中文摘要：

为正确预测WebService的服务质量（Quality of Service，QoS），帮助用户选择符合服务质量需求的Web Service，提出一种基于径向基神经网络模型的服务质量组合预测方法。首先使用时间序列模型对数据集建立线性和非线性预测模型，并选择最优模型，同时根据数据特点建立不同滑动窗口的灰色等维新息模型，再将上述2模型的预测结果作为输入源传递给径向基神经网络的训练模型，进行预测。实验结果表明，该方法与已有方法相比较，在预测精度方面有一定程度的提高。

英文摘要：

A combination forecasting approach for Quality of Service based on Radial Basis Function neural network （RBF） is proposed, which uses time series model to establish linear and nonlinear forecasting models, and chooses the optimal model, then establishes different size sliding window dimension gray filling forecasting model according to the data characteristics. The forecasting results of these two models are passed into the RBF training model as the input source, and then begin to forecast. The experimental results show that our approach is better than existing models and improves the accuracy of prediction.