The combination of the Monte-Carlo and fault tree analysis is one of the most effective way of analyzing and predicting the reliability of complex large-scale system.
蒙特卡罗方法和故障树分析相结合是当前对大规模复杂系统进行可靠性分析预测的最有效途径之一。
The software reliability growth models (SRGMs) are the main methods for evaluating and predicting software reliability.
软件可靠性增长模型是评估和预测软件可靠性的主要方法。
In this paper bench tight blasting effects are predicted by genetic neural network, then the reliability of predicting effects is enhanced.
应用遗传神经网络模型对台阶压碴爆破效果进行了预测，增强了预测结果的可靠性。
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