Moreover we in-depth study the issues of super node selection model, and present a new method for super node selection based on CPU dynamically processing power.
并对模型中超级节点选取问题进行了深入研究，提出了基于CPU动态处理能力的超级节点选取方法。
The study shows the selection of input and output models have significant influence on the results of model prediction. To select suitable input and output factors can get better prediction results.
结果表明，模型输入、输出因子的选择对模型预测结果影响很大，选用合适的输入、输出因子，会得到比较好的预测效果。
On the aspect of model identification, we specially study the selection techniques for nonlinear model structure with linear parameters and the corresponding algorithms.
在模型辨识方面，我们专门研究了参数线性的非线性模型结构的选择技术及其算法。
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