Wavelet analysis, as a time frequency analysis tool, has been widely used in many fields of electric power system. To select and construct the best wavelet for user is of important significance.
小波分析作为时频分析工具已经越来越广泛地应用于电力系统的各个领域，如何选择和构造最适合用户需要的小波具有重要意义。
Aiming the square sum of error (SSE), we construct the algorithm to iterate and select an optimal parameter for optimizing the new models, which ADAPTS the model to time series more.
又以预测误差平方和SSE最小为目标，构造了优选并自动生成最佳平滑参数使平滑模型得以优化的最速下降算法，增强了指数平滑模型对时间序列的适应能力。
On the basis of separating the literal description and the data description, NN is applied to select the model type and es is applied to construct model structure.
在对文字描述和数据描述分离的基础上，应用专家系统实现模型类型的选择，应用神经网络实现模型结构的构造。
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