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。
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。
An improvement for dynamic fuzzy neural network (DFNN) was presented to avoid its running into the local extreme.
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。
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.
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.
提出了一种新的动态模糊自组织神经网络模型(TGFCM)，并将其用于文本聚类中。
This paper proposed a new model of dynamic fuzzy Kohonen neural network (TGFCM), which was applied to the text clustering.
针对仿射非线性系统，提出了一种新型的基于动态递归模糊神经网络(DRFNN)的间接自适应控制器。
A novel indirect adaptive controller based on dynamic recurrent fuzzy neural network (DRFNN) is proposed for affine nonlinear system.
提出了一种新的基于移动检测技术、神经网络和模糊判断方法的城市路网动态交通拥挤预测模型。
A model for urban road network traffic congestion forecast based on probe vehicle technology, fuzzy logic judgement and back-propagation (BP) neural network was proposed.
仿真实验结果表明，具有自适应神经网络的模糊推理系统控制的异步电机矢量控制系统不仅动态和稳态性能都得到提高，而且具有较强的鲁棒性。
Simulation results show that the induction motor vector control system with adaptive neuro-fuzzy inference system can improve the static and dynamic performance of the motor and has good robust.
仿真实验结果表明，具有自适应神经网络的模糊推理系统控制的异步电机矢量控制系统不仅动态和稳态性能都得到提高，而且具有较强的鲁棒性。
Simulation results show that the induction motor vector control system with adaptive neuro-fuzzy inference system can improve the static and dynamic performance of the motor and has good robust.
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