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 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.

Amazon Employees Resources Access Data Extraction via Clonal Selection Algorithm and Logic Mining Approach

“…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 ].…”

“…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.…”

“…The structure of DHNN consists of input and output neurons that store bipolar or binary pattern [ 18 ]. In addition, DHNN utilizes the Lyapunov energy function to determine degree of convergence of the solution [ 19 ]. This paper incorporates the Wan Abdullah (WA) method of finding the synaptic weights by comparing the Lyapunov energy function with the cost function [ 20 ].…”

Systematic Boolean Satisfiability Programming in Radial Basis Function Neural Network

“…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]:…”

“…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.…”