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.
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.
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.
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.
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.
提出一种在用户-网络接口处利用对角递归神经网络(DRNN)作为自适应预测器，实现AT M网络自适应拥塞控制的模型。
This paper presents an adaptive congestion control model in ATM networks at the user to network interface by using a diagonal recurrent neural network (DRNN) as an predictor.
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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