Enhanced Hopfield Network for Pattern Satisfiability Optimization
Автор: Mohd. Asyraf Mansor, Mohd Shareduwan M. Kasihmuddin, Saratha Sathasivam
Журнал: International Journal of Intelligent Systems and Applications(IJISA) @ijisa
Статья в выпуске: 11 vol.8, 2016 года.
Бесплатный доступ
Highly-interconnected Hopfield network with Content Addressable Memory (CAM) are shown to be extremely effective in constraint optimization problem. The emergent of the Hopfield network has producing a prolific amount of research. Recently, 3 Satisfiability (3-SAT) has becoming a tool to represent a variety combinatorial problems. Incorporated with 3-SAT, Hopfield neural network (HNN-3SAT) can be used to optimize pattern satisfiability (Pattern-SAT) problem. Hence, we proposed the HNN-3SAT with Hyperbolic Tangent activation function and the conventional McCulloch-Pitts function. The aim of this study is to investigate the accuracy of the pattern generated by our proposed algorithms. Microsoft Visual C++ 2013 is used as a platform for training, testing and validating our Pattern-SAT design. The detailed performance of HNN-3SAT of our proposed algorithms in doing Pattern-SAT will be discussed based on global pattern-SAT and running time. The result obtained from the simulation demonstrate the effectiveness of HNN-3SAT in doing Pattern-SAT.
Pattern-SAT, Hopfield Network, 3-Satisfiability, Hyperbolic Tangent Activation Function, McCulloch-Pitts Function
Короткий адрес: https://sciup.org/15010873
IDR: 15010873
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