分析了动态递归神经网络系统辨识的参数学习算法。
The parameter learning algorithm of dynamic recurrent neural network based on system identification is analyzed. D.
分析了动态递归神经网络系统辨识的参数学习算法。
The parameter learning algorithm of dynamic recurrent neural network based on system identification is analyzed.
对所提出的动态递归神经网络进行了分析，以及如何利用它们来进行系统辨识。
The dynamic recurrent neural network is analyzed, and how to use it for system identification is also analyzed.
与改进BP算法相结合，各取所长，形成集成化动态递归神经网络建模辨识算法。
The identification algorithm integrating the forward evolutionary algorithm and improved BP algorithm for the dynamic recursive neural network model is formed.
接着，结合其存在的问题，对动态递归神经网络、R BF神经网络和自适应逆控制进行了算法研究。
Then, aiming at the existing problem, the algorithm of dynamic recurrent neural network, RBF neural network and adaptive inverse control is studied in the paper.
提出一种基于动态递归神经网络的自适应pid控制方案，该控制系统由神经网络辨识器和神经网络控制器组成。
This paper presents an adaptive PID control scheme based on dynamic recurrent neural network. The control system is consisted of the neural network identifier and the neural network controller.
提出一种基于动态递归神经网络的自适应pid控制方案，该控制系统由神经网络辨识器和神经网络控制器组成。
An adaptive PID control scheme based on dynamic recurrent neural network is presented. The control system is consisted of the neural network identifier and the neural network controller.
应用该模型对线性结构和非线性结构在变阻尼控制和外荷载激励下结构的响应进行了数值仿真，表明所提的动态递归神经网络可以达到较高的预测精度。
Simulations on linear and nonlinear structures demonstrate that RDRNN is very effective on predicting the response of a structure subject to semi-active control and external excitation.
为了考察该系统的动态性能，采用递归BP神经网络对该系统进行辨识。
To test the dynamic property, this pneumatic fatigue test system was identified by a recursive BP neural network.
由于其反馈特征，使得递归神经网络模型能获取系统的动态响应。
With the feedback behavior, the recursive neural network can catch up with the dynamic response of the system.
由于其反馈特征，使得递归神经网络模型能获取系统的动态响应特性。
With the feedback behavior, the recurrent neural network can catch up with the dynamic response of the system.
利用对角递归神经网络在线自适应调整PID控制器的参数，从而使系统的静态和动态性能指标较为理想。
DRNN is used to adjust the parameters of PID control on-line, accordingly it can make static and dynamic performance index comparatively ideal.
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.
本文介绍了动态对角递归网络，并针对BP算法收敛慢的缺点，将递推预报误差学习算法应用到神经网络权值和域值的训练。
To overcome the slow convergence of the BP algorithm, recursive prediction error algorithm is proposed, which can train both the weight and the bias.
本文介绍了动态对角递归网络，并针对BP算法收敛慢的缺点，将递推预报误差学习算法应用到神经网络权值和域值的训练。
To overcome the slow convergence of the BP algorithm, recursive prediction error algorithm is proposed, which can train both the weight and the bias.
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