A review of chaotic systems based on memristive Hopfield neural networks
Since the Lorenz chaotic system was discovered in 1963, the construction of chaotic
systems with complex dynamics has been a research hotspot in the field of chaos. Recently …
systems with complex dynamics has been a research hotspot in the field of chaos. Recently …
Weighted random k satisfiability for k= 1, 2 (r2SAT) in discrete Hopfield neural network
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 …
to avoid production of repetitive final neuron states which improves the quality of global …
Supervised learning perspective in logic mining
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 …
Without optimal data structure, intelligence systems integrated into logic mining, such as an …
A modified reverse-based analysis logic mining model with Weighted Random 2 Satisfiability logic in Discrete Hopfield Neural Network and multi-objective training of …
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 …
most logic mining models disregarded any efforts in expanding the search space which led …
YRAN2SAT: A novel flexible random satisfiability logical rule in discrete hopfield neural network
The current development of the satisfiability logical representation in Discrete Hopfield
Neural Network has two prominent perspectives which are systematic and non-systematic …
Neural Network has two prominent perspectives which are systematic and non-systematic …
PRO2SAT: Systematic probabilistic satisfiability logic in discrete hopfield neural network
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 …
provides an alternative to represent the knowledge of any datasets. Fueled by this nature …
Non-systematic weighted satisfiability in discrete hopfield neural network using binary artificial bee colony optimization
SS Muhammad Sidik, NE Zamri… - Mathematics, 2022 - mdpi.com
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 …
Discrete Hopfield Neural Network. This new variant of satisfiability logical rule will provide …
Multi-discrete genetic algorithm in hopfield neural network with weighted random k satisfiability
Abstract The existing Discrete Hopfield Neural Network with systematic Satisfiability models
produced repetition of final neuron states which promotes to overfitting global minima …
produced repetition of final neuron states which promotes to overfitting global minima …
[HTML][HTML] Multi-unit Discrete Hopfield Neural Network for higher order supervised learning through logic mining: Optimal performance design and attribute selection
In the perspective of logic mining, the attribute selection, and the objective function of the
best logic is the two main factors that identifies the effectiveness of our proposed logic …
best logic is the two main factors that identifies the effectiveness of our proposed logic …
Dual optimization approach in discrete Hopfield neural network
Having effective learning and retrieval phases of satisfiability logic in Discrete Hopfield
Neural Network models ensures optimal synaptic weight management, which consequently …
Neural Network models ensures optimal synaptic weight management, which consequently …