Current studies on non-systematic satisfiability in Discrete Hopfield Neural Network are able
to avoid production of repetitive final neuron states which improves the quality of global …

Creating optimal logic mining is strongly dependent on how the learning data are structured.
Without optimal data structure, intelligence systems integrated into logic mining, such as an …

Over the years, the study on logic mining approach has increased exponentially. However,
most logic mining models disregarded any efforts in expanding the search space which led …

The current development of the satisfiability logical representation in Discrete Hopfield
Neural Network has two prominent perspectives which are systematic and non-systematic …

Satisfiability is prominent in the field of computer science and mathematics because SAT
provides an alternative to represent the knowledge of any datasets. Fueled by this nature …

Recently, new variants of non-systematic satisfiability logic were proposed to govern
Discrete Hopfield Neural Network. This new variant of satisfiability logical rule will provide …

Abstract The existing Discrete Hopfield Neural Network with systematic Satisfiability models
produced repetition of final neuron states which promotes to overfitting global minima …

Having effective learning and retrieval phases of satisfiability logic in Discrete Hopfield
Neural Network models ensures optimal synaptic weight management, which consequently …

One of the main problems in representing information in the form of nonsystematic logic is
the lack of flexibility, which leads to potential overfitting. Although nonsystematic logic …

This study introduced a novel ant colony optimization algorithm that implements the
population selection strategy of the Estimation of Distribution Algorithm and a new …