Hybridised Network of Fuzzy Logic and a Genetic Algorithm in Solving 3-Satisfiability Hopfield Neural Networks

Azizan, Farah Liyana; Sathasivam, Saratha; Ali, Majid Khan Majahar; Roslan, Nurshazneem; Feng, Caicai

doi:10.3390/axioms12030250

Cited by 7 publications

(2 citation statements)

References 34 publications

(55 reference statements)

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“…where j is the corresponding attribute (from 1 to n), and i represents the number of instances used in the design set. The minimum and maximum ranges of the fuzzy input are calculated by Equations ( 21) and (22).…”

Section: Application To Medical Classificationmentioning

confidence: 99%

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Interval Type-3 Fuzzy Inference System Design for Medical Classification Using Genetic Algorithms

Melin,

Sánchez,

Castillo

2023

Axioms

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An essential aspect of healthcare is receiving an appropriate and opportune disease diagnosis. In recent years, there has been enormous progress in combining artificial intelligence to help professionals perform these tasks. The design of interval Type-3 fuzzy inference systems (IT3FIS) for medical classification is proposed in this work. This work proposed a genetic algorithm (GA) for the IT3FIS design where the fuzzy inputs correspond to attributes relational to a particular disease. This optimization allows us to find some main fuzzy inference systems (FIS) parameters, such as membership function (MF) parameters and the fuzzy if-then rules. As a comparison against the proposed method, the results achieved in this work are compared with Type-1 fuzzy inference systems (T1FIS), Interval Type-2 fuzzy inference systems (IT2FIS), and General Type-2 fuzzy inference systems (GT2FIS) using medical datasets such as Haberman’s Survival, Cryotherapy, Immunotherapy, PIMA Indian Diabetes, Indian Liver, and Breast Cancer Coimbra dataset, which achieved 75.30, 87.13, 82.04, 77.76, 71.86, and 71.06, respectively. Also, cross-validation tests were performed. Instances established as design sets are used to design the fuzzy inference systems, the optimization technique seeks to reduce the classification error using this set, and finally, the testing set allows the validation of the real performance of the FIS.

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Section: Application To Medical Classificationmentioning

confidence: 99%

“…The parameters of the FIS are optimized using a Firefly Algorithm [7]. Although there are currently many techniques for performing optimizations, GA is one of the first methods used to search for parameters and architectures, which continues to be an excellent tool for obtaining optimized parameters related to FL [20][21][22][23]. In Ref.…”

Section: Introductionmentioning

confidence: 99%

Interval Type-3 Fuzzy Inference System Design for Medical Classification Using Genetic Algorithms

Melin,

Sánchez,

Castillo

2023

Axioms

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A novel hybrid technique using fuzzy logic, neural networks and genetic algorithm for intrusion detection system

Ishaque,

Johar,

Khatibi

et al. 2023

Measurement: Sensors

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Modeling of 3 SAT discrete Hopfield neural network optimization using genetic algorithm optimized K-modes clustering

Xie,

Sathasivam,

2024

MATH

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<p>The discrete Hopfield neural network 3-satisfiability (DHNN-3SAT) model represents an innovative application of deep learning techniques to the Boolean SAT problem. Existing research indicated that the DHNN-3SAT model demonstrated significant advantages in handling 3SAT problem instances of varying scales and complexities. Compared to traditional heuristic algorithms, this model converged to local minima more rapidly and exhibited enhanced exploration capabilities within the global search space. However, the model faced several challenges and limitations. As constraints in SAT problems dynamically increased, decreased, or changed, and as problem scales expanded, the model's computational complexity and storage requirements may increase dramatically, leading to reduced performance in handling large-scale SAT problems. To address these challenges, this paper first introduced a method for designing network synaptic weights based on fundamental logical clauses. This method effectively utilized the synaptic weight information from the original SAT problem within the DHNN network, thereby significantly reducing redundant computations. Concrete examples illustrated the design process of network synaptic weights when constraints were added, removed, or updated, offering new approaches for managing the evolving constraints in SAT problems. Subsequently, the paper presented a DHNN-3SAT model optimized by genetic algorithms combined with K-modes clustering. This model employed genetic algorithm-optimized K-modes clustering to effectively cluster the initial space, significantly reducing the search space. This approach minimized the likelihood of redundant searches and reduced the risk of getting trapped in local minima, thus improving search efficiency. Experimental tests on benchmark datasets showed that the proposed model outperformed traditional DHNN-3SAT models, DHNN-3SAT models combined with genetic algorithms, and DHNN-3SAT models combined with imperialist competitive algorithms across four evaluation metrics. This study not only broadened the application of DHNN in solving 3SAT problems but also provided valuable insights and guidance for future research.</p>

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Hybridised Network of Fuzzy Logic and a Genetic Algorithm in Solving 3-Satisfiability Hopfield Neural Networks

Cited by 7 publications

References 34 publications

Interval Type-3 Fuzzy Inference System Design for Medical Classification Using Genetic Algorithms

Interval Type-3 Fuzzy Inference System Design for Medical Classification Using Genetic Algorithms

A novel hybrid technique using fuzzy logic, neural networks and genetic algorithm for intrusion detection system

Modeling of 3 SAT discrete Hopfield neural network optimization using genetic algorithm optimized K-modes clustering

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