中文说明：建模和预测的软件可靠性和网络可靠性 本论文研究尝试探索一方面，在软件中，另一方面，模型的累积失败的期限中的软件可靠性预测模型的 网络可靠性评估或任何其他系统，可以作为一个网络建模 (定向或不）。 人工神经网络和自回归方法已用于预测 累积软件故障。这些方法的培训以进化和模拟退火算法。发达国家的办法是，有资格非参数化模型。数值结果表明，适合善良和下一步一步预见性的我们 建议的各种办法有更准确地预测软件累积失败相比其他的办法。 评价网络的可靠性，我们建议两个 e 3 熟练掌握算法。第一次 一个用于枚举最小 pathsets 在向的网络和第二个用于枚举非定向网络中的极小割。这些算法都加上 容斥原理计算网络的可靠性。这两种算法 使用网络建议在文学和有趣的结果在给一组测试 职权范围准确性和执行时间。
English Description：
Modeling and Prediction of Software Reliability and Network Reliability This dissertation research attempts to explore on the one hand, models for software reliability prediction in term of cumulative failure in the software, on the other hand, models for networks reliability evaluation or any other system which can be modeled as a network (directed or not). Artificial neural networks and the Auto-regression methods have been used to predict the cumulative software failure. These methods are trained by evolutionary and simulated annealing algorithms. The developed approaches are qualified as non-parametric models. Numerical results show that both the goodness-of-fit and the next-step-predictability of our proposed approaches have greater accuracy in predicting software cumulative failure compared to other approaches. For evaluating the reliability of the networks we propose two ecient algorithms. The first one for enumerating min