all simulations will be measured against existing methods by evaluating appropriate performance evaluation metrics.…”
Section: Methodsmentioning
confidence: 99%
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“…The three components of 2SAT logic are as follows: Each of the variable in 2SAT takes binary value {0, 1} which exemplified the idea of false and true. The goal of 2SAT programming is to optimize the parameters in ANN [30]. The general formula for 2SAT logic is as follows [29]:…”
Section: Satisfiability Representationmentioning
confidence: 99%
“…Note that, the value of the global minimum energy, H min P 2SAT can be pre-calculated because the magnitude of the energy for each clause is always constant [30,36]. Hence, the optimized neuron state can be obtained by examining H min P 2SAT − H P 2SAT ≤ ∂ where ∂ is pre-defined by the user.…”
Section: Satisfiability Programming In Hopfield Neural Networkmentioning
confidence: 99%