Multi terminal net routing for island style FPGAs using nearly-2-SAT computation

Mukherjee, Shyamapada; Roy, Suchismita

doi:10.1109/isvdat.2015.7208142

Cited by 5 publications

(2 citation statements)

References 13 publications

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“…2SAT is the main impetus since the focus of the logic programming is to ensure the programme only considers 2 literals per clause per execution. It was substantiated by various research [18][19][20] that numerous combinatorial problems can be formulated by using the 2SAT paradigm. The choice 461 of 2 literals per clause in satisfiability problem reduces the logical complexity in discovering the relation between variables in the neural network or solver.…”

Section: Research Methods 21 2 Satisfiability (2sat) Logicmentioning

confidence: 99%

Radial basis function neural network for 2 satisfiability programming

Alzaeemi

Mansor

Kasihmuddin

et al. 2020

IJEECS

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<span>Radial Basis Function Neural Network (RBFNN) is very prominent in data processing. However, improving this technique is vital for the NN training process. This paper presents an integrated 2 Satisfiability in radial basis function neural network (RBFNN-2SAT). There are two different types of training in RBFNN, namely no-training technique and half-training technique. The performance of the solutions via Genetic Algorithm (GA) training was investigated by comparing the Radial Basis Function Neural Network No-Training Technique (RBFNN- 2SATNT) and Radial Basis Function Neural Network Half-Training Technique (RBFNN- 2SATHT). The comparison of both techniques was examined on 2 Satisfiability problem by using a C# software that was developed for this experiment. The performance of the RBFNN-2SATNT and RBFNN-2SATHT in performing 2SAT is discussed in terms of root mean squared error (RMSE), sum squared error (SSE), mean absolute percentage error (MAPE), mean absolute error (MAE), number of the hidden neurons and CPU time. Results obtained from a computer simulation showed that RBFNN-2SATHT outperformed RBFNN-2SATNT.</span>

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Section: Research Methods 21 2 Satisfiability (2sat) Logicmentioning

confidence: 99%

Radial basis function neural network for 2 satisfiability programming

Alzaeemi

Mansor

Kasihmuddin

et al. 2020

IJEECS

View full text Add to dashboard Cite

show abstract

“…In this study, 2SAT is considered as the main driver because the focus of logical programming involves ensuring that the program considers only two letters per item per implementation. It has been proven in previous studies that many of the combinatorial problems can be formulated using the 2SAT logic [56][57][58]. A key reason that makes the 2SAT logic an appropriate approach of representing logical rules in the neural network involves choosing two literals per clause in satisfiability logic, which can reduce the logic complexity of disclosing the relation between the variables in the neural network.…”

Section: Of 28mentioning

confidence: 99%

Artificial Immune System in Doing 2-Satisfiability Based Reverse Analysis Method via a Radial Basis Function Neural Network

Alzaeemi

Sathasivam

2020

Processes

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A radial basis function neural network-based 2-satisfiability reverse analysis (RBFNN-2SATRA) primarily depends on adequately obtaining the linear optimal output weights, alongside the lowest iteration error. This study aims to investigate the effectiveness, as well as the capability of the artificial immune system (AIS) algorithm in RBFNN-2SATRA. Moreover, it aims to improve the output linearity to obtain the optimal output weights. In this paper, the artificial immune system (AIS) algorithm will be introduced and implemented to enhance the effectiveness of the connection weights throughout the RBFNN-2SATRA training. To prove that the introduced method functions efficiently, five well-established datasets were solved. Moreover, the use of AIS for the RBFNN-2SATRA training is compared with the genetic algorithm (GA), differential evolution (DE), particle swarm optimization (PSO), and artificial bee colony (ABC) algorithms. In terms of measurements and accuracy, the simulation results showed that the proposed method outperformed in the terms of Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), Schwarz Bayesian Criterion (SBC), and Central Process Unit time (CPU time). The introduced method outperformed the existing four algorithms in the aspect of robustness, accuracy, and sensitivity throughout the simulation process. Therefore, it has been proven that the proposed AIS algorithm effectively conformed to the RBFNN-2SATRA in relation to (or in terms of) the average value of training of RMSE rose up to 97.5%, SBC rose up to 99.9%, and CPU time by 99.8%. Moreover, the average value of testing in MAE was rose up to 78.5%, MAPE—rose up to 71.4%, and was capable of classifying a higher percentage (81.6%) of the test samples compared with the results for the GA, DE, PSO, and ABC algorithms.

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Packing and Legalization Free Boolean Satisfiability-based Placement Algorithm for Heterogeneous FPGAs

Mukherjee

Purkayastha

2021

Arab J Sci Eng

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Multi terminal net routing for island style FPGAs using nearly-2-SAT computation

Cited by 5 publications

References 13 publications

Radial basis function neural network for 2 satisfiability programming

Radial basis function neural network for 2 satisfiability programming

Artificial Immune System in Doing 2-Satisfiability Based Reverse Analysis Method via a Radial Basis Function Neural Network

Packing and Legalization Free Boolean Satisfiability-based Placement Algorithm for Heterogeneous FPGAs

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