Abstract
The main task of system reliability design is to find the best layout of components to maximize reliability or to minimize cost. A reliability optimization approach using neural networks to identify the choice of components in series-parallel systems with multiple constraints is presented in this paper. The McCulloch-Pittes neural network model is used in this approach. The design methods of the neural network construction and its energy function are described in detail. The optimal solutions of the reliability problem are obtained by minimizing the energy function of the neural networks. Simulation results show the reliability optimization approach using neural networks can find the optimal or near-optimal solutions for most of the problems in a relatively short time, it is a useful alternative for system reliability design of complex systems.
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