YRAN2SAT: A novel flexible random satisfiability logical rule in discrete hopfield neural network

Guo, Yueling; Kasihmuddin, Mohd Shareduwan Mohd; Gao, Yuan; Mansor, Mohd. Asyraf; Wahab, Habibah A.; Zamri, Nur Ezlin; Chen, Ju

doi:10.1016/j.advengsoft.2022.103169

Cited by 35 publications

(15 citation statements)

References 28 publications

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“…By adapting various forms of 2SAT logical structure during the learning phase of HNN, P2SATRA outperfomed the 2SATRA, E2SATRA and RA approaches when being measured with the performance metrics such as the accuracy, precision, sensitivity, F-Score and MCC after the logic mining analysis with 11 different real datasets. For instance, it will be interesting to infuse different logical rule such as Maximum Satisfiability [20], Y-Type Random Satisfiability [8], G-Type Random Satisfiability [9] and Random k Satisfiability [5]. In terms of network architecture, it will be interesting if other learning mechanism such as in [21,22] were embeded into logic mining.…”

Section: Resultsmentioning

confidence: 99%

“…This shows that the interpretation of the 2SAT logical rule in DHNN can be further optimized using optimization algorithm. The implementation of 2SAT in various network inspires the emergence of other useful logic such as [5][6][7][8][9] in doing DHNN. Various type of logical rule creates optimal modelling of DHNN that has wide range of behavior.…”

Section: Introductionmentioning

confidence: 99%

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Modified 2 Satisfiability Reverse Analysis Method via Logical Permutation燨perator

Jamaludin¹,

Mansor²,

Baharum³

et al. 2023

Computers, Materials &Amp; Continua

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The effectiveness of the logic mining approach is strongly correlated to the quality of the induced logical representation that represent the behaviour of the data. Specifically, the optimum induced logical representation indicates the capability of the logic mining approach in generalizing the real datasets of different variants and dimensions. The main issues with the logic extracted by the standard logic mining techniques are lack of interpretability and the weakness in terms of the structural and arrangement of the 2 Satisfiability logic causing lower accuracy. To address the issues, the logical permutation serves as an alternative mechanism that can enhance the probability of the 2 Satisfiability logical rule becoming true by utilizing the definitive finite arrangement of attributes. This work aims to examine and analyze the significant effect of logical permutation on the performance of data extraction ability of the logic mining approach incorporated with the recurrent discrete Hopfield Neural Network. Based on the theory, the effect of permutation and associate memories in recurrent Hopfield Neural Network will potentially improve the accuracy of the existing logic mining approach. To validate the impact of the logical permutation on the retrieval phase of the logic mining model, the proposed work is experimentally tested on a different class of the benchmark real datasets ranging from the multivariate and timeseries datasets. The experimental results show the significant improvement in the proposed logical permutation-based logic mining according to the domains such as compatibility, accuracy, and competitiveness as opposed to the plethora of standard 2 Satisfiability Reverse Analysis methods.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modified 2 Satisfiability Reverse Analysis Method via Logical Permutation燨perator

Jamaludin¹,

Mansor²,

Baharum³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…In this section, the performance of DHNN-RANMAX2SATES, DHNN-RANMAX2SATGA, and DHNN-RANMAX2SATEA will be examined by various performance metrics. To examine the actual performance of the network, these performance metrics have been used by several researchers in neural network studies [18][19][20][21]. The purpose of the program is to obtain the best training model of DHNN-RANMAX2SAT.…”

Section: Performance Metricesmentioning

confidence: 99%

“…The proposed r2SAT was reported to obtain final neuron state with high total neuron variation. In another development, Guo et al [20] combined the beneficial feature of both systematic and non-systematic logic by proposing Y-Type Random 2 Satisfiability (YRAN2SAT). The proposed logic shows an interesting behavior because YRAN2SAT can be reduced to both 2SAT and RAN2SAT.…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, Gao et al [21] extended the order of the clause in the logic by adding third order clause. Although the final energy for [20,21] tends to converge to local minimum energy (due to high number of neuron), both logics offer a wide range of flexibility to represent symbolic rule in HNN. Despite rapid development in the field of logic in HNN, none of the mentioned studies consider the existence of a redundant variable in the logical rule.…”

Section: Introductionmentioning

confidence: 99%

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Random Maximum 2 Satisfiability Logic in Discrete Hopfield Neural Network Incorporating Improved Election Algorithm

et al. 2022

Self Cite

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Real life logical rule is not always satisfiable in nature due to the redundant variable that represents the logical formulation. Thus, the intelligence system must be optimally governed to ensure the system can behave according to non-satisfiable structure that finds practical applications particularly in knowledge discovery tasks. In this paper, we a propose non-satisfiability logical rule that combines two sub-logical rules, namely Maximum 2 Satisfiability and Random 2 Satisfiability, that play a vital role in creating explainable artificial intelligence. Interestingly, the combination will result in the negative logical outcome where the cost function of the proposed logic is always more than zero. The proposed logical rule is implemented into Discrete Hopfield Neural Network by computing the cost function associated with each variable in Random 2 Satisfiability. Since the proposed logical rule is difficult to be optimized during training phase of DHNN, Election Algorithm is implemented to find consistent interpretation that minimizes the cost function of the proposed logical rule. Election Algorithm has become the most popular optimization metaheuristic technique for resolving constraint optimization problems. The fundamental concepts of Election Algorithm are taken from socio-political phenomena which use new and efficient processes to produce the best outcome. The behavior of Random Maximum 2 Satisfiability in Discrete Hopfield Neural Network is investigated based on several performance metrics. The performance is compared between existing conventional methods with Genetic Algorithm and Election Algorithm. The results demonstrate that the proposed Random Maximum 2 Satisfiability can become the symbolic instruction in Discrete Hopfield Neural Network where Election Algorithm has performed as an effective training process of Discrete Hopfield Neural Network compared to Genetic Algorithm and Exhaustive Search.

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J-type random 2,3 satisfiability: a higher-order logical rule in discrete hopfield neural network

Jiang,

Kasihmuddin,

Guo

et al. 2024

Evol. Intel.

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YRAN2SAT: A novel flexible random satisfiability logical rule in discrete hopfield neural network

Cited by 35 publications

References 28 publications

Modified 2 Satisfiability Reverse Analysis Method via Logical Permutation燨perator

Modified 2 Satisfiability Reverse Analysis Method via Logical Permutation燨perator

Random Maximum 2 Satisfiability Logic in Discrete Hopfield Neural Network Incorporating Improved Election Algorithm

J-type random 2,3 satisfiability: a higher-order logical rule in discrete hopfield neural network

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