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1·Then completed reliability prediction by using FTA.

应用故障树完成系统的可靠性预计工作。

2·The function and implication of stress analysis for reliability prediction is introduced.

阐述了应力分析可靠性预计的作用与意义。

3·Software reliability prediction model is the base to evaluate and predict software reliability.

软件可靠性预测模型是定量评估和预测软件可靠性的基础。

4·The result have proved that the method can improve the accuracy of fatigue reliability prediction.

理论分析及算例验证表明，该法能提高可靠性预测精度。

5·Based on the fuzzy neural network, an approach to early software reliability prediction is proposed.

在基于模糊神经网络的基础上，提出了一种新的软件可靠性早期预计方法。

6·Finally, the integrating with FMECA reliability prediction method is compared with the conventional one.

还对结合FMECA可靠性预计法和常规可靠性预计法进行了比较。

7·The results of experiments indicate: the ensemble neural network can improve greatly the accuracy of software reliability prediction.

实验结果表明，这种组合神经网络能够显著提高软件可靠性的预测精度。

8·Basic way of thinking on robot system reliability study is put forward. Reliability prediction theory of robot system is established.

阐述了工业机器人系统可靠性研究的意义和基本思路，提出了工业机器人可靠性预测的模型和方法。

9·The main factors affecting software reliability must be considered firstly before early software reliability prediction model is studied.

对软件可靠性早期预测模型的研究，首先必须考虑影响软件可靠性的主要因素。

10·Based on JM model, we propose a software reliability prediction model involving fault-remove time which followed exponential distribution.

因此在JM模型的基础上，提出了排错时间为负指数分布的软件可靠性模型及本模型的极大似然参数估计方法。