Original ContributionSolving inequality constrained combinatorial optimization problems by the hopfield neural networks
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2001, International Journal of Electrical Power and Energy SystemsCitation Excerpt :For the mapping of ED problems, we combine here the improved Aiyer's mapping technique that has been described for quadratic 0–1 programming problems with linear equality and inequality constraints [13,14], with Abe's formulation [16] for inequality constraints.
Using Hopfield neural networks for operational sequencing for prismatic parts on NC machines
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2000, NeurocomputingCitation Excerpt :Specifically, two fundamental issues were observed: an assessment that the suggested procedure establishes the complete solution set as stable points and convergence characteristics of the DHN given that all solutions are stable points. A set of constraint satisfaction and optimization problems including the traveling salesman problem (TSP), the graph path search problem (GPSP), the assignment problem (AP), the N-queens problem (NQP), and the weighted matching problem (WMP) were used in the study [2,14,22,23]. Initial test case involved the determination of the set ordering relationship between the solution set and the stable point set for the optimization problems considered.
Extended Hopfield models for combinatorial optimization
1999, IEEE Transactions on Neural Networks