Neural Network and Local Search to Solve Binary CSP
Continuous Hopfield neural Network (CHN) is one of the effective approaches to solve Constrain Satisfaction Problems (CSPs). However, the main problem with CHN is that it can reach stabilisation with outputs in real values, which means an inconsistent solution or an incomplete assignment of CSP vari...
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| Main Authors: | , , , |
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| Format: | EJournal Article |
| Published: |
Institute of Advanced Engineering and Science,
2018-06-01.
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| Subjects: | |
| Online Access: | Get fulltext Get fulltext Get fulltext Get fulltext Get fulltext |
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| Summary: | Continuous Hopfield neural Network (CHN) is one of the effective approaches to solve Constrain Satisfaction Problems (CSPs). However, the main problem with CHN is that it can reach stabilisation with outputs in real values, which means an inconsistent solution or an incomplete assignment of CSP variables. In this paper, we propose a new hybrid approach combining CHN and min-conflict heuristic to mitigate these problems. The obtained results show an improvement in terms of solution quality, either our approach achieves feasible soluion with a high rate of convergence, furthermore, this approach can also enhance theperformance more than conventional CHN in some cases, particularly, when the network crashes. |
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| Item Description: | https://ijeecs.iaescore.com/index.php/IJEECS/article/view/10689 |