在学习过程中通过同时调整小波基函数的平移因子和隶属度函数的形状，使得模糊小波网络的精度和泛化能力大大提高。
By adjusting the translation parameters of the wavelets and the shape of membership functions, the accuracy and generalization capability of FWN can be remarkably improved.
应用所引入的因子模糊化训练法可使训练速度加快。
During training of the network, the fuzzified factor based training technique is used, and the training process is accelerated rapidly.
网络的训练利用改进的BP算法，将因子模糊化快速进行。样本点数据则由利用动态逆控制所得到的结果来提供。
The networks are trained by the fast BP algorithm via fuzzy variables decision, and training samples are provided by the dynamic inversion control results.
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