| Autor: |
Liew Ching Kho, Mohd Kasihmuddin, Mohd Shareduwan, Asyraf Mansor, Mohd., Sathasivam, Saratha |
| Předmět: |
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| Zdroj: |
AIP Conference Proceedings; 2019, Vol. 2184 Issue 1, p060009-1-060009-9, 9p, 1 Diagram, 1 Chart, 3 Graphs |
| Abstrakt: |
This finding presents the limitation of traditional Hopfield Neural Network (HNN) in doing 2 Satisfiability problem (2SAT). More precisely, both traditional exhaustive search method (ES) and ant colony optimization (ACO) were proposed in doing 2SAT problem. Both learning method will reduce logical inconsistencies of 2SAT in HNN. Since both learning method will eventually complete the learning phase, the efficiency of both method is difficult to describe. In this study, both learning method will undergo restricted learning environment during learning phase of HNN. The robustness of ACO and ES in doing 2SAT will be evaluated based on root mean square error (RMSE), mean absolute error (MAE) and mean percentage error (MAPE). The results obtained from the computer simulation demonstrates the effectiveness of ACO in doing 2SAT in HNN. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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