Augmented Hopfield network for mixed-integer programming

Watta and Hassoun (1996) proposed a coupled gradient neural network for mixed integer programming. In this network continuous neurons were used to represent discrete variables. For the larger temporal problem they attempted many of the solutions found were infeasible. This paper proposes an augmente...

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Veröffentlicht in:IEEE transactions on neural networks 1999-03, Vol.10 (2), p.456-458
Hauptverfasser: Walsh, M.P., Flynn, M.E., O'Malley, M.J.
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O'Malley, M.J.
description Watta and Hassoun (1996) proposed a coupled gradient neural network for mixed integer programming. In this network continuous neurons were used to represent discrete variables. For the larger temporal problem they attempted many of the solutions found were infeasible. This paper proposes an augmented Hopfield network which is similar to the coupled gradient network proposed by Watta and Hassoun. However, in this network truly discrete neurons are used. It is shown that this network can be applied to mixed integer programming. Results illustrate that feasible solutions are now obtained for the larger temporal problem.
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subjects Applied sciences
Electric, optical and optoelectronic circuits
Electronics
Exact sciences and technology
Linear programming
Mixed integer
Networks
Neural networks
Neurons
Programming
Temporal logic
Transfer functions
title Augmented Hopfield network for mixed-integer programming
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