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Integrated Markov-neural reliability computation method: A case for multiple automated guided vehicle system

机译：马尔可夫神经系统综合可靠性计算方法：多导引导车系统案例

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摘要

This paper proposes an integrated Markovian and back propagation neural network approaches to compute reliability of a system. While states of failure occurrences are significant elements for accurate reliability computation, Markovian based reliability assessment method is designed. Due to drawbacks shown by Markovian model for steady state reliability computations and neural network for initial training pattern, integration being called Markov-neural is developed and evaluated. To show efficiency of the proposed approach comparative analyses are performed. Also, for managerial implication purpose an application case for multiple automated guided vehicles (AGVs) in manufacturing networks is conducted.
机译：本文提出了一种综合的马尔可夫和反向传播神经网络方法来计算系统的可靠性。虽然故障发生的状态是准确进行可靠性计算的重要因素，但还是设计了基于马尔可夫的可靠性评估方法。由于马尔可夫模型在稳态可靠性计算和神经网络初始训练模式方面存在缺陷，因此开发并评估了称为马尔可夫神经网络的集成方法。为了显示所提出方法的效率，进行了比较分析。此外，出于管理方面的目的，针对制造网络中的多个自动导引车（AGV）进行了应用案例研究。

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