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Quantized hopfield networks for reliability optimization
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DOI：10.1016/S0951-8320(03)00097-8.png)
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The use of neural networks in the reliability optimization field is rare. This paper presents an application of a recent kind of neural networks in a reliability optimization problem for a series system with multiple-choice constraints incorporated at each subsystem, to maximize the system reliability subject to the system budget. The problem is formulated as a nonlinear binary integer programming problem and characterized as an NP-hard problem. Our design of neural network to solve efficiently this problem is based on a quantized Hopfield network. This network allows us to obtain optimal design solutions very frequently and much more quickly than others Hoptield networks. (C) 2003 Elsevier Science Ltd. All rights reserved.
Keyword:
neural network design
multiple-choice
reliability optimization
series system
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