alexa Optimization using neural networks
Engineering

Engineering

Journal of Information Technology & Software Engineering

Author(s): GA Tagliarini, JF Christ, EW Page

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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.

This article was published in IEEE Computer Society and referenced in Journal of Information Technology & Software Engineering

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