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。
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。
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。
The proposed DFNN controller was applied on tracking control system of 6-dof parallel platform, and the results show that this method has better tracking performance and robustness.
利用动态模糊神经网络控制器对并联平台的轨迹跟踪控制进行了仿真，结果表明此控制算法具有较好的跟踪性能和较强的鲁棒性。
Aiming at the low control accuracy of 6-dof parallel platform, a dynamical fuzzy neural network (DFNN) was proposed to control the parallel platform which had advantages of artificial neural networks.
针对六自由度并联平台运动控制精度不高的缺点，结合人工神经网络的优点，提出了一种动态模糊神经网络(DFNN)控制器来控制并联平台。
Aiming at the low control accuracy of 6-dof parallel platform, a dynamical fuzzy neural network (DFNN) was proposed to control the parallel platform which had advantages of artificial neural networks.
针对六自由度并联平台运动控制精度不高的缺点，结合人工神经网络的优点，提出了一种动态模糊神经网络(DFNN)控制器来控制并联平台。
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