提出了一种基于对角递归神经网络的盲均衡算法。
A new blind equalization algorithm based on diagonal recurrent neural networks (DRNN) is proposed.
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.
这为快速训练复值递归神经网络提供了一条新的途径。
This provides a new way to the fast training of complex valued recurrent neural network.
研究了带时变时滞的递归神经网络的全局渐近稳定性。
The problem of the globally asymptotical stability of recurrent neural networks with time varying delay is investigated.
基于人工神经网络提出了一种局部递归神经网络控制器。
A locally recurrent neural network controller based on neural network is proposed.
应用实例验证了所提出的递归神经网络预测模型的有效性。
The presented prediction approach is proved to be useful and effective with simulation resu...
研究了一类随机变时滞递归神经网络的几乎指数稳定性问题。
The almost surely exponential stability of stochastic recurrent neural networks with time-varying delays is investigated.
提出一种准对角递归神经网络(QDRNN)结构及学习算法。
A structure and training algorithm for quasi-diagonal recurrent neural network (QDRNN) is presented.
这是我写的关于对角递归神经网络的程序，或许对你有所帮助。
This is written about me on the diagonal recurrent neural network procedures, may be helpful to you.
实验结果表明，递归神经网络用于飞机目标识别是有效可行的。
The result demonstrates the feasibility of using recurrent neural networks for aircraft target recognition.
本文用递归神经网络逼近非线性ARMA模型预测电力短期负荷。
The recursive neural network based nonlinear approaching ARMA model is adopted for short-term power load prediction in this paper.
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.
一种新的基于数字滤波器理论的全互连复值递归神经网络训练方法被提出。
A new training approach for the training algorithm of a fully connected recurrent neural network based on the digital filter theory is proposed.
无限区间上s -分布时滞广义递归神经网络模型概周期解的全局渐近稳定性。
Global asymptotic stability of general recurrent neural network models with S-type distributed delays on infinite intervals.
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.
基于递归神经网络给出了仅含一个非线性环节的一类非线性系统的自适应控制方案。
A scheme of adaptive control based on recurrent neural network is presented for a class of nonlinear systems only with a nonlinear part.
本文利用递归神经网络来建立异步电机转速辩识模型，其网络学习采用实时递归学习算法。
This paper presents a model for identifying induction motor speed using the recurrent neural network, which is trained by a real time recurrent learning algorithm.
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.
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.
该文提出一种基于对角递归神经网络的内模控制系统，并以跳汰生产过程床层松散状况为对象进行了研究。
This paper proposes an internal model control system based on recurrent neural network, and considers jigger layer loose condition as research object.
针对可控受限多变量耦合系统，提出了一种基于对角递归神经网络(DRNN)整定的PID混合解耦控制。
According to the limited controllability of the multivariable coupling system, a PID self-tuning mixed decoupling control method based on DRNN is put forward.
ESN(回声状态网络)是一种新型的递归神经网络，可有效处理非线性系统辨识以及混沌时间序列预测问题。
As a new type of recurrent neural network, echo state network (ESN) is applied to nonlinear system identification and chaotic time series prediction.
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.
为使问题的解能实时、可靠地完成，将一种用于最短路径计算的双层递归神经网络应用于路由选择的流量导数法中。
To make the solution be implemented reliably in real time, a neural network for shortest path computation that is a two-layer recurrent structure is applied to flow deviation method.
实验结果表明，基于对角递归神经网络整定的PID控制的交流伺服系统具有响应速度快、稳态精度高和鲁棒性强等特点。
The results of experiments show that AC servo system based on DRNN PID control has quick response speed, high steady accuracy and good robustness.
实验结果表明，基于对角递归神经网络整定的PID控制的交流伺服系统具有响应速度快、稳态精度高和鲁棒性强等特点。
The results of experiments show that AC servo system based on DRNN PID control has quick response speed, high steady accuracy and good robustness.
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