基于优化神经网络的采集终端软件可靠性预测

采集终端软件的可靠性是评价软件系统生命周期的一个重要指标。针对多种神经网络和支持向量机等方法在软件系统可靠性评价中存在的参数优化困难、软件系统预测模型的低准确率问题，提出基于SAGFA-BPNN的建模方法。该方法采用PCA对实验数据降维处理，剔除影响模型准确率的冗余和干扰样本；在优化SA和GA的基础上，给出退火遗传融合优化算法(SAGFA),并发挥其全局寻优能力，以及BPNN非线性映射能力，提出SAGFA-BPNN网络，及基于它的建模方法，以提高训练速度、全局寻优能力及准确度。文章还应用该方法对采集终端软件的可靠性进行了预测，预测结果表明，该方法可以有效地提高模型的准确度。

Prediction of Reliability of Acquisition Terminal Software Based on Optimization Neural Network

The reliability of acquisition terminal software is an essential criterion for the life span of software system.In order to solve problems such as: high difficulty in parameter optimization during software system reliability evaluation using multi neural network (NN) method and SVP method and low accuracy of prediction using predictive model for software system, method of building model based on Simulated Annealing Genetic Fusion Algorithm (SAGFA) BP NN is raised in this article.Firstly, PCA is used to reduce the dimension of the experimental data, this is to eliminate the redundancy and interference sample that will affect the accuracy of model.Then, SAGFA is developed based on the optimization of SA and GA method. Furthermore, by utilizing the global optimization property of SAGFA method and the non-linear mapping property of BP NN, we are able to produce SAGFA-BPNN and the method of building this model. This will raise the training speed, improve the ability of global optimization and increase the accuracy of prediction. This method is then applied to predict the reliability of acquisition terminal software and the prediction result shows that the method can effectively raise the accuracy of model.