An improvement for dynamic fuzzy neural network (DFNN) was presented to avoid its running into the local extreme.

针对动态模糊神经网络(DFNN)在进行预测应用时容易陷入“局部极值”的缺陷，提出一种改进方案。

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The structure of DFNN and a parameter regulating method which is based on the shrinking span membership functions and BP algorithm are proposed.

给出了DFNN的网络结构，为基于收缩间距隶属函数和BP算法提供了参数调整方法。

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Since a static fuzzy neural network cannot deal with the temporal problem, a dynamic fuzzy neural network (DFNN) with recurrent units is proposed.

针对静态网络无法处理暂态问题，对具有递归环节的动态模糊神经网络进行了研究。

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