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Optimization Using Neural Networks
December 1991 (vol. 40 no. 12)
pp. 1347-1358

The design of feedback (or recurrent) neural networks to produce good solutions to complex optimization problems is discussed. The theoretical basis for applying neural networks to optimization problems is reviewed, and a design rule that serves as a primitive for constructing a wide class of constraints is introduced. The use of the design rule is illustrated by developing a neural network for producing high-quality solutions to a probabilistic resource allocation task. The resulting neural network has been simulated on a high-performance parallel processor that has been optimized for neural network simulation.

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Index Terms:
optimisation; neural networks; feedback; design rule; probabilistic resource allocation task; high-performance parallel processor; simulation; feedback; neural nets; optimisation; simulation.
Citation:
G.A. Tagliarini, J.F. Christ, E.W. Page, "Optimization Using Neural Networks," IEEE Transactions on Computers, vol. 40, no. 12, pp. 1347-1358, Dec. 1991, doi:10.1109/12.106220
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