VLSI Circuit Configuration Using Satisfiability Logic in Hopfield Network

Mansor, Mohd. Asyraf; Kasihmuddin, Mohd Shareduwan Mohd; Sathasivam, Saratha

doi:10.5815/ijisa.2016.09.03

Cited by 14 publications

(8 citation statements)

References 27 publications

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“…The proposed work showed that information embedded in 3SAT yielded a better result for Pattern-SAT. The work by [17] utilized 3SAT integrated with an HNN to configure a Very Large-Scale Integrated (VLSI) circuit. The proposed hybrid network achieved more that 90% accuracy in terms of circuit verification.…”

Section: Introductionmentioning

confidence: 99%

Election Algorithm for Random k Satisfiability in the Hopfield Neural Network

et al. 2020

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Election Algorithm (EA) is a novel variant of the socio-political metaheuristic algorithm, inspired by the presidential election model conducted globally. In this research, we will investigate the effect of Bipolar EA in enhancing the learning processes of a Hopfield Neural Network (HNN) to generate global solutions for Random k Satisfiability (RANkSAT) logical representation. Specifically, this paper utilizes a bipolar EA incorporated with the HNN in optimizing RANkSAT representation. The main goal of the learning processes in our study is to ensure the cost function of RANkSAT converges to zero, indicating the logic function is satisfied. The effective learning phase will affect the final states of RANkSAT and determine whether the final energy is a global minimum or local minimum. The comparison will be made by adopting the same network and logical rule with the conventional learning algorithm, namely, exhaustive search (ES) and genetic algorithm (GA), respectively. Performance evaluation analysis is conducted on our proposed hybrid model and the existing models based on the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Sum of Squared Error (SSE), and Mean Absolute Error (MAPE). The result demonstrates the capability of EA in terms of accuracy and effectiveness as the learning algorithm in HNN for RANkSAT with a different number of neurons compared to ES and GA.

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Section: Introductionmentioning

confidence: 99%

Election Algorithm for Random k Satisfiability in the Hopfield Neural Network

et al. 2020

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“…For this reason, exhaustive searches are only practical for a small number of variables. For this reason, exhaustive searches in a minimal search area are only practicable for a small number of variables [12,18,28,54].…”

Section: Stage 4: Clause Evaluationmentioning

confidence: 99%

“…The cost of each country is ascertained by calculating its objectives function 12 ( , ,..., ) Np f l l l .…”

Section: Imperialist Competitive Algorithm (Ica)mentioning

confidence: 99%

“…An examination of the fundamental insights into how the ICA evolved and how it extended to industrial engineering disciplines in [11]. In [12], a new method has been described for determining the learning rate of ANN which named as cyclical learning rates, which virtually eliminate the need to determine the best values and schedule for optimal learning rates experimentally. One of the major breakthroughs for AI and computational sciences is the neuro-symbolic computation that combines the benefit metaheuristics, Hopfield network and logic programming in finding an optimal solution of various optimization problems [4,[12][13][14].…”

Section: Introductionmentioning

confidence: 99%

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Modified Election Algorithm in Accelerating the Performance of Hopfield Neural Network for Random kSatisfiability

Abubakar¹,

Sathasivam²,

Mansor³

et al. 2020

Preprint

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Election Algorithm (EA) is a powerful metaheuristics model motivated by phenomena of the socio-political mechanism of the presidential election conducted in many countries. EA is selected as a topic of discussion due to its capability and robustness to carry out complex problems in the random-2SAT logic program. This paper utilizes a hybridized EA assimilated with the Hopfield neural network (HNN) in carrying out random logic program (HNN-R2SATEA). The efficiency of the proposed method was compared with the existing traditional exhaustive search (HNN-R2SATES) model and the recently introduced HNN-R2SATICA model. From the result obtained, clearly proven that based on our proposed hybrid model outperformed other existing model based on the Global Minima Ratio (ZM), Mean Absolute Error (MAE), Bayesian Information Criterion (BIC) and Execution Time (ET). The expected outcome portrays that the EA algorithm outperformed the other two algorithms in doing random-kSAT logic program. The results proved the robustness, effectiveness, and compatibility of the HNN-R2SATEA model.

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“…The disadvantages of the proposed HNN model are the storage capacity [13], high computation time [14] and easily trapped in local minima [15]. Since then, the usage of logic programming in ANN has been integrated into several disciplines such as Pattern Reconstruction [16], Agent Based Modelling [17] and Circuit modelling [18]. The rapid development of logic programming in ANN was extended to accommodate real-life datasets.…”

Section: Introductionmentioning

confidence: 99%

Systematic Boolean Satisfiability Programming in Radial Basis Function Neural Network

et al. 2020

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Radial Basis Function Neural Network (RBFNN) is a class of Artificial Neural Network (ANN) that contains hidden layer processing units (neurons) with nonlinear, radially symmetric activation functions. Consequently, RBFNN has extensively suffered from significant computational error and difficulties in approximating the optimal hidden neuron, especially when dealing with Boolean Satisfiability logical rule. In this paper, we present a comprehensive investigation of the potential effect of systematic Satisfiability programming as a logical rule, namely 2 Satisfiability (2SAT) to optimize the output weights and parameters in RBFNN. The 2SAT logical rule has extensively applied in various disciplines, ranging from industrial automation to the complex management system. The core impetus of this study is to investigate the effectiveness of 2SAT logical rule in reducing the computational burden for RBFNN by obtaining the parameters in RBFNN. The comparison is made between RBFNN and the existing method, based on the Hopfield Neural Network (HNN) in searching for the optimal neuron state by utilizing different numbers of neurons. The comparison was made with the HNN as a benchmark to validate the final output of our proposed RBFNN with 2SAT logical rule. Note that the final output in HNN is represented in terms of the quality of the final states produced at the end of the simulation. The simulation dynamic was carried out by using the simulated data, randomly generated by the program. In terms of 2SAT logical rule, simulation revealed that RBFNN has two advantages over HNN model: RBFNN can obtain the correct final neuron state with the lowest error and does not require any approximation for the number of hidden layers. Furthermore, this study provides a new paradigm in the field feed-forward neural network by implementing a more systematic propositional logic rule.

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VLSI Circuit Configuration Using Satisfiability Logic in Hopfield Network

Cited by 14 publications

References 27 publications

Election Algorithm for Random k Satisfiability in the Hopfield Neural Network

Election Algorithm for Random k Satisfiability in the Hopfield Neural Network

Modified Election Algorithm in Accelerating the Performance of Hopfield Neural Network for Random kSatisfiability

Systematic Boolean Satisfiability Programming in Radial Basis Function Neural Network

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